Data Science & Analytics Internship — 1 Month
You'll learn core Data Science & Analytics through structured modules and work on a real live client project alongside senior developers. By the end, you can build confidently and have contributed to something that a real client uses.
40+
live training hours
10
core modules
1–2
live client projects
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for basic 3D orientation. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Codebase Basics
🔒 100+ Sessions • Career-Ready
Core Data Science & Analytics modules
To introduce participants to the data analytics lifecycle, business intelligence concepts, and the role of data-driven decision-making in organizations.
Topics covered
Fundamentals of data analytics and its importance in modern businesses.
Types of analytics: descriptive, diagnostic, predictive, and prescriptive.
Data analytics lifecycle: collection, cleaning, transformation, analysis, visualization, and reporting.
Structured, semi-structured, and unstructured data.
Introduction to business intelligence, KPIs, metrics, and business reporting.
Industry applications of data analytics in finance, healthcare, retail, education, and IT.
Overview of the data analytics technology stack and commonly used tools.
What you'll build
- Identify data analytics use cases in different business domains.
- Examine a sample sales dataset and identify relevant business questions.
- Define KPIs for a simple business scenario, such as sales performance or customer retention.
- Map the steps required to transform raw data into a business report.
By the end: Participants will understand the purpose of data analytics, the complete analytics workflow, and how organizations use data to support business decisions.
To develop the Python programming skills required to process data, automate repetitive tasks, and build analytical solutions.
Topics covered
Python installation and development environments: Jupyter Notebook and VS Code.
Variables, data types, operators, and type conversion.
Conditional statements and loops.
Functions, parameters, return values, and reusable code.
Lists, tuples, dictionaries, and sets.
String manipulation and commonly used built-in functions.
File handling for reading and writing CSV, text, and JSON files.
Exception handling and basic debugging.
Introduction to Python modules and packages.
What you'll build
- Write Python programs to calculate sales totals, averages, and percentage growth.
- Read a CSV file and display selected records.
- Create functions to calculate revenue, expenses, and profit.
- Process a list of employee records using dictionaries and lists.
- Build a simple Python program to summarize daily sales from a file.
By the end: Participants will be able to write structured Python programs, process basic datasets, work with files, and automate simple data-related tasks.
To teach participants how to efficiently load, manipulate, transform, and prepare datasets for analysis using Python libraries.
Topics covered
Introduction to NumPy arrays and numerical computations.
Array indexing, slicing, filtering, and mathematical operations.
Introduction to Pandas Series and DataFrames.
Importing datasets from CSV and Excel files.
Inspecting datasets using `head()`, `info()`, `describe()`, and related methods.
Selecting, filtering, sorting, and renaming columns.
Handling missing values and duplicate records.
Grouping and aggregation using `groupby()`.
Combining datasets using merge, join, and concatenation operations.
Creating calculated columns and transforming data.
Exporting processed data into CSV and Excel formats.
What you'll build
- Load a sales dataset and examine its structure and data types.
- Filter transactions based on product, region, date, or revenue.
- Calculate total sales and average order value by category.
- Merge customer and transaction datasets.
- Clean missing values and duplicate records in an employee dataset.
- Generate a summarized dataset for further reporting.
By the end: Participants will be able to use NumPy and Pandas to manipulate datasets, perform common transformations, combine data from multiple sources, and prepare analysis-ready data.
To develop the ability to retrieve, filter, aggregate, and analyse business data stored in relational databases.
Topics covered
Introduction to relational databases, tables, rows, columns, and relationships.
Database concepts: primary keys, foreign keys, and constraints.
SQL commands: DDL, DML, DQL, and transaction-control fundamentals.
Data retrieval using `SELECT`, `WHERE`, `DISTINCT`, and `ORDER BY`.
Filtering records using logical operators, `IN`, `BETWEEN`, and pattern matching.
Aggregate functions: `COUNT()`, `SUM()`, `AVG()`, `MIN()`, and `MAX()`.
Grouping and filtering aggregated results using `GROUP BY` and `HAVING`.
Joins: INNER, LEFT, RIGHT, and FULL OUTER JOIN where supported.
Subqueries and Common Table Expressions (CTEs).
Introduction to window functions such as `ROW_NUMBER()`, `RANK()`, and `SUM() OVER()`.
Connecting Python applications to databases such as MySQL or PostgreSQL.
What you'll build
- Create a database containing customer, product, employee, and sales tables.
- Write SQL queries to identify top-selling products and high-revenue regions.
- Calculate monthly revenue and department-wise employee counts.
- Join customer and order tables to analyse customer purchasing behaviour.
- Use a subquery or CTE to identify customers whose purchases exceed the average.
- Retrieve database records through Python for analysis.
By the end: Participants will be able to query relational databases, combine information from multiple tables, perform business-oriented analysis using SQL, and retrieve data for Python-based workflows.
To train participants to identify data-quality issues and transform raw datasets into reliable, consistent, and analysis-ready information.
Topics covered
Importance of data quality in business analytics.
Identifying missing, duplicated, inconsistent, and invalid records.
Handling missing values using deletion or suitable imputation techniques.
Standardizing text, date, currency, and categorical formats.
Detecting incorrect data types and invalid values.
Identifying outliers and understanding their potential impact.
Data validation rules, range checks, and uniqueness checks.
Data reconciliation and consistency checks across datasets.
Creating reusable data-cleaning functions in Python.
Maintaining a record of data-cleaning decisions and transformations.
What you'll build
- Clean a customer dataset containing missing phone numbers, duplicate entries, and inconsistent text formats.
- Standardize date formats and categorical values.
- Validate employee ages, salaries, transaction amounts, and required fields.
- Compare source and processed datasets to identify unexpected record-count differences.
- Build a reusable Python script that cleans a dataset and produces a data-quality summary.
By the end: Participants will be able to identify common data-quality problems, apply appropriate cleaning techniques, validate the results, and document transformations before analysis.
To develop the ability to explore datasets, identify patterns, apply statistical techniques, and derive meaningful insights from business data.
Topics covered
Purpose and workflow of Exploratory Data Analysis (EDA).
Understanding distributions, data ranges, and variable relationships.
Descriptive statistics: mean, median, mode, variance, and standard deviation.
Percentiles, quartiles, and interquartile range.
Frequency distributions and categorical data analysis.
Correlation and relationships between numerical variables.
Outlier identification using statistical methods and visualizations.
Time-based trends and comparisons across categories.
Introduction to probability, sampling, and hypothesis testing.
Distinguishing correlation from causation.
Translating analytical observations into business findings.
What you'll build
- Perform EDA on a retail sales dataset using Pandas.
- Calculate summary statistics for sales, profit, and order values.
- Identify unusual transactions and investigate potential outliers.
- Analyse relationships between discounts, sales, and profit.
- Compare monthly performance across regions.
- Prepare a short insight report containing findings supported by data.
By the end: Participants will be able to explore datasets systematically, apply fundamental statistical methods, identify trends and unusual patterns, and communicate evidence-based business insights.
To equip participants with the skills required to present analytical results through meaningful visualizations and interactive business dashboards.
Topics covered
Principles of effective data visualization and chart selection.
Creating charts using Matplotlib, Seaborn, and Plotly.
Bar charts, line charts, scatter plots, histograms, box plots, and heatmaps.
Comparing categories and visualizing time-series trends.
Introduction to Microsoft Power BI and its interface.
Importing data from Excel, CSV files, and databases.
Data transformation using Power Query.
Relationships, tables, and basic data modelling.
Creating calculated columns and measures using DAX.
Designing dashboards with KPIs, slicers, filters, and interactive visuals.
Dashboard usability, performance, and presentation best practices.
What you'll build
- Visualize monthly sales, profit, and customer growth using Python.
- Create a correlation heatmap for a business dataset.
- Import sales data into Power BI and transform it using Power Query.
- Build an interactive sales dashboard with region, product, and date filters.
- Create KPI cards for revenue, profit margin, order count, and sales growth.
- Present dashboard findings through a short business-focused explanation.
By the end: Participants will be able to develop clear data visualizations, build interactive Power BI dashboards, and communicate trends and performance indicators effectively.
To develop practical spreadsheet analysis, KPI monitoring, and reporting skills commonly required in corporate analytics assignments.
Topics covered
Excel worksheets, tables, sorting, filtering, and data validation.
Essential formulas and functions for analytical tasks.
Conditional functions such as `IF`, `SUMIF`, `SUMIFS`, `COUNTIF`, and `COUNTIFS`.
Lookup functions such as `XLOOKUP` or `VLOOKUP`, where available.
Text and date functions for data preparation.
PivotTables and PivotCharts for summarizing business data.
Conditional formatting and spreadsheet-based KPI tracking.
Business metrics: revenue, profit, profit margin, growth rate, and customer retention.
Report structuring, summaries, charts, and recommendations.
Communicating insights to technical and non-technical stakeholders.
What you'll build
- Analyse a sales spreadsheet using formulas and PivotTables.
- Create a monthly revenue and profit report.
- Compare performance across products, departments, and regions.
- Build an Excel KPI tracker with conditional formatting.
- Prepare a one-page management report with key findings and recommendations.
- Explain the business implications of the results in simple language.
By the end: Participants will be able to analyse business data in Excel, calculate and monitor KPIs, prepare structured reports, and communicate findings in a format suitable for managers and clients.
To introduce participants to automated data workflows and responsible AI-assisted development practices that improve productivity and reduce repetitive manual work.
Topics covered
Fundamentals of ETL: Extract, Transform, and Load.
Importing data from CSV, Excel, JSON, databases, and APIs.
Introduction to REST APIs and HTTP requests for data retrieval.
Combining data from multiple sources into a unified dataset.
Automating repetitive data-cleaning and reporting tasks with Python.
Generating reports and exporting results programmatically.
Scheduling and monitoring automated scripts at a basic level.
Using AI coding assistants to generate code drafts and SQL queries.
AI-assisted debugging, code explanation, refactoring, test-case generation, and documentation.
Validating AI-generated code and checking outputs against expected results.
Protecting confidential business data and credentials when using AI tools.
Using Git and GitHub for version control and project collaboration.
What you'll build
- Retrieve sample data from an API and convert it into a Pandas DataFrame.
- Combine multiple monthly sales files into one consolidated dataset.
- Create a Python script that cleans data and generates a summary report.
- Use an AI coding assistant to draft a data-cleaning function, then test and correct it.
- Generate SQL queries with AI assistance and verify their results against the dataset.
- Track project changes using Git and maintain documentation in GitHub.
By the end: Participants will be able to build basic automated analytics workflows, integrate data from different sources, use AI tools to accelerate development responsibly, and maintain organized project code.
To enable participants to apply the skills developed throughout the internship by completing a practical analytics project based on a realistic business problem.
Topics covered
Understanding business requirements and defining the problem statement.
Identifying data sources, business questions, and measurable KPIs.
Collecting data from files, databases, or suitable APIs.
Cleaning, transforming, and validating datasets using Python and SQL.
Performing exploratory data analysis and statistical analysis.
Creating visualizations and developing an interactive Power BI dashboard.
Preparing business reports with findings and recommendations.
Testing analytical calculations and validating report accuracy.
Organizing project files, maintaining version control, and documenting the workflow.
Preparing a final presentation and demonstrating the solution to stakeholders.
What you'll build
- Participants will develop an **End-to-End Sales Performance and Business Insights System** that includes:
- Business Requirement Analysis:** Define the business problem and establish KPIs such as revenue, profit, sales growth, and regional performance.
- Data Collection:** Obtain a suitable sales dataset from CSV or Excel files, or another appropriate source.
- Data Preparation:** Clean and validate the data using Python and Pandas.
- Database Analysis:** Store or access structured data and perform business queries using SQL.
- Exploratory Analysis:** Identify sales trends, top-performing products, regional differences, and profit patterns.
- Visualization:** Create charts using Python and develop an interactive Power BI dashboard.
- Reporting:** Prepare a business report summarizing key findings and actionable recommendations.
- Automation:** Build a reusable script to refresh or regenerate analytical outputs from updated input data.
- Quality Checks:** Validate calculations, compare totals, and verify dashboard results against source data.
- Project Presentation:** Demonstrate the workflow, explain the insights, and present the final solution.
By the end: Participants will be able to complete and present an end-to-end data analytics project that demonstrates their ability to collect, prepare, query, analyse, visualize, and report data using industry-relevant tools and workflows.
Live client projects
In the 1-month program you work on one live client project alongside your mentor. These are real projects built for actual client requirements — not practice exercises. You build on the same codebase your seniors are working on.
Live client project
TalentBeacon — Intelligent Workforce Analytics Platform
Headcount analytics, attrition risk scoring, compensation equity analysis, and hiring velocity dashboards.
PythonPandasPower BISQLStreamlit
Live client project
Employee Attendance and Workforce Analytics System
Attendance anomaly detection, shift compliance tracking, absenteeism trend analysis, and automated summary reports.
PythonNumPyPandasMatplotlibPostgreSQL
Everything included in your 1-month enrollment
✓
40+ live training hours with senior developers✓
Limited VR orientation sessions (introduction to 3D codebase visualizer)✓
10 core Data Science & Analytics modules with practical exercises✓
1 live client project on an active production codebase✓
Internship completion certificate from WhiteandBox.com✓
Remote, hybrid, or in-office at Bangalore1-Month Data Science & Analytics Internship
Live sessions · Real project · Certificate and offer letter
₹5,000
₹2,500
50% off
1 month · live daily sessions
Want to go deeper into every topic?
The 2–6 month program covers the same 10 modules in significantly more depth — more exercises, more concepts, and 5 additional advanced modules that the 1-month doesn't reach.
Data Science & Analytics Internship — 2 to 6 Months
The same core Data Science & Analytics curriculum, covered in significantly more depth — more exercises, more edge cases, more patterns. Plus five additional modules that take you into advanced concepts, system architectures, and production delivery. With more time comes stronger ability, a deeper portfolio, and real exposure to how a professional team ships software.
120+
live training hours
15+
modules
2–3
live client projects
Everything in the 1-month program is included here. The same 10 core modules — but every one of them goes deeper: more exercises, more business scenarios, more edge cases. On top of that, you get five additional modules that the 1-month doesn't have time to cover.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for project development lab. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Architectural Walkthroughs
🔒 100+ Sessions • Career-Ready
Foundation modules — all 10, covered in full depth
To introduce participants to the data analytics lifecycle, business intelligence concepts, and the role of data-driven decision-making in organizations.
Topics covered
Fundamentals of data analytics and its importance in modern businesses.
Types of analytics: descriptive, diagnostic, predictive, and prescriptive.
Data analytics lifecycle: collection, cleaning, transformation, analysis, visualization, and reporting.
Structured, semi-structured, and unstructured data.
Introduction to business intelligence, KPIs, metrics, and business reporting.
Industry applications of data analytics in finance, healthcare, retail, education, and IT.
Overview of the data analytics technology stack and commonly used tools.
What you'll build
- Identify data analytics use cases in different business domains.
- Examine a sample sales dataset and identify relevant business questions.
- Define KPIs for a simple business scenario, such as sales performance or customer retention.
- Map the steps required to transform raw data into a business report.
By the end: Participants will understand the purpose of data analytics, the complete analytics workflow, and how organizations use data to support business decisions.
To develop the Python programming skills required to process data, automate repetitive tasks, and build analytical solutions.
Topics covered
Python installation and development environments: Jupyter Notebook and VS Code.
Variables, data types, operators, and type conversion.
Conditional statements and loops.
Functions, parameters, return values, and reusable code.
Lists, tuples, dictionaries, and sets.
String manipulation and commonly used built-in functions.
File handling for reading and writing CSV, text, and JSON files.
Exception handling and basic debugging.
Introduction to Python modules and packages.
What you'll build
- Write Python programs to calculate sales totals, averages, and percentage growth.
- Read a CSV file and display selected records.
- Create functions to calculate revenue, expenses, and profit.
- Process a list of employee records using dictionaries and lists.
- Build a simple Python program to summarize daily sales from a file.
By the end: Participants will be able to write structured Python programs, process basic datasets, work with files, and automate simple data-related tasks.
To teach participants how to efficiently load, manipulate, transform, and prepare datasets for analysis using Python libraries.
Topics covered
Introduction to NumPy arrays and numerical computations.
Array indexing, slicing, filtering, and mathematical operations.
Introduction to Pandas Series and DataFrames.
Importing datasets from CSV and Excel files.
Inspecting datasets using `head()`, `info()`, `describe()`, and related methods.
Selecting, filtering, sorting, and renaming columns.
Handling missing values and duplicate records.
Grouping and aggregation using `groupby()`.
Combining datasets using merge, join, and concatenation operations.
Creating calculated columns and transforming data.
Exporting processed data into CSV and Excel formats.
What you'll build
- Load a sales dataset and examine its structure and data types.
- Filter transactions based on product, region, date, or revenue.
- Calculate total sales and average order value by category.
- Merge customer and transaction datasets.
- Clean missing values and duplicate records in an employee dataset.
- Generate a summarized dataset for further reporting.
By the end: Participants will be able to use NumPy and Pandas to manipulate datasets, perform common transformations, combine data from multiple sources, and prepare analysis-ready data.
To develop the ability to retrieve, filter, aggregate, and analyse business data stored in relational databases.
Topics covered
Introduction to relational databases, tables, rows, columns, and relationships.
Database concepts: primary keys, foreign keys, and constraints.
SQL commands: DDL, DML, DQL, and transaction-control fundamentals.
Data retrieval using `SELECT`, `WHERE`, `DISTINCT`, and `ORDER BY`.
Filtering records using logical operators, `IN`, `BETWEEN`, and pattern matching.
Aggregate functions: `COUNT()`, `SUM()`, `AVG()`, `MIN()`, and `MAX()`.
Grouping and filtering aggregated results using `GROUP BY` and `HAVING`.
Joins: INNER, LEFT, RIGHT, and FULL OUTER JOIN where supported.
Subqueries and Common Table Expressions (CTEs).
Introduction to window functions such as `ROW_NUMBER()`, `RANK()`, and `SUM() OVER()`.
Connecting Python applications to databases such as MySQL or PostgreSQL.
What you'll build
- Create a database containing customer, product, employee, and sales tables.
- Write SQL queries to identify top-selling products and high-revenue regions.
- Calculate monthly revenue and department-wise employee counts.
- Join customer and order tables to analyse customer purchasing behaviour.
- Use a subquery or CTE to identify customers whose purchases exceed the average.
- Retrieve database records through Python for analysis.
By the end: Participants will be able to query relational databases, combine information from multiple tables, perform business-oriented analysis using SQL, and retrieve data for Python-based workflows.
To train participants to identify data-quality issues and transform raw datasets into reliable, consistent, and analysis-ready information.
Topics covered
Importance of data quality in business analytics.
Identifying missing, duplicated, inconsistent, and invalid records.
Handling missing values using deletion or suitable imputation techniques.
Standardizing text, date, currency, and categorical formats.
Detecting incorrect data types and invalid values.
Identifying outliers and understanding their potential impact.
Data validation rules, range checks, and uniqueness checks.
Data reconciliation and consistency checks across datasets.
Creating reusable data-cleaning functions in Python.
Maintaining a record of data-cleaning decisions and transformations.
What you'll build
- Clean a customer dataset containing missing phone numbers, duplicate entries, and inconsistent text formats.
- Standardize date formats and categorical values.
- Validate employee ages, salaries, transaction amounts, and required fields.
- Compare source and processed datasets to identify unexpected record-count differences.
- Build a reusable Python script that cleans a dataset and produces a data-quality summary.
By the end: Participants will be able to identify common data-quality problems, apply appropriate cleaning techniques, validate the results, and document transformations before analysis.
To develop the ability to explore datasets, identify patterns, apply statistical techniques, and derive meaningful insights from business data.
Topics covered
Purpose and workflow of Exploratory Data Analysis (EDA).
Understanding distributions, data ranges, and variable relationships.
Descriptive statistics: mean, median, mode, variance, and standard deviation.
Percentiles, quartiles, and interquartile range.
Frequency distributions and categorical data analysis.
Correlation and relationships between numerical variables.
Outlier identification using statistical methods and visualizations.
Time-based trends and comparisons across categories.
Introduction to probability, sampling, and hypothesis testing.
Distinguishing correlation from causation.
Translating analytical observations into business findings.
What you'll build
- Perform EDA on a retail sales dataset using Pandas.
- Calculate summary statistics for sales, profit, and order values.
- Identify unusual transactions and investigate potential outliers.
- Analyse relationships between discounts, sales, and profit.
- Compare monthly performance across regions.
- Prepare a short insight report containing findings supported by data.
By the end: Participants will be able to explore datasets systematically, apply fundamental statistical methods, identify trends and unusual patterns, and communicate evidence-based business insights.
To equip participants with the skills required to present analytical results through meaningful visualizations and interactive business dashboards.
Topics covered
Principles of effective data visualization and chart selection.
Creating charts using Matplotlib, Seaborn, and Plotly.
Bar charts, line charts, scatter plots, histograms, box plots, and heatmaps.
Comparing categories and visualizing time-series trends.
Introduction to Microsoft Power BI and its interface.
Importing data from Excel, CSV files, and databases.
Data transformation using Power Query.
Relationships, tables, and basic data modelling.
Creating calculated columns and measures using DAX.
Designing dashboards with KPIs, slicers, filters, and interactive visuals.
Dashboard usability, performance, and presentation best practices.
What you'll build
- Visualize monthly sales, profit, and customer growth using Python.
- Create a correlation heatmap for a business dataset.
- Import sales data into Power BI and transform it using Power Query.
- Build an interactive sales dashboard with region, product, and date filters.
- Create KPI cards for revenue, profit margin, order count, and sales growth.
- Present dashboard findings through a short business-focused explanation.
By the end: Participants will be able to develop clear data visualizations, build interactive Power BI dashboards, and communicate trends and performance indicators effectively.
To develop practical spreadsheet analysis, KPI monitoring, and reporting skills commonly required in corporate analytics assignments.
Topics covered
Excel worksheets, tables, sorting, filtering, and data validation.
Essential formulas and functions for analytical tasks.
Conditional functions such as `IF`, `SUMIF`, `SUMIFS`, `COUNTIF`, and `COUNTIFS`.
Lookup functions such as `XLOOKUP` or `VLOOKUP`, where available.
Text and date functions for data preparation.
PivotTables and PivotCharts for summarizing business data.
Conditional formatting and spreadsheet-based KPI tracking.
Business metrics: revenue, profit, profit margin, growth rate, and customer retention.
Report structuring, summaries, charts, and recommendations.
Communicating insights to technical and non-technical stakeholders.
What you'll build
- Analyse a sales spreadsheet using formulas and PivotTables.
- Create a monthly revenue and profit report.
- Compare performance across products, departments, and regions.
- Build an Excel KPI tracker with conditional formatting.
- Prepare a one-page management report with key findings and recommendations.
- Explain the business implications of the results in simple language.
By the end: Participants will be able to analyse business data in Excel, calculate and monitor KPIs, prepare structured reports, and communicate findings in a format suitable for managers and clients.
To introduce participants to automated data workflows and responsible AI-assisted development practices that improve productivity and reduce repetitive manual work.
Topics covered
Fundamentals of ETL: Extract, Transform, and Load.
Importing data from CSV, Excel, JSON, databases, and APIs.
Introduction to REST APIs and HTTP requests for data retrieval.
Combining data from multiple sources into a unified dataset.
Automating repetitive data-cleaning and reporting tasks with Python.
Generating reports and exporting results programmatically.
Scheduling and monitoring automated scripts at a basic level.
Using AI coding assistants to generate code drafts and SQL queries.
AI-assisted debugging, code explanation, refactoring, test-case generation, and documentation.
Validating AI-generated code and checking outputs against expected results.
Protecting confidential business data and credentials when using AI tools.
Using Git and GitHub for version control and project collaboration.
What you'll build
- Retrieve sample data from an API and convert it into a Pandas DataFrame.
- Combine multiple monthly sales files into one consolidated dataset.
- Create a Python script that cleans data and generates a summary report.
- Use an AI coding assistant to draft a data-cleaning function, then test and correct it.
- Generate SQL queries with AI assistance and verify their results against the dataset.
- Track project changes using Git and maintain documentation in GitHub.
By the end: Participants will be able to build basic automated analytics workflows, integrate data from different sources, use AI tools to accelerate development responsibly, and maintain organized project code.
To enable participants to apply the skills developed throughout the internship by completing a practical analytics project based on a realistic business problem.
Topics covered
Understanding business requirements and defining the problem statement.
Identifying data sources, business questions, and measurable KPIs.
Collecting data from files, databases, or suitable APIs.
Cleaning, transforming, and validating datasets using Python and SQL.
Performing exploratory data analysis and statistical analysis.
Creating visualizations and developing an interactive Power BI dashboard.
Preparing business reports with findings and recommendations.
Testing analytical calculations and validating report accuracy.
Organizing project files, maintaining version control, and documenting the workflow.
Preparing a final presentation and demonstrating the solution to stakeholders.
What you'll build
- Participants will develop an **End-to-End Sales Performance and Business Insights System** that includes:
- Business Requirement Analysis:** Define the business problem and establish KPIs such as revenue, profit, sales growth, and regional performance.
- Data Collection:** Obtain a suitable sales dataset from CSV or Excel files, or another appropriate source.
- Data Preparation:** Clean and validate the data using Python and Pandas.
- Database Analysis:** Store or access structured data and perform business queries using SQL.
- Exploratory Analysis:** Identify sales trends, top-performing products, regional differences, and profit patterns.
- Visualization:** Create charts using Python and develop an interactive Power BI dashboard.
- Reporting:** Prepare a business report summarizing key findings and actionable recommendations.
- Automation:** Build a reusable script to refresh or regenerate analytical outputs from updated input data.
- Quality Checks:** Validate calculations, compare totals, and verify dashboard results against source data.
- Project Presentation:** Demonstrate the workflow, explain the insights, and present the final solution.
By the end: Participants will be able to complete and present an end-to-end data analytics project that demonstrates their ability to collect, prepare, query, analyse, visualize, and report data using industry-relevant tools and workflows.
Additional modules — topics the 1-month doesn't reach
Develop spreadsheet-based analytical skills used in business operations and reporting.
Topics covered
Spreadsheet fundamentals and workbook organisation.
Data sorting and filtering.
Excel tables and structured references.
Mathematical and statistical functions.
IF and nested conditional formulas.
SUMIF and SUMIFS.
COUNTIF and COUNTIFS.
XLOOKUP and lookup-based analysis.
Text and date functions.
Error handling in formulas.
Conditional formatting.
Data validation.
Removing duplicates and identifying missing values.
PivotTables and PivotCharts.
Creating summary reports.
Basic dashboard design.
Automating spreadsheet processing with Python.
What you'll build
- Create an Excel sales report containing region-wise revenue, product rankings, monthly comparisons, and PivotCharts.
- Expected Learning Outcome
- Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
By the end: Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
Apply statistical methods to summarise and interpret data accurately.
Topics covered
Introduction to descriptive statistics.
Mean, median, and mode.
Range, variance, and standard deviation.
Quartiles and percentiles.
Frequency distributions.
Probability fundamentals.
Population and sample concepts.
Sampling methods.
Correlation and covariance.
Understanding distributions.
Introduction to normal distribution.
Confidence intervals.
Hypothesis-testing fundamentals.
Understanding p-values at an introductory level.
Correlation versus causation.
Interpreting statistical results and limitations.
What you'll build
- Analyse customer spending patterns, compare performance across groups, and interpret the results without overstating the conclusions.
- Expected Learning Outcome
- Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
By the end: Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
Explore datasets systematically to identify patterns, relationships, and potential business opportunities.
Topics covered
Understanding dataset structure and dimensions.
Generating summary statistics.
Analysing numerical and categorical variables.
Distribution analysis.
Group-wise comparisons.
Trend analysis.
Correlation analysis.
Identifying outliers.
Detecting potential data quality issues.
Analysing customer and product behaviour.
Investigating changes over time.
Formulating analytical questions.
Documenting observations and assumptions.
Translating observations into business findings.
What you'll build
- Conduct EDA on retail sales data to identify revenue trends, customer segments, product performance, and regional differences.
- Expected Learning Outcome
- Learners can perform structured exploratory analysis and develop evidence-based observations.
By the end: Learners can perform structured exploratory analysis and develop evidence-based observations.
Communicate data patterns through effective visual representations.
Topics covered
Principles of effective data visualisation.
Introduction to Matplotlib.
Introduction to Seaborn.
Line charts and trend analysis.
Bar charts and category comparisons.
Histograms and distribution analysis.
Box plots and outlier visualisation.
Scatter plots and relationship analysis.
Heatmaps and correlation matrices.
Plot titles, labels, legends, and scales.
Choosing suitable chart types.
Avoiding misleading visualisations.
Saving charts for reports.
Introduction to interactive charts with Plotly.
Creating reusable chart functions.
What you'll build
- Create a visual report containing monthly sales trends, category comparisons, customer spending distributions, and regional performance charts.
- Expected Learning Outcome
- Learners can select suitable charts and communicate analytical findings clearly.
By the end: Learners can select suitable charts and communicate analytical findings clearly.
Introduce learners to business intelligence and interactive reporting.
Topics covered
Introduction to business intelligence.
Power BI Desktop interface.
Connecting to Excel, CSV, and database sources.
Importing and inspecting data.
Introduction to Power Query.
Cleaning and transforming data.
Data types and column transformations.
Loading data into the Power BI model.
Creating basic relationships.
Building tables, charts, and KPI cards.
Using slicers and filters.
Formatting reports.
Creating report pages.
Saving and managing Power BI files.
Understanding dashboards versus reports.
What you'll build
- Build a basic sales report showing total revenue, order count, product performance, and regional sales with interactive filters.
- Expected Learning Outcome
- Learners can import data, create basic data models, and build interactive Power BI reports.
By the end: Learners can import data, create basic data models, and build interactive Power BI reports.
Live client projects
In the extended program you work on 2–3 live client projects across the duration. You're not just doing exercises — you're contributing to software a real client is waiting to receive.
Live client project
TalentBeacon — Intelligent Workforce Analytics Platform
Headcount analytics, attrition risk scoring, compensation equity analysis, and hiring velocity dashboards.
PythonPandasPower BISQLStreamlit
Live client project
Employee Attendance and Workforce Analytics System
Attendance anomaly detection, shift compliance tracking, absenteeism trend analysis, and automated summary reports.
PythonNumPyPandasMatplotlibPostgreSQL
Live client project
MailSentry Insight — Email Intelligence & Classification Platform
Inbound support ticket triage, customer sentiment tracking, intent extraction, and automated routing analytics.
PythonScikit-learnNLTKSeabornSQLite
Live client project
Project Cost and Budget Variance Analytics
Milestone expenditure tracking, burn-rate forecasting, cost-overrun alerts, and executive financial dashboards.
PythonPandasPower BIExcel AdvancedSQL
Everything included in your 2–6 month enrollment
✓
120+ live training hours with senior developers✓
Limited VR project sessions (interactive debugging & architecture walkthroughs)✓
15+ modules: all 10 core + 5 advanced modules✓
2–3 live client projects with production code review✓
Internship completion certificate + Letter of Recommendation✓
LinkedIn profile optimization & resume review✓
Remote, hybrid, or in-office at Bangalore2–6 Month Data Science & Analytics Internship
Deeper learning · More projects · Stronger portfolio
₹9,000
₹4,500
50% off
2 / 3 / 4 / 6 months · live daily sessions
Ready for the complete career-ready path?
The Career-Ready flagship bundles everything here with the full Data Science & Analytics career course, industry project standards, and 25 AI-powered development modules — 400+ live hours total. The program serious developers choose when they want job outcomes, not just a certificate.
Career-Ready Program — Flagship
The complete path from zero to job-ready. Four programs bundled into one: the full Data Science & Analytics internship, the advanced Data Science & Analytics career course, industry-grade project engineering, and 25+ modules on AI-powered development. Every day is five hours of live training. This is the program for students who want a real outcome — not just a certificate on a shelf.
400+
live training hours
4
full programs
5+
live client projects
How the 400+ hours break down: 5 live hours per day, 30 hours per week, approximately 120 hours per month — plus extra mentorship, modular support sessions, project reviews, and code walkthroughs on top of scheduled training. Over a 3–6 month program, this compounds to 400+ contact hours.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
100+ VR SessionsUnlimited Revision (2 Yrs)
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
Program 1 — Data Science & Analytics Internship (all 15 modules, complete in full depth)
Complete Foundational Track Included
In the Flagship program, you complete all 15 modules of the internship in full depth, building enterprise capabilities before advancing to specialized architecture.
To introduce participants to the data analytics lifecycle, business intelligence concepts, and the role of data-driven decision-making in organizations.
Topics covered
Fundamentals of data analytics and its importance in modern businesses.
Types of analytics: descriptive, diagnostic, predictive, and prescriptive.
Data analytics lifecycle: collection, cleaning, transformation, analysis, visualization, and reporting.
Structured, semi-structured, and unstructured data.
Introduction to business intelligence, KPIs, metrics, and business reporting.
Industry applications of data analytics in finance, healthcare, retail, education, and IT.
Overview of the data analytics technology stack and commonly used tools.
What you'll build
- Identify data analytics use cases in different business domains.
- Examine a sample sales dataset and identify relevant business questions.
- Define KPIs for a simple business scenario, such as sales performance or customer retention.
- Map the steps required to transform raw data into a business report.
By the end: Participants will understand the purpose of data analytics, the complete analytics workflow, and how organizations use data to support business decisions.
To develop the Python programming skills required to process data, automate repetitive tasks, and build analytical solutions.
Topics covered
Python installation and development environments: Jupyter Notebook and VS Code.
Variables, data types, operators, and type conversion.
Conditional statements and loops.
Functions, parameters, return values, and reusable code.
Lists, tuples, dictionaries, and sets.
String manipulation and commonly used built-in functions.
File handling for reading and writing CSV, text, and JSON files.
Exception handling and basic debugging.
Introduction to Python modules and packages.
What you'll build
- Write Python programs to calculate sales totals, averages, and percentage growth.
- Read a CSV file and display selected records.
- Create functions to calculate revenue, expenses, and profit.
- Process a list of employee records using dictionaries and lists.
- Build a simple Python program to summarize daily sales from a file.
By the end: Participants will be able to write structured Python programs, process basic datasets, work with files, and automate simple data-related tasks.
To teach participants how to efficiently load, manipulate, transform, and prepare datasets for analysis using Python libraries.
Topics covered
Introduction to NumPy arrays and numerical computations.
Array indexing, slicing, filtering, and mathematical operations.
Introduction to Pandas Series and DataFrames.
Importing datasets from CSV and Excel files.
Inspecting datasets using `head()`, `info()`, `describe()`, and related methods.
Selecting, filtering, sorting, and renaming columns.
Handling missing values and duplicate records.
Grouping and aggregation using `groupby()`.
Combining datasets using merge, join, and concatenation operations.
Creating calculated columns and transforming data.
Exporting processed data into CSV and Excel formats.
What you'll build
- Load a sales dataset and examine its structure and data types.
- Filter transactions based on product, region, date, or revenue.
- Calculate total sales and average order value by category.
- Merge customer and transaction datasets.
- Clean missing values and duplicate records in an employee dataset.
- Generate a summarized dataset for further reporting.
By the end: Participants will be able to use NumPy and Pandas to manipulate datasets, perform common transformations, combine data from multiple sources, and prepare analysis-ready data.
To develop the ability to retrieve, filter, aggregate, and analyse business data stored in relational databases.
Topics covered
Introduction to relational databases, tables, rows, columns, and relationships.
Database concepts: primary keys, foreign keys, and constraints.
SQL commands: DDL, DML, DQL, and transaction-control fundamentals.
Data retrieval using `SELECT`, `WHERE`, `DISTINCT`, and `ORDER BY`.
Filtering records using logical operators, `IN`, `BETWEEN`, and pattern matching.
Aggregate functions: `COUNT()`, `SUM()`, `AVG()`, `MIN()`, and `MAX()`.
Grouping and filtering aggregated results using `GROUP BY` and `HAVING`.
Joins: INNER, LEFT, RIGHT, and FULL OUTER JOIN where supported.
Subqueries and Common Table Expressions (CTEs).
Introduction to window functions such as `ROW_NUMBER()`, `RANK()`, and `SUM() OVER()`.
Connecting Python applications to databases such as MySQL or PostgreSQL.
What you'll build
- Create a database containing customer, product, employee, and sales tables.
- Write SQL queries to identify top-selling products and high-revenue regions.
- Calculate monthly revenue and department-wise employee counts.
- Join customer and order tables to analyse customer purchasing behaviour.
- Use a subquery or CTE to identify customers whose purchases exceed the average.
- Retrieve database records through Python for analysis.
By the end: Participants will be able to query relational databases, combine information from multiple tables, perform business-oriented analysis using SQL, and retrieve data for Python-based workflows.
To train participants to identify data-quality issues and transform raw datasets into reliable, consistent, and analysis-ready information.
Topics covered
Importance of data quality in business analytics.
Identifying missing, duplicated, inconsistent, and invalid records.
Handling missing values using deletion or suitable imputation techniques.
Standardizing text, date, currency, and categorical formats.
Detecting incorrect data types and invalid values.
Identifying outliers and understanding their potential impact.
Data validation rules, range checks, and uniqueness checks.
Data reconciliation and consistency checks across datasets.
Creating reusable data-cleaning functions in Python.
Maintaining a record of data-cleaning decisions and transformations.
What you'll build
- Clean a customer dataset containing missing phone numbers, duplicate entries, and inconsistent text formats.
- Standardize date formats and categorical values.
- Validate employee ages, salaries, transaction amounts, and required fields.
- Compare source and processed datasets to identify unexpected record-count differences.
- Build a reusable Python script that cleans a dataset and produces a data-quality summary.
By the end: Participants will be able to identify common data-quality problems, apply appropriate cleaning techniques, validate the results, and document transformations before analysis.
To develop the ability to explore datasets, identify patterns, apply statistical techniques, and derive meaningful insights from business data.
Topics covered
Purpose and workflow of Exploratory Data Analysis (EDA).
Understanding distributions, data ranges, and variable relationships.
Descriptive statistics: mean, median, mode, variance, and standard deviation.
Percentiles, quartiles, and interquartile range.
Frequency distributions and categorical data analysis.
Correlation and relationships between numerical variables.
Outlier identification using statistical methods and visualizations.
Time-based trends and comparisons across categories.
Introduction to probability, sampling, and hypothesis testing.
Distinguishing correlation from causation.
Translating analytical observations into business findings.
What you'll build
- Perform EDA on a retail sales dataset using Pandas.
- Calculate summary statistics for sales, profit, and order values.
- Identify unusual transactions and investigate potential outliers.
- Analyse relationships between discounts, sales, and profit.
- Compare monthly performance across regions.
- Prepare a short insight report containing findings supported by data.
By the end: Participants will be able to explore datasets systematically, apply fundamental statistical methods, identify trends and unusual patterns, and communicate evidence-based business insights.
To equip participants with the skills required to present analytical results through meaningful visualizations and interactive business dashboards.
Topics covered
Principles of effective data visualization and chart selection.
Creating charts using Matplotlib, Seaborn, and Plotly.
Bar charts, line charts, scatter plots, histograms, box plots, and heatmaps.
Comparing categories and visualizing time-series trends.
Introduction to Microsoft Power BI and its interface.
Importing data from Excel, CSV files, and databases.
Data transformation using Power Query.
Relationships, tables, and basic data modelling.
Creating calculated columns and measures using DAX.
Designing dashboards with KPIs, slicers, filters, and interactive visuals.
Dashboard usability, performance, and presentation best practices.
What you'll build
- Visualize monthly sales, profit, and customer growth using Python.
- Create a correlation heatmap for a business dataset.
- Import sales data into Power BI and transform it using Power Query.
- Build an interactive sales dashboard with region, product, and date filters.
- Create KPI cards for revenue, profit margin, order count, and sales growth.
- Present dashboard findings through a short business-focused explanation.
By the end: Participants will be able to develop clear data visualizations, build interactive Power BI dashboards, and communicate trends and performance indicators effectively.
To develop practical spreadsheet analysis, KPI monitoring, and reporting skills commonly required in corporate analytics assignments.
Topics covered
Excel worksheets, tables, sorting, filtering, and data validation.
Essential formulas and functions for analytical tasks.
Conditional functions such as `IF`, `SUMIF`, `SUMIFS`, `COUNTIF`, and `COUNTIFS`.
Lookup functions such as `XLOOKUP` or `VLOOKUP`, where available.
Text and date functions for data preparation.
PivotTables and PivotCharts for summarizing business data.
Conditional formatting and spreadsheet-based KPI tracking.
Business metrics: revenue, profit, profit margin, growth rate, and customer retention.
Report structuring, summaries, charts, and recommendations.
Communicating insights to technical and non-technical stakeholders.
What you'll build
- Analyse a sales spreadsheet using formulas and PivotTables.
- Create a monthly revenue and profit report.
- Compare performance across products, departments, and regions.
- Build an Excel KPI tracker with conditional formatting.
- Prepare a one-page management report with key findings and recommendations.
- Explain the business implications of the results in simple language.
By the end: Participants will be able to analyse business data in Excel, calculate and monitor KPIs, prepare structured reports, and communicate findings in a format suitable for managers and clients.
To introduce participants to automated data workflows and responsible AI-assisted development practices that improve productivity and reduce repetitive manual work.
Topics covered
Fundamentals of ETL: Extract, Transform, and Load.
Importing data from CSV, Excel, JSON, databases, and APIs.
Introduction to REST APIs and HTTP requests for data retrieval.
Combining data from multiple sources into a unified dataset.
Automating repetitive data-cleaning and reporting tasks with Python.
Generating reports and exporting results programmatically.
Scheduling and monitoring automated scripts at a basic level.
Using AI coding assistants to generate code drafts and SQL queries.
AI-assisted debugging, code explanation, refactoring, test-case generation, and documentation.
Validating AI-generated code and checking outputs against expected results.
Protecting confidential business data and credentials when using AI tools.
Using Git and GitHub for version control and project collaboration.
What you'll build
- Retrieve sample data from an API and convert it into a Pandas DataFrame.
- Combine multiple monthly sales files into one consolidated dataset.
- Create a Python script that cleans data and generates a summary report.
- Use an AI coding assistant to draft a data-cleaning function, then test and correct it.
- Generate SQL queries with AI assistance and verify their results against the dataset.
- Track project changes using Git and maintain documentation in GitHub.
By the end: Participants will be able to build basic automated analytics workflows, integrate data from different sources, use AI tools to accelerate development responsibly, and maintain organized project code.
To enable participants to apply the skills developed throughout the internship by completing a practical analytics project based on a realistic business problem.
Topics covered
Understanding business requirements and defining the problem statement.
Identifying data sources, business questions, and measurable KPIs.
Collecting data from files, databases, or suitable APIs.
Cleaning, transforming, and validating datasets using Python and SQL.
Performing exploratory data analysis and statistical analysis.
Creating visualizations and developing an interactive Power BI dashboard.
Preparing business reports with findings and recommendations.
Testing analytical calculations and validating report accuracy.
Organizing project files, maintaining version control, and documenting the workflow.
Preparing a final presentation and demonstrating the solution to stakeholders.
What you'll build
- Participants will develop an **End-to-End Sales Performance and Business Insights System** that includes:
- Business Requirement Analysis:** Define the business problem and establish KPIs such as revenue, profit, sales growth, and regional performance.
- Data Collection:** Obtain a suitable sales dataset from CSV or Excel files, or another appropriate source.
- Data Preparation:** Clean and validate the data using Python and Pandas.
- Database Analysis:** Store or access structured data and perform business queries using SQL.
- Exploratory Analysis:** Identify sales trends, top-performing products, regional differences, and profit patterns.
- Visualization:** Create charts using Python and develop an interactive Power BI dashboard.
- Reporting:** Prepare a business report summarizing key findings and actionable recommendations.
- Automation:** Build a reusable script to refresh or regenerate analytical outputs from updated input data.
- Quality Checks:** Validate calculations, compare totals, and verify dashboard results against source data.
- Project Presentation:** Demonstrate the workflow, explain the insights, and present the final solution.
By the end: Participants will be able to complete and present an end-to-end data analytics project that demonstrates their ability to collect, prepare, query, analyse, visualize, and report data using industry-relevant tools and workflows.
Develop spreadsheet-based analytical skills used in business operations and reporting.
Topics covered
Spreadsheet fundamentals and workbook organisation.
Data sorting and filtering.
Excel tables and structured references.
Mathematical and statistical functions.
IF and nested conditional formulas.
SUMIF and SUMIFS.
COUNTIF and COUNTIFS.
XLOOKUP and lookup-based analysis.
Text and date functions.
Error handling in formulas.
Conditional formatting.
Data validation.
Removing duplicates and identifying missing values.
PivotTables and PivotCharts.
Creating summary reports.
Basic dashboard design.
Automating spreadsheet processing with Python.
What you'll build
- Create an Excel sales report containing region-wise revenue, product rankings, monthly comparisons, and PivotCharts.
- Expected Learning Outcome
- Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
By the end: Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
Apply statistical methods to summarise and interpret data accurately.
Topics covered
Introduction to descriptive statistics.
Mean, median, and mode.
Range, variance, and standard deviation.
Quartiles and percentiles.
Frequency distributions.
Probability fundamentals.
Population and sample concepts.
Sampling methods.
Correlation and covariance.
Understanding distributions.
Introduction to normal distribution.
Confidence intervals.
Hypothesis-testing fundamentals.
Understanding p-values at an introductory level.
Correlation versus causation.
Interpreting statistical results and limitations.
What you'll build
- Analyse customer spending patterns, compare performance across groups, and interpret the results without overstating the conclusions.
- Expected Learning Outcome
- Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
By the end: Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
Explore datasets systematically to identify patterns, relationships, and potential business opportunities.
Topics covered
Understanding dataset structure and dimensions.
Generating summary statistics.
Analysing numerical and categorical variables.
Distribution analysis.
Group-wise comparisons.
Trend analysis.
Correlation analysis.
Identifying outliers.
Detecting potential data quality issues.
Analysing customer and product behaviour.
Investigating changes over time.
Formulating analytical questions.
Documenting observations and assumptions.
Translating observations into business findings.
What you'll build
- Conduct EDA on retail sales data to identify revenue trends, customer segments, product performance, and regional differences.
- Expected Learning Outcome
- Learners can perform structured exploratory analysis and develop evidence-based observations.
By the end: Learners can perform structured exploratory analysis and develop evidence-based observations.
Communicate data patterns through effective visual representations.
Topics covered
Principles of effective data visualisation.
Introduction to Matplotlib.
Introduction to Seaborn.
Line charts and trend analysis.
Bar charts and category comparisons.
Histograms and distribution analysis.
Box plots and outlier visualisation.
Scatter plots and relationship analysis.
Heatmaps and correlation matrices.
Plot titles, labels, legends, and scales.
Choosing suitable chart types.
Avoiding misleading visualisations.
Saving charts for reports.
Introduction to interactive charts with Plotly.
Creating reusable chart functions.
What you'll build
- Create a visual report containing monthly sales trends, category comparisons, customer spending distributions, and regional performance charts.
- Expected Learning Outcome
- Learners can select suitable charts and communicate analytical findings clearly.
By the end: Learners can select suitable charts and communicate analytical findings clearly.
Introduce learners to business intelligence and interactive reporting.
Topics covered
Introduction to business intelligence.
Power BI Desktop interface.
Connecting to Excel, CSV, and database sources.
Importing and inspecting data.
Introduction to Power Query.
Cleaning and transforming data.
Data types and column transformations.
Loading data into the Power BI model.
Creating basic relationships.
Building tables, charts, and KPI cards.
Using slicers and filters.
Formatting reports.
Creating report pages.
Saving and managing Power BI files.
Understanding dashboards versus reports.
What you'll build
- Build a basic sales report showing total revenue, order count, product performance, and regional sales with interactive filters.
- Expected Learning Outcome
- Learners can import data, create basic data models, and build interactive Power BI reports.
By the end: Learners can import data, create basic data models, and build interactive Power BI reports.
Program 2 — Full Data Science & Analytics Career Course
Understand the fundamentals of data analytics, its business applications, and the role of data professionals.
Topics covered
Introduction to data and information.
What is data analytics and why organisations use it.
Types of data: structured, semi-structured, and unstructured.
Types of analytics: descriptive, diagnostic, predictive, and prescriptive.
Data analytics lifecycle.
Role of a data analyst and BI analyst.
Differences between data analytics, data science, and data engineering.
Common analytics use cases across industries.
Understanding business problems through data.
Introduction to datasets, data sources, metrics, and KPIs.
Overview of the data analytics technology stack.
What you'll build
- Examine a sample retail sales dataset and identify the business questions that can be answered using sales, customer, product, and regional data.
- Expected Learning Outcome
- Learners understand the purpose of data analytics, the responsibilities of an analyst, and how data supports business decisions.
By the end: Learners understand the purpose of data analytics, the responsibilities of an analyst, and how data supports business decisions.
Understand how data is represented, collected, and prepared for analysis.
Topics covered
Numerical, categorical, ordinal, and date/time data.
Structured and semi-structured data formats.
Data sources: files, databases, APIs, and business applications.
Introduction to CSV, Excel, JSON, and text files.
Understanding rows, columns, records, and fields.
Data schemas and column definitions.
Primary keys and unique identifiers.
Introduction to transactional and reference data.
Data collection methods and source reliability.
Data privacy and responsible data handling.
Identifying incomplete or inconsistent data sources.
What you'll build
- Import customer, product, and sales datasets in different formats and document their structures, data types, and key fields.
- Expected Learning Outcome
- Learners can identify suitable data sources, understand common data formats, and prepare datasets for further processing.
By the end: Learners can identify suitable data sources, understand common data formats, and prepare datasets for further processing.
Develop the Python programming skills needed for data analytics.
Topics covered
Installing and configuring Python.
Using VS Code and Jupyter Notebook.
Variables, constants, and data types.
Operators and expressions.
Conditional statements.
For loops and while loops.
Lists, tuples, sets, and dictionaries.
String operations and formatting.
Functions, parameters, and return values.
Built-in functions and modules.
File handling fundamentals.
Exception handling.
Writing readable and reusable code.
Debugging basic Python programs.
What you'll build
- Develop programs to calculate employee salary summaries, process transaction amounts, classify records using conditions, and generate basic statistical summaries.
- Expected Learning Outcome
- Learners can write and execute Python programs that form the foundation for data manipulation and analytical workflows.
By the end: Learners can write and execute Python programs that form the foundation for data manipulation and analytical workflows.
Apply Python programming concepts to structured data-processing tasks.
Topics covered
Functions and modular programming.
List and dictionary comprehensions.
Lambda functions and introductory functional operations.
Sorting and filtering collections.
Working with dates and times.
Working with CSV and JSON files.
Reading and writing text files.
Reusable functions for data processing.
Exception handling and input validation.
Installing libraries using pip.
Understanding dependencies and requirements files.
Basic code organisation and naming conventions.
What you'll build
- Build a Python program that reads transaction records, validates required fields, calculates category-wise totals, and exports a summary file.
- Expected Learning Outcome
- Learners can develop modular Python scripts for repeatable data-processing tasks.
By the end: Learners can develop modular Python scripts for repeatable data-processing tasks.
Perform efficient numerical operations using NumPy.
Topics covered
Introduction to NumPy arrays.
Creating and inspecting arrays.
Array dimensions and shapes.
Indexing and slicing.
Reshaping arrays.
Arithmetic and comparison operations.
Vectorised calculations.
Aggregations such as sum, mean, minimum, and maximum.
Boolean indexing and filtering.
Handling missing or invalid numerical values.
Broadcasting fundamentals.
Introduction to random number generation.
Comparing array operations with standard Python operations.
What you'll build
- Use NumPy to analyse product prices, calculate revenue totals, compare monthly performance, and perform numerical transformations on sample business data.
- Expected Learning Outcome
- Learners can perform efficient numerical calculations and understand how NumPy supports data analytics.
By the end: Learners can perform efficient numerical calculations and understand how NumPy supports data analytics.
Manipulate and analyse structured datasets using Pandas.
Topics covered
Introduction to Series and DataFrames.
Creating DataFrames.
Importing CSV and Excel files.
Reading JSON data.
Inspecting datasets using head, tail, info, and describe.
Selecting rows and columns.
Filtering and sorting records.
Adding, updating, and removing columns.
Renaming columns.
Handling data types.
Grouping and aggregating data.
Merging and joining DataFrames.
Concatenating datasets.
Exporting results to CSV and Excel.
Writing reusable data transformation functions.
What you'll build
- Analyse a retail sales dataset to calculate total revenue, average order value, product performance, and region-wise sales.
- Expected Learning Outcome
- Learners can perform common data analysis tasks using Pandas and transform raw datasets into useful analytical outputs.
By the end: Learners can perform common data analysis tasks using Pandas and transform raw datasets into useful analytical outputs.
Improve data quality before performing analysis or building reports.
Topics covered
Importance of data quality.
Identifying missing values.
Handling duplicate records.
Standardising column names.
Correcting inconsistent formats.
Converting data types.
Cleaning date and time fields.
Handling inconsistent category labels.
Identifying invalid values.
Detecting outliers for further investigation.
Applying validation rules.
Combining datasets with different structures.
Documenting data-cleaning decisions.
Comparing datasets before and after cleaning.
Preserving raw data and transformation traceability.
What you'll build
- Clean a customer dataset containing duplicate records, missing email addresses, inconsistent city names, invalid dates, and improperly formatted numerical values.
- Expected Learning Outcome
- Learners can identify and handle common data quality problems without making unsupported changes to the original data.
By the end: Learners can identify and handle common data quality problems without making unsupported changes to the original data.
Learn to retrieve and manipulate data stored in relational databases.
Topics covered
Introduction to relational databases.
Tables, rows, columns, and data types.
Creating databases and tables.
Primary keys and foreign keys.
SELECT and DISTINCT.
WHERE conditions.
AND, OR, and NOT operators.
ORDER BY and LIMIT.
INSERT, UPDATE, and DELETE.
Aggregate functions.
COUNT, SUM, AVG, MIN, and MAX.
NULL values.
Constraints and basic data integrity.
Understanding SQL query structure.
What you'll build
- Create a simple sales database and write queries to retrieve transactions, filter products, calculate total sales, and summarise customer orders.
- Expected Learning Outcome
- Learners can create basic database structures and write SQL queries to retrieve and manipulate business data.
By the end: Learners can create basic database structures and write SQL queries to retrieve and manipulate business data.
Use advanced SQL techniques to solve complex analytical requirements.
Topics covered
GROUP BY and HAVING.
INNER JOIN and LEFT JOIN.
RIGHT JOIN and self joins.
Multiple-table joins.
Subqueries.
Common table expressions.
CASE statements.
Conditional aggregation.
Date and string functions.
Window functions.
ROW\_NUMBER, RANK, and DENSE\_RANK.
Running totals and cumulative calculations.
Moving averages.
Month-over-month comparisons.
Year-over-year analysis.
Views and reusable queries.
Introduction to query optimisation and indexing.
Analysing query results for accuracy.
What you'll build
- Write SQL queries to identify top customers, rank products by revenue, calculate monthly growth, analyse repeat purchases, and compare sales across regions.
- Expected Learning Outcome
- Learners can use advanced SQL to perform analytical calculations and prepare data for dashboards and business reports.
By the end: Learners can use advanced SQL to perform analytical calculations and prepare data for dashboards and business reports.
Understand relational database design and manage data for analytical applications.
Topics covered
Database concepts and architecture.
Database schemas and relationships.
Entity-relationship diagrams.
Primary and foreign key relationships.
Database normalisation fundamentals.
One-to-one, one-to-many, and many-to-many relationships.
Referential integrity.
Designing tables for business applications.
Views and reusable database objects.
Transactions and basic ACID concepts.
Database users and permissions.
Importing and exporting database records.
Connecting Python to a database.
Executing SQL queries from Python.
Secure credential management.
What you'll build
- Design a database for an order management system containing customers, products, orders, and order items. Connect Python to the database and retrieve analytical summaries.
- Expected Learning Outcome
- Learners understand database design and can retrieve data from relational databases for analysis.
By the end: Learners understand database design and can retrieve data from relational databases for analysis.
Develop spreadsheet-based analytical skills used in business operations and reporting.
Topics covered
Spreadsheet fundamentals and workbook organisation.
Data sorting and filtering.
Excel tables and structured references.
Mathematical and statistical functions.
IF and nested conditional formulas.
SUMIF and SUMIFS.
COUNTIF and COUNTIFS.
XLOOKUP and lookup-based analysis.
Text and date functions.
Error handling in formulas.
Conditional formatting.
Data validation.
Removing duplicates and identifying missing values.
PivotTables and PivotCharts.
Creating summary reports.
Basic dashboard design.
Automating spreadsheet processing with Python.
What you'll build
- Create an Excel sales report containing region-wise revenue, product rankings, monthly comparisons, and PivotCharts.
- Expected Learning Outcome
- Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
By the end: Learners can analyse data in Excel, prepare business summaries, and reduce repetitive spreadsheet work.
Apply statistical methods to summarise and interpret data accurately.
Topics covered
Introduction to descriptive statistics.
Mean, median, and mode.
Range, variance, and standard deviation.
Quartiles and percentiles.
Frequency distributions.
Probability fundamentals.
Population and sample concepts.
Sampling methods.
Correlation and covariance.
Understanding distributions.
Introduction to normal distribution.
Confidence intervals.
Hypothesis-testing fundamentals.
Understanding p-values at an introductory level.
Correlation versus causation.
Interpreting statistical results and limitations.
What you'll build
- Analyse customer spending patterns, compare performance across groups, and interpret the results without overstating the conclusions.
- Expected Learning Outcome
- Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
By the end: Learners can apply basic statistical techniques and explain what analytical results do and do not establish.
Explore datasets systematically to identify patterns, relationships, and potential business opportunities.
Topics covered
Understanding dataset structure and dimensions.
Generating summary statistics.
Analysing numerical and categorical variables.
Distribution analysis.
Group-wise comparisons.
Trend analysis.
Correlation analysis.
Identifying outliers.
Detecting potential data quality issues.
Analysing customer and product behaviour.
Investigating changes over time.
Formulating analytical questions.
Documenting observations and assumptions.
Translating observations into business findings.
What you'll build
- Conduct EDA on retail sales data to identify revenue trends, customer segments, product performance, and regional differences.
- Expected Learning Outcome
- Learners can perform structured exploratory analysis and develop evidence-based observations.
By the end: Learners can perform structured exploratory analysis and develop evidence-based observations.
Communicate data patterns through effective visual representations.
Topics covered
Principles of effective data visualisation.
Introduction to Matplotlib.
Introduction to Seaborn.
Line charts and trend analysis.
Bar charts and category comparisons.
Histograms and distribution analysis.
Box plots and outlier visualisation.
Scatter plots and relationship analysis.
Heatmaps and correlation matrices.
Plot titles, labels, legends, and scales.
Choosing suitable chart types.
Avoiding misleading visualisations.
Saving charts for reports.
Introduction to interactive charts with Plotly.
Creating reusable chart functions.
What you'll build
- Create a visual report containing monthly sales trends, category comparisons, customer spending distributions, and regional performance charts.
- Expected Learning Outcome
- Learners can select suitable charts and communicate analytical findings clearly.
By the end: Learners can select suitable charts and communicate analytical findings clearly.
Introduce learners to business intelligence and interactive reporting.
Topics covered
Introduction to business intelligence.
Power BI Desktop interface.
Connecting to Excel, CSV, and database sources.
Importing and inspecting data.
Introduction to Power Query.
Cleaning and transforming data.
Data types and column transformations.
Loading data into the Power BI model.
Creating basic relationships.
Building tables, charts, and KPI cards.
Using slicers and filters.
Formatting reports.
Creating report pages.
Saving and managing Power BI files.
Understanding dashboards versus reports.
What you'll build
- Build a basic sales report showing total revenue, order count, product performance, and regional sales with interactive filters.
- Expected Learning Outcome
- Learners can import data, create basic data models, and build interactive Power BI reports.
By the end: Learners can import data, create basic data models, and build interactive Power BI reports.
Develop advanced analytical dashboards using data modelling and DAX calculations.
Topics covered
Star schema fundamentals.
Fact tables and dimension tables.
Relationship cardinality and filter direction.
Calculated columns versus measures.
DAX syntax and evaluation context fundamentals.
SUM, COUNTROWS, DISTINCTCOUNT, and AVERAGE.
CALCULATE and filter context.
DIVIDE and conditional calculations.
Date tables and time intelligence.
Month-over-month and year-over-year calculations.
KPI measures and performance indicators.
Drill-down and drill-through.
Report interactions and cross-filtering.
Dashboard layout and usability.
Model and report performance fundamentals.
Publishing and sharing reports according to organisational access policies.
Refresh configuration and troubleshooting.
What you'll build
- Develop an interactive business dashboard that analyses revenue, profit, sales growth, regional performance, and product category trends.
- Expected Learning Outcome
- Learners can create advanced Power BI reports with consistent KPI definitions and interactive analytical features.
By the end: Learners can create advanced Power BI reports with consistent KPI definitions and interactive analytical features.
Translate business goals into measurable metrics and analytical reports.
Topics covered
Understanding business objectives.
Defining measurable KPIs.
Revenue and profit calculations.
Profit margin and growth rate.
Average order value.
Customer acquisition and retention metrics.
Repeat purchase rate.
Inventory and stock movement indicators.
Employee productivity metrics.
Conversion rate fundamentals.
Operational efficiency indicators.
Establishing baselines and comparison periods.
Selecting dimensions and filters.
Documenting metric definitions.
Distinguishing descriptive, diagnostic, and predictive questions.
What you'll build
- Develop a KPI framework for a retail business and create the SQL queries, Excel calculations, or Power BI measures needed to calculate the selected indicators.
- Expected Learning Outcome
- Learners can connect technical analysis to business goals and define KPIs that are measurable and clearly documented.
By the end: Learners can connect technical analysis to business goals and define KPIs that are measurable and clearly documented.
Understand how data is extracted from multiple sources, transformed, and loaded into analytical systems.
Topics covered
ETL and ELT fundamentals.
Data source identification.
Extracting data from files and databases.
Combining data from multiple sources.
Source-to-target mapping.
Transformation and standardisation.
Full loads and incremental loads.
Handling new and updated records.
Avoiding duplicate processing.
Data validation and reconciliation.
Error handling and logging.
Failed-record management.
Reusable data processing functions.
Introduction to data pipeline design.
What you'll build
- Build a workflow that imports sales data from CSV, product information from Excel, and customer information from a database, then combines them into a validated analytical dataset.
- Expected Learning Outcome
- Learners understand the fundamentals of data integration and can build basic repeatable ETL workflows.
By the end: Learners understand the fundamentals of data integration and can build basic repeatable ETL workflows.
Learn how to collect data from approved external systems using APIs.
Topics covered
Introduction to APIs.
REST API fundamentals.
HTTP methods and status codes.
Endpoints and query parameters.
JSON request and response structures.
Making API requests using Python.
Authentication fundamentals.
Pagination and rate limits.
Timeouts and error handling.
Validating API responses.
Converting JSON into DataFrames.
Introduction to Postman.
Managing credentials securely.
Respecting access permissions and API usage policies.
What you'll build
- Retrieve permitted public or synthetic data from an API, convert it into a structured dataset, and analyse the results.
- Expected Learning Outcome
- Learners can collect external data through APIs and integrate it into a basic analytics workflow.
By the end: Learners can collect external data through APIs and integrate it into a basic analytics workflow.
Automate repetitive data processing and reporting tasks using Python and approved scheduling tools.
Topics covered
Identifying repetitive manual tasks.
Creating reusable Python scripts.
Automating file imports and transformations.
Running SQL queries from Python.
Generating Excel reports and charts.
Exporting data to multiple formats.
Scheduling scripts with Windows Task Scheduler or cron.
Introduction to automated workflow execution.
Logging and monitoring script execution.
Handling missing files and processing failures.
Avoiding duplicate processing during reruns.
Sending approved completion or failure notifications.
Recording execution history.
Comparing manual and automated workflows.
What you'll build
- Build an automated reporting workflow that processes sales files, calculates KPIs, creates an Excel report, and records whether the process completed successfully.
- Expected Learning Outcome
- Learners can create basic automation workflows that reduce repetitive manual reporting.
By the end: Learners can create basic automation workflows that reduce repetitive manual reporting.
Ensure that datasets, calculations, and reports meet defined accuracy and quality requirements.
Topics covered
Data quality dimensions.
Completeness, validity, consistency, uniqueness, and timeliness.
Defining validation rules.
Identifying missing and duplicate records.
Validating data types and ranges.
Checking SQL query results.
Reconciling source and processed totals.
Testing Python functions.
Introduction to pytest.
Testing empty datasets and invalid inputs.
Regression testing fundamentals.
Testing joins and aggregation logic.
Dashboard KPI validation.
Recording defects and corrective actions.
Documenting test results.
What you'll build
- Create a set of validation checks for a sales analytics pipeline and verify that its output matches expected results.
- Expected Learning Outcome
- Learners can test their analytical work and demonstrate that reports and calculations are reliable.
By the end: Learners can test their analytical work and demonstrate that reports and calculations are reliable.
Introduce version control and project organisation practices used by analytics teams.
Topics covered
Introduction to version control.
Installing and configuring Git.
Creating repositories.
Git add, commit, push, pull, and clone.
Working with branches.
Merging changes.
Resolving basic merge conflicts.
Writing meaningful commit messages.
Using GitHub for project storage.
Writing README files.
Organising scripts, notebooks, SQL files, and tests.
Maintaining requirements files.
Using `.gitignore`.
Avoiding the publication of passwords and confidential data.
Reviewing code changes.
Preparing project documentation.
What you'll build
- Create a GitHub repository for a data analytics project containing Python scripts, SQL queries, sample data, a README file, and setup instructions.
- Expected Learning Outcome
- Learners can manage analytics projects using version control and maintain a professional, reproducible project repository.
By the end: Learners can manage analytics projects using version control and maintain a professional, reproducible project repository.
Introduce cloud technologies commonly used to store, process, and analyse business data.
Topics covered
Introduction to cloud computing.
Cloud storage fundamentals.
Relational databases in the cloud.
Data lakes and data warehouses.
Introduction to managed analytics services.
Uploading and accessing approved datasets.
Cloud permissions and identity management.
Secure data storage and transfer.
Batch processing fundamentals.
Data refresh and scheduled execution.
Monitoring and cost awareness.
Backup and recovery concepts.
Comparing local and cloud-based analytics.
Example Technologies
AWS: Amazon S3, Amazon Athena, Amazon RDS.
Microsoft Azure: Azure Storage, Azure SQL Database, Azure Data Factory.
Google Cloud: Cloud Storage and BigQuery.
What you'll build
- Use an approved training environment to store a sample dataset in one cloud platform and perform a basic analytical query or transformation.
- Expected Learning Outcome
- Learners understand cloud analytics architecture and recognise when cloud-based services may be useful.
- Note:** Cloud services are introduced at a foundational level. Detailed cloud engineering should be treated as a separate specialisation if required.
By the end: Learners understand cloud analytics architecture and recognise when cloud-based services may be useful. Note:** Cloud services are introduced at a foundational level. Detailed cloud engineering should be treated as a separate specialisation if required.
Use approved AI tools to accelerate coding, troubleshooting, documentation, and analytical development while maintaining quality.
Topics covered
Introduction to AI-assisted development.
Using ChatGPT or GitHub Copilot for coding support.
Writing effective prompts for Python and SQL.
Generating starter code from business requirements.
Creating Pandas transformations with AI assistance.
Generating SQL queries from table schemas.
Producing Excel formulas and DAX measures.
Understanding and modifying generated code.
Debugging errors with AI assistance.
Generating test cases and validation rules.
Creating documentation and code explanations.
Reviewing performance and readability.
Identifying hallucinations and incorrect suggestions.
Protecting confidential information.
Validating calculations and generated outputs.
Measuring productivity improvements against a manual baseline.
What you'll build
- Use an AI assistant to develop a sales analysis script, then review, test, and improve the generated code. Compare its output with manually verified calculations and document the corrections required.
- Expected Learning Outcome
- Learners can use AI as a coding assistant to improve development speed while retaining responsibility for code quality, data accuracy, and security.
By the end: Learners can use AI as a coding assistant to improve development speed while retaining responsibility for code quality, data accuracy, and security.
Apply the knowledge acquired throughout the course to develop a complete analytics solution based on a realistic business scenario.
Topics covered
By the end: Project requirements document. Data dictionary. Database schema and SQL queries, where applicable. Python scripts and analytical notebooks. Cleaned and validated dataset. Data quality report. KPI calculations. Interactive Power BI dashboard. Automated Excel report. Test cases and validation results. GitHub repository and README. Final presentation and project documentation.
Program 3 — Industry Project Engineering Standards (25 modules)
The complete professional software engineering curriculum — from requirements through deployment, monitoring, and client handover. Every module produces a real deliverable. By the end, you have shipped a complete application the way a consulting firm would: requirements signed off, code tested, system documented, and ownership transferred.
Understand the role of data analytics in corporate operations and how analytics projects deliver business value.
Topics covered
Role of data analysts, BI developers, analytics engineers, and data engineers.
Differences between academic exercises and production-oriented client projects.
Common business domains: retail, banking, healthcare, manufacturing, HR, logistics, and sales.
Typical analytics use cases, such as sales performance, customer retention, inventory management, and operational efficiency.
Data analytics lifecycle: requirement gathering, data acquisition, preparation, analysis, visualisation, testing, deployment, and maintenance.
Stakeholders involved in analytics projects.
Business KPIs and performance measurement.
What you'll build
- and production-oriented client projects.
- Common business domains: retail, banking, healthcare, manufacturing, HR, logistics, and sales.
- Typical analytics use cases, such as sales performance, customer retention, inventory management, and operational efficiency.
- Data analytics lifecycle: requirement gathering, data acquisition, preparation, analysis, visualisation, testing, deployment, and maintenance.
- Stakeholders involved in analytics projects.
- Business KPIs and performance measurement.
By the end: expected from an industrial analytics project. Functional and non-functional requirements.
Convert client expectations into clear and implementable analytics requirements.
Topics covered
Conducting requirement-gathering sessions.
Understanding client pain points and business objectives.
Preparing requirement questionnaires.
Identifying business users and report consumers.
Translating business questions into analytical requirements.
Defining KPIs and their calculation rules.
Establishing dashboard filters, reporting frequency, and access requirements.
Identifying data availability and source-system limitations.
Documenting assumptions, exclusions, and dependencies.
Preparing a Business Requirements Document (BRD).
Writing acceptance criteria for reports and dashboards.
Managing changes in client requirements.
By the end: Interns can document requirements clearly and reduce misunderstandings between business stakeholders and the development team.
Identify available data sources and determine whether their contents are suitable for the project.
Topics covered
Understanding structured, semi-structured, and unstructured data.
Identifying data sources and data owners.
Analysing CSV, Excel, JSON, and database tables.
Understanding ERP, CRM, HRMS, inventory, and transaction-system data.
Reviewing table schemas and relationships.
Identifying missing fields and inconsistent definitions.
Analysing record counts, data types, and date ranges.
Understanding primary keys, foreign keys, and business identifiers.
Assessing source-system limitations and data freshness.
Creating source-to-target mapping documents.
Understanding data lineage and traceability.
By the end: Interns can assess data availability before beginning development and identify potential data issues early.
Develop maintainable Python scripts for data processing and business analysis.
Topics covered
Python variables, data types, conditions, loops, and functions.
Lists, dictionaries, tuples, and sets.
String manipulation and date/time processing.
File handling and structured data formats.
Exception handling and error reporting.
Functions and reusable modules.
Classes and object-oriented programming fundamentals where appropriate.
Working with packages and virtual environments.
Managing configuration separately from application code.
Logging and diagnostic messages.
Writing readable and maintainable code.
Avoiding hard-coded file paths and repeated logic.
Basic memory and performance considerations.
By the end: Interns can build structured Python scripts that are easier to test, maintain, and reuse in a corporate project.
Retrieve, analyse, and manipulate business data efficiently using SQL.
Topics covered
Relational database fundamentals.
Tables, rows, columns, keys, and relationships.
SELECT, WHERE, ORDER BY, DISTINCT, and LIMIT.
Aggregate functions.
GROUP BY and HAVING.
INNER JOIN, LEFT JOIN, RIGHT JOIN, and self joins.
Subqueries and common table expressions.
CASE statements and conditional aggregation.
Date and string functions.
Window functions, ranking, and running totals.
INSERT, UPDATE, and DELETE fundamentals.
Transactions and basic integrity constraints.
Views and reusable queries.
NULL handling and duplicate detection.
Query execution plans and introductory optimisation.
Parameterised queries and secure database access.
By the end: Interns can write reliable SQL queries for business reporting and understand how to verify the correctness of query results.
Use advanced SQL techniques to solve complex reporting and analytical requirements.
Topics covered
Complex joins across multiple tables.
Multi-level aggregations.
Common table expressions (CTEs).
Window functions such as ROW\_NUMBER, RANK, and DENSE\_RANK.
Running totals and moving averages.
Period-over-period comparisons.
Year-over-year and month-over-month calculations.
Conditional aggregation.
Deduplication using SQL.
Identifying gaps and missing business records.
Cohort and repeat-purchase analysis fundamentals.
Query optimisation and index awareness.
Reusable views for reporting.
Validating query output against source records.
By the end: Interns can handle more complex analytics requirements without relying entirely on manual spreadsheet calculations.
Prepare reliable datasets for analysis, reporting, and dashboard development.
Topics covered
Identifying missing values and duplicates.
Standardising column names and data formats.
Correcting date, currency, and numeric data types.
Handling inconsistent category labels.
Identifying invalid records.
Applying business validation rules.
Detecting outliers for investigation.
Combining and reconciling datasets.
Handling conflicting records.
Maintaining original data and documenting transformations.
Comparing record counts before and after cleaning.
Producing data quality summaries.
Avoiding silent deletion or unsupported modification of records.
By the end: Interns can prepare analysis-ready datasets while preserving traceability and minimising the risk of incorrect business results.
Apply spreadsheet techniques commonly used in corporate reporting and business operations.
Topics covered
Data organisation, sorting, filtering, and tables.
IF and conditional formulas.
SUMIF, SUMIFS, COUNTIF, and COUNTIFS.
XLOOKUP and lookup-based integration.
Date and text functions.
Error handling in formulas.
PivotTables and PivotCharts.
Conditional formatting and data validation.
Data reconciliation and duplicate checks.
Building summary reports.
Creating charts and KPI summaries.
Automating spreadsheet preparation using Python.
Understanding workbook dependencies and formula errors.
By the end: Interns can work with spreadsheet-based business processes and automate repetitive Excel reporting tasks.
Understand and implement ETL workflows that move data from source systems into analytical datasets.
Topics covered
ETL: Extract, Transform, Load.
Introduction to ELT: Extract, Load, Transform.
Reading data from files, databases, and approved APIs.
Combining data from multiple sources.
Source-to-target mapping.
Data transformation and standardisation.
Full-load versus incremental-load concepts.
Handling newly added and updated records.
Idempotent processing and safe reruns.
Duplicate prevention and reconciliation.
Error handling and failed-record management.
Logging pipeline execution.
Understanding data refresh frequency and dependencies.
By the end: Interns can implement basic repeatable ETL workflows and understand the controls needed for reliable data movement.
Design data structures that support consistent, scalable business reporting.
Topics covered
Differences between operational databases and analytical data warehouses.
Relational modelling fundamentals.
Fact tables and dimension tables.
Star schema and snowflake schema.
Identifying grain and business keys.
Designing sales, customer, product, and date dimensions.
Primary and foreign key relationships.
Measures and descriptive attributes.
Slowly changing dimensions fundamentals.
Historical data handling.
Avoiding double counting in joined datasets.
Building reporting-friendly views.
Data lineage and transformation documentation.
By the end: Interns can create understandable analytical data models that support consistent KPIs and efficient dashboard development.
Investigate datasets and identify meaningful trends, relationships, and business opportunities.
Topics covered
Understanding dataset dimensions and distributions.
Descriptive statistics.
Segment-wise and category-wise comparisons.
Trend analysis.
Correlation analysis.
Outlier investigation.
Customer and product performance analysis.
Identifying seasonality and recurring patterns.
Hypothesis formation.
Distinguishing correlation from causation.
Converting findings into business questions.
Documenting analytical assumptions and limitations.
By the end: Interns can conduct structured analysis and communicate findings supported by data rather than assumptions.
Apply statistical concepts to interpret business performance accurately.
Topics covered
Mean, median, mode, variance, and standard deviation.
Percentages and growth rates.
Quartiles and percentiles.
Correlation and covariance.
Sampling and population concepts.
Basic probability.
Confidence intervals.
Hypothesis-testing fundamentals.
Comparing business segments.
Interpreting statistical significance appropriately.
Understanding practical versus statistical significance.
Recognising sampling bias and data limitations.
By the end: Interns can apply basic statistical methods and avoid overstating what the data proves.
Present analytical results clearly using charts and business-friendly visual summaries.
Topics covered
Choosing suitable charts for analytical questions.
Line, bar, scatter, histogram, and box plots.
Heatmaps and correlation visualisations.
Chart labels, titles, legends, and units.
Comparing categories and trends.
Visualising outliers and distributions.
Avoiding misleading scales and chart design.
Building reusable visualisation functions.
Exporting charts for reports and presentations.
Preparing data summaries for non-technical audiences.
By the end: Interns can choose appropriate visualisations and present findings in a way that business stakeholders can understand.
Develop interactive dashboards used for corporate monitoring, performance reporting, and management decision-making.
Topics covered
Introduction to Power BI Desktop.
Importing data from Excel, CSV, and databases.
Data transformation using Power Query.
Creating data relationships.
Understanding fact and dimension tables.
Creating measures and calculated columns.
DAX fundamentals.
KPI cards, charts, tables, slicers, and filters.
Drill-through and drill-down.
Report layout and usability.
Dashboard performance considerations.
Validating totals against source data.
Publishing reports and managing access according to organisational policies.
Understanding refresh configuration and credentials.
By the end: Interns can create interactive dashboards that meet defined business requirements and produce consistent metrics.
Improve the quality, usability, and performance of business intelligence solutions.
Topics covered
Star-schema principles in BI models.
Relationship direction and cardinality.
Measures versus calculated columns.
DAX evaluation context fundamentals.
Time intelligence and date tables.
KPI definitions and reusable measures.
Reducing unnecessary columns and rows.
Avoiding ambiguous relationships and double counting.
Report layout and accessibility considerations.
Filter behaviour and user interaction.
Performance analysis and optimisation fundamentals.
Refresh and publication troubleshooting.
Managing dashboard versions and stakeholder feedback.
By the end: Interns can maintain and improve dashboards beyond basic chart creation.
Establish systematic checks to ensure that analytical outputs are complete, consistent, and accurate.
Topics covered
Data quality dimensions: accuracy, completeness, consistency, validity, uniqueness, and timeliness.
Defining business validation rules.
Checking required columns and data types.
Identifying duplicates and invalid records.
Reconciling source and target record counts.
Comparing totals across source systems and reports.
Validating transformations and joins.
Detecting missing categories and date gaps.
Creating data quality reports.
Documenting defects and corrective actions.
Designing repeatable validation checks.
Establishing data quality acceptance criteria.
By the end: Interns can identify data quality issues early and demonstrate that reports meet defined acceptance criteria.
Test analytics scripts, SQL logic, transformations, and dashboard calculations before delivery.
Topics covered
Unit testing and integration testing fundamentals.
Testing Python functions with pytest.
Testing data transformation logic.
Testing SQL query outputs against expected results.
Testing missing, empty, and invalid inputs.
Regression testing after code changes.
Validating joins and aggregations.
Testing date boundaries and reporting periods.
Testing dashboard filters and calculations.
Preparing test cases and expected results.
Recording defects and retesting fixes.
User acceptance testing (UAT).
Preparing test evidence for client sign-off.
By the end: Interns can demonstrate the reliability of analytical solutions before handing them over to clients.
Retrieve data from approved external systems and services for analytics projects.
Topics covered
API fundamentals and HTTP methods.
Understanding REST API endpoints.
JSON request and response structures.
API authentication and authorisation basics.
Calling APIs from Python.
Query parameters and pagination.
Handling rate limits and timeouts.
Error handling and retry strategies.
Validating API responses.
Converting JSON data into DataFrames.
Managing credentials through approved secret-management methods.
Testing endpoints using Postman.
Respecting access permissions, licensing, and usage policies.
By the end: Interns can integrate external data sources into an analytics workflow while managing authentication, failures, and data quality.
Reduce manual effort by automating recurring data preparation, analysis, and report delivery.
Topics covered
Identifying repetitive business reporting activities.
Creating reusable Python scripts.
Automating file ingestion and transformation.
Scheduling scripts using cron or Windows Task Scheduler.
Understanding workflow scheduling and dependencies.
Creating logs and execution summaries.
Handling failed runs and missing inputs.
Sending approved notifications for successful or failed jobs.
Generating Excel reports and charts.
Managing output folders and file naming.
Avoiding duplicate processing during reruns.
Monitoring runtime and data freshness.
Documenting operating procedures.
By the end: Interns can build reliable scheduled workflows and reduce repeated manual reporting work.
Understand how production analytics workflows are scheduled, monitored, and coordinated when a simple script is no longer sufficient.
Topics covered
Why organisations use workflow orchestration.
Introduction to Apache Airflow.
DAGs, tasks, dependencies, and schedules.
Designing multi-step data workflows.
Managing task failures and retries.
Understanding task logs and execution history.
Handling dependencies between extraction, cleaning, and reporting.
Rerunning failed tasks safely.
Managing configuration and environment variables.
Monitoring workflow status.
Understanding backfills and scheduled data intervals.
Comparing basic OS scheduling with orchestration platforms.
By the end: Interns understand the fundamentals of orchestrated pipelines and when enterprise teams may need them. Scope note:** Apache Airflow is an additional capability for projects that require multi-step scheduling, dependencies, monitoring, and controlled retries. It is not mandatory for every small analytics project.
Understand how corporate analytics solutions use cloud storage, databases, and analytics services.
Topics covered
Cloud computing fundamentals.
Cloud storage and relational databases.
Data lakes and data warehouses.
Uploading and accessing approved datasets.
Cloud permissions and identity management fundamentals.
Secure data transfer and storage.
Understanding managed data services.
Batch processing and scheduled analytics.
Cost awareness and resource usage.
Monitoring, logging, and service availability.
Backup and recovery concepts.
Differences between local and cloud-based workflows.
Example Technology Options
AWS:** Amazon S3, Amazon Athena, Amazon RDS.
Microsoft Azure:** Azure Storage, Azure SQL Database, Azure Data Factory.
Google Cloud:** Cloud Storage, BigQuery, and relevant data integration services.
By the end: Interns understand the basic architecture of cloud analytics solutions and can recognise when cloud services are relevant to a client requirement. Scope note:** Select one cloud platform for hands-on training if the client project requires it. Learning all three platforms in depth is not necessary for an entry-level analytics project.
Apply corporate data handling and security practices throughout the analytics lifecycle.
Topics covered
Data confidentiality and classification.
Personally identifiable information (PII).
Data minimisation and approved data access.
Role-based access control.
Least-privilege permissions.
Credential and secret management.
Secure database connections.
Data masking and anonymisation fundamentals.
Handling client datasets in development environments.
Data retention and deletion requirements.
Data lineage and auditability.
Responsible use of AI tools with corporate data.
Understanding applicable organisational and legal requirements.
Documenting security assumptions and access controls.
By the end: Interns understand that client data must be handled according to contractual, organisational, and applicable legal requirements.
Work effectively within a corporate development team using version control and structured project management.
Topics covered
Git repositories, commits, branches, and merges.
Creating and maintaining project repositories.
Writing clear commit messages.
Pull requests and code reviews.
Managing merge conflicts.
Maintaining README files and setup instructions.
Organising Python scripts, SQL files, notebooks, tests, and documentation.
Tracking tasks and defects.
Using Jira, Azure DevOps, or equivalent project management tools.
Participating in stand-up meetings and progress reviews.
Recording blockers and dependencies.
Managing requirement changes.
Following code review and approval procedures.
By the end: Interns can collaborate on analytics projects using workflows similar to those followed by corporate teams.
Prepare analytics solutions for controlled release, operational use, and client handover.
Topics covered
Preparing code for deployment.
Separating development, testing, and production configurations.
Managing environment variables and secrets.
Creating repeatable setup instructions.
Validating production data connections.
Configuring dashboard refreshes and permissions.
Monitoring pipeline execution and data freshness.
Handling failed jobs and missing datasets.
Maintaining logs and execution records.
Documenting known limitations.
Preparing user guides and technical documentation.
Conducting client demonstrations.
Supporting user acceptance testing.
Capturing feedback and resolving agreed issues.
Preparing release notes and handover checklists.
By the end: Interns understand that project completion includes deployment readiness, operational documentation, stakeholder acceptance, and knowledge transfer—not just writing code.
Build and present a complete analytics solution using industry practices and the tools covered throughout the training.
Topics covered
By the end: Business Requirements Document. Data source inventory and data dictionary. Database schema and SQL scripts. Python data ingestion and cleaning scripts. Data quality validation report. Analytical queries and KPI definitions. Interactive dashboard. Automated Excel report. Test cases and test execution results. Git repository and README. Technical documentation and user guide. Final presentation and client handover checklist.
Program 4 — Leveraging AI for 10× Faster Software Development (29 modules)
29 hands-on modules that take you through the complete development lifecycle — from requirement gathering to deployment — using AI coding tools at every stage. All premium tools (ChatGPT-4, Gemini Advanced, GitHub Copilot, Cursor, and more) are configured free on your laptop. Value: ₹15,000+. You use them throughout the program, not just here — and they stay yours.
Learn to write effective prompts that help AI accelerate data analysis, Python coding, SQL query generation, visualization, statistical analysis, and report preparation.
Topics covered
Fundamentals of prompt engineering for data analytics and data science.
Writing role-based prompts for data analysts, data scientists, and business intelligence developers.
Generating Python code using Pandas and NumPy.
Generating SQL queries for data retrieval, filtering, aggregation, and joins.
Creating prompts for data cleaning, transformation, and exploratory data analysis (EDA).
Generating charts, dashboards, and business reports using AI assistance.
Refining prompts to improve code accuracy and analytical results.
Validating AI-generated code, calculations, and interpretations.
What you'll build
- Create reusable prompts for data cleaning, SQL analysis, and visualization.
- Ask AI to generate Python code that handles missing values and duplicate records.
- Generate SQL queries to calculate sales, revenue, and department-wise performance.
- Use AI to create visualizations from a sample dataset.
- Improve a prompt to obtain more accurate and relevant analytical results.
By the end: Develop reusable prompts for common data analysis tasks. Reduce repetitive coding and query-writing effort. Use AI to accelerate analysis while verifying calculations and conclusions.
Learn how AI can analyze datasets, data dictionaries, Python scripts, SQL queries, dashboards, and project documentation to build project context and reduce manual investigation.
Topics covered
Understanding the structure of data analytics and data science projects.
Using AI to analyze CSV, Excel, SQL, and other supported data sources.
Understanding column names, data types, business definitions, and relationships.
Generating dataset summaries and data dictionaries.
Analyzing existing Python scripts, notebooks, SQL queries, and dashboard calculations.
Identifying data sources, transformation steps, and analytical dependencies.
Documenting data pipelines and analysis workflows.
Creating reusable project-context documents for AI-assisted analysis.
Protecting confidential business data and personally identifiable information.
What you'll build
- Provide a sample sales dataset and ask AI to summarize its structure.
- Generate a data dictionary containing column names, data types, and descriptions.
- Analyze an existing Python notebook to understand its data-cleaning and analysis steps.
- Examine SQL queries and document the business metrics they calculate.
- Generate a project overview containing data sources, transformations, metrics, and outputs.
By the end: Understand unfamiliar datasets and analysis projects more quickly. Reduce time spent manually exploring files, queries, and documentation. Build reusable project context to improve the relevance of AI-generated code and analysis.
Learn how AI can analyze a Software Requirements Specification (SRS) and extract data sources, business questions, analytical requirements, metrics, reporting needs, and data-processing constraints.
Topics covered
Understanding SRS documents for data analytics and data science projects.
Extracting functional and non-functional requirements.
Identifying business problems and analytical objectives.
Extracting requirements for data sources, formats, and collection methods.
Identifying data-cleaning, transformation, and validation requirements.
Mapping business questions to KPIs and analytical metrics.
Identifying dashboard, reporting, and visualization requirements.
Extracting statistical analysis and predictive-modeling requirements when applicable.
Identifying data quality, privacy, access control, and performance requirements.
Detecting ambiguous requirements and generating requirement traceability matrices.
What you'll build
- Analyze an SRS for a sales performance analytics project.
- Extract the required datasets, KPIs, filters, and reports.
- Identify data quality checks and transformation requirements.
- Map business questions to SQL queries, Python analysis, or dashboard features.
- Generate a requirements checklist and identify missing information.
By the end: Convert lengthy SRS documents into actionable analytical requirements. Identify missing data, metric definitions, and reporting needs before implementation. Reduce manual requirement-analysis effort and improve alignment with business objectives.
Learn how AI can convert an SRS into a Product Requirements Document (PRD) that defines analytical features, data requirements, KPIs, reports, dashboards, expected outputs, and acceptance criteria.
Topics covered
Understanding the differences between SRS and PRD.
Extracting business goals and user needs from an SRS.
Defining analytical features and expected business outcomes.
Documenting data sources, fields, formats, and refresh frequency.
Defining KPIs, business rules, calculations, and aggregation logic.
Specifying data-cleaning and transformation requirements.
Defining dashboard layouts, filters, charts, and reporting requirements.
Documenting analytical methods and predictive capabilities when required.
Creating measurable acceptance criteria for reports and analytical outputs.
Defining implementation priorities and project milestones.
Reviewing AI-generated PRDs against the original SRS.
What you'll build
- Convert an SRS for an employee performance analytics system into a PRD.
- Define the required datasets, KPIs, filters, and dashboard features.
- Generate acceptance criteria for department-wise performance reports.
- Create a feature-wise implementation plan covering data preparation, analysis, and visualization.
- Compare the generated PRD with the SRS to identify missing requirements.
By the end: Generate structured PRDs more quickly. Translate business needs into clearly defined analytical deliverables. Reduce repetitive documentation and planning effort. Establish measurable criteria for validating dashboards and analytical results.
Learn how AI can identify unclear business requirements and generate focused questions to clarify client expectations about datasets, KPIs, reporting, analytical methods, and business outcomes.
Topics covered
Identifying ambiguous or incomplete analytical requirements.
Generating client clarification questions from SRS and PRD documents.
Understanding business objectives and decision-making needs.
Clarifying KPI definitions, formulas, units, and reporting periods.
Identifying required data sources and data availability.
Clarifying missing values, duplicate records, and data quality expectations.
Understanding dashboard users, filters, drill-downs, and reporting frequency.
Identifying expectations for forecasting or predictive analysis when applicable.
Clarifying data privacy, access permissions, and retention requirements.
Preparing client meeting agendas and requirement-confirmation documents.
What you'll build
- Analyze a sales analytics requirement and generate clarification questions.
- Prepare questions for a client requesting an employee performance dashboard.
- Identify missing details about KPI formulas, reporting periods, data sources, and filters.
- Generate a client questionnaire and meeting summary template.
- Convert confirmed client answers into updated analytical requirements.
By the end: Identify missing business definitions before analysis begins. Reduce misunderstandings between clients and data teams. Minimize rework caused by unclear KPIs or incomplete data requirements. Improve the relevance and usability of dashboards, reports, and analytical solutions.
Learn how AI can assist in designing data analytics and data science solutions by recommending suitable tools, defining data-processing workflows, planning data storage, and designing analytical and visualization components.
Topics covered
Fundamentals of data analytics and data science solution architecture.
Selecting suitable tools based on project requirements.
Designing data ingestion, cleaning, transformation, analysis, and reporting workflows.
Planning data storage using CSV, Excel, relational databases, or cloud data platforms as appropriate.
Designing SQL-based data retrieval and transformation processes.
Planning Python workflows using Pandas and NumPy.
Selecting suitable visualization tools such as Power BI, Tableau, or Python libraries.
Designing reusable analytical modules and automated reporting workflows.
Planning data validation, logging, error handling, and data quality checks.
Defining access controls, privacy safeguards, and data governance requirements.
Generating architecture diagrams, project structures, and initial code scaffolding using AI.
Evaluating scalability, maintainability, performance, and cost.
What you'll build
- Design a data analytics solution for a sales performance dashboard.
- Ask AI to recommend suitable data storage, processing, and visualization tools.
- Generate a workflow from raw data ingestion to dashboard publication.
- Create a Python project structure for data cleaning, analysis, and reporting.
- Generate an architecture diagram and initial Python or SQL code.
- Review the design for data quality, privacy, performance, and maintainability.
By the end: Design data analytics solutions more efficiently. Accelerate planning for data processing and reporting workflows. Establish clear responsibilities for data ingestion, transformation, analysis, and visualization. Produce architecture documents, implementation plans, and initial code for review.
Understand how AI supports data analysts and accelerates everyday analytical tasks in corporate environments.
Topics covered
Role and responsibilities of a data analyst in an organisation.
Data analytics lifecycle: collection, cleaning, transformation, analysis, visualisation, and reporting.
Traditional data analytics workflows versus AI-assisted workflows.
Using AI to generate code, explain datasets, recommend analytical methods, and debug errors.
Identifying repetitive tasks that can be accelerated through AI.
Understanding the limitations of AI-generated code and insights.
Human validation, analytical accuracy, and responsible AI usage.
How AI Helps Achieve 10× Productivity:
Converts plain-language requirements into initial analytical workflows.
Generates starter code and explains unfamiliar programming concepts.
Helps identify suitable libraries, functions, and analytical approaches.
Reduces time spent searching for syntax and troubleshooting common errors.
By the end: Interns understand where AI can assist at each stage of a data analytics project and where human judgement remains necessary.
Learn to communicate analytical requirements clearly so AI produces useful, accurate, and maintainable code.
Topics covered
Fundamentals of prompt engineering.
Writing clear instructions with business context and expected outputs.
Providing dataset schemas, column names, data types, and sample records.
Asking AI to generate Python functions, SQL queries, and spreadsheet formulas.
Specifying constraints, edge cases, output formats, and performance requirements.
Improving weak prompts through iterative refinement.
Asking AI to explain code before using it.
Requesting test cases, error handling, and alternative solutions.
Breaking complex analytics requirements into smaller tasks.
How AI Helps Achieve 10× Productivity:
Reduces repeated attempts caused by vague instructions.
Generates code that better matches the required output.
Allows analysts to refine existing solutions through conversational instructions.
Produces documentation and test cases alongside the initial implementation.
By the end: Interns can write structured prompts that generate relevant, testable, and reusable analytical code.
Develop the Python knowledge required for analytics while using AI to accelerate implementation and learning.
Topics covered
Variables, data types, operators, and expressions.
Conditional statements and loops.
Lists, tuples, sets, and dictionaries.
Functions, parameters, return values, and reusable code.
String manipulation and date handling.
File handling and exception handling.
Understanding and modifying AI-generated Python code.
How AI Helps Achieve 10× Productivity:
Generates initial code from plain-language instructions.
Explains unfamiliar syntax and programming errors.
Converts repeated code into reusable functions.
Suggests clearer implementations and handles common edge cases.
By the end: Interns can understand, modify, test, and execute AI-generated Python code rather than relying on code they cannot explain.
Learn to manipulate, transform, and analyse datasets efficiently using Python libraries and AI assistance.
Topics covered
Introduction to Pandas Series and DataFrames.
Importing CSV, Excel, and JSON files.
Selecting, filtering, sorting, and grouping records.
Creating, updating, and removing columns.
Handling missing values and duplicate records.
Merging, joining, and concatenating datasets.
Aggregations and group-based calculations.
NumPy arrays and numerical operations.
Vectorised operations and efficient data transformations.
Exporting processed datasets to CSV and Excel.
How AI Helps Achieve 10× Productivity:
Generates Pandas operations from natural-language requirements.
Recommends appropriate functions for filtering and aggregation.
Explains errors involving column names, indexes, and data types.
Suggests vectorised alternatives to inefficient loops.
By the end: Interns can perform common data manipulation tasks quickly and validate that transformations preserve the intended data.
Collect and combine data from multiple sources using AI-assisted scripts.
Topics covered
Importing data from CSV, Excel, JSON, and text files.
Reading data from relational databases.
Understanding APIs and structured API responses.
Loading data from approved business data sources.
Combining datasets with different structures.
Handling inconsistent column names and data formats.
Validating records after importing or merging.
Managing file paths, encoding issues, and import errors.
Understanding pagination, API limits, and data access permissions.
How AI Helps Achieve 10× Productivity:
Generates import scripts for common file formats.
Creates starter code for database connections and approved API integrations.
Helps troubleshoot parsing and data-type errors.
Suggests repeatable workflows for combining multiple datasets.
By the end: Interns can create repeatable data ingestion workflows and verify that the combined dataset is complete and consistent.
Use AI-assisted Python to identify and correct common data quality issues.
Topics covered
Identifying missing, duplicate, and inconsistent records.
Standardising text, dates, currencies, and category names.
Detecting invalid values and unexpected data types.
Applying validation rules to business datasets.
Identifying outliers for further investigation.
Creating reusable data-cleaning functions.
Generating data quality summaries.
Logging errors and recording cleaning operations.
Comparing dataset quality before and after cleaning.
Preserving original data and documenting changes.
How AI Helps Achieve 10× Productivity:
Generates reusable cleaning functions from business rules.
Suggests validation checks for common data quality problems.
Automates repetitive formatting and standardisation tasks.
Creates scripts that report detected issues before corrective changes are applied.
By the end: Interns can automate routine cleaning while retaining traceability and avoiding unsupported changes to business data.
Explore datasets systematically and use AI to accelerate the discovery of useful patterns.
Topics covered
Understanding dataset dimensions and structure.
Descriptive statistics and summary tables.
Analysing distributions, averages, medians, and variability.
Identifying relationships between numerical variables.
Comparing business segments and categories.
Analysing trends across time periods.
Detecting unusual observations and potential anomalies.
Generating analytical questions from initial findings.
Distinguishing correlation from causation.
Documenting findings with supporting evidence.
How AI Helps Achieve 10× Productivity:
Generates code for descriptive statistics and grouped summaries.
Suggests useful exploratory questions based on the dataset schema.
Helps interpret statistical outputs in business language.
Produces initial analytical summaries that analysts can verify.
By the end: Interns can conduct structured exploratory analysis and convert verified observations into meaningful business findings.
Use AI to write, understand, debug, and improve SQL queries for business analytics.
Topics covered
SQL fundamentals: SELECT, WHERE, ORDER BY, and DISTINCT.
Aggregate functions: COUNT, SUM, AVG, MIN, and MAX.
GROUP BY and HAVING.
INNER JOIN, LEFT JOIN, RIGHT JOIN, and self joins.
Subqueries and common table expressions.
CASE statements and conditional aggregation.
Date functions and string functions.
Window functions, ranking, and running totals.
Query debugging and handling unexpected results.
Understanding execution plans and basic query optimisation.
Parameterised queries and safe database access.
How AI Helps Achieve 10× Productivity:
Converts business questions into initial SQL queries.
Generates joins and aggregations from table schemas.
Explains complex queries and identifies likely syntax errors.
Suggests alternative query structures and optimisation opportunities.
By the end: Interns can generate and validate analytical SQL queries, understand their logic, and recognise when an apparently correct query returns incorrect results.
Use AI to accelerate spreadsheet-based analysis commonly performed in corporate departments.
Topics covered
Data organisation, sorting, and filtering.
Excel tables and structured references.
SUMIF, SUMIFS, COUNTIF, COUNTIFS, and conditional calculations.
IF, nested conditions, and error-handling formulas.
XLOOKUP and lookup-based data integration.
Date and text functions.
PivotTables and PivotCharts.
Conditional formatting and data validation.
Identifying spreadsheet errors and inconsistent calculations.
Automating Excel reports with Python and Pandas.
Exporting formatted analytical results.
How AI Helps Achieve 10× Productivity:
Generates formulas from plain-language requirements.
Explains why a formula produces an unexpected result.
Suggests suitable lookup and aggregation functions.
Generates Python scripts to reduce repetitive spreadsheet processing.
By the end: Interns can combine spreadsheet analysis with AI-assisted formula generation and Python automation.
Apply basic statistical methods to interpret business data and support evidence-based decisions.
Topics covered
Mean, median, mode, range, variance, and standard deviation.
Percentages, growth rates, and comparative analysis.
Percentiles and quartiles.
Correlation and covariance.
Sampling and population concepts.
Basic probability concepts.
Confidence intervals and hypothesis-testing fundamentals.
Comparing groups and business segments.
Identifying statistically unusual observations.
Interpreting statistical results without overstating conclusions.
How AI Helps Achieve 10× Productivity:
Generates Python code for common statistical calculations.
Explains statistical concepts in accessible language.
Helps select candidate methods based on the analytical question.
Drafts interpretations that analysts can check against the actual results.
By the end: Interns can perform basic statistical analysis, understand its limitations, and distinguish numerical differences from adequately supported conclusions.
Generate informative charts and visual summaries using AI-assisted coding.
Topics covered
Principles of effective data visualisation.
Line charts for trends over time.
Bar charts for category comparisons.
Histograms and box plots for distributions.
Scatter plots for relationships.
Heatmaps for correlation and comparative patterns.
Matplotlib and Seaborn fundamentals.
Interactive visualisations with Plotly.
Chart titles, labels, legends, and appropriate scales.
Choosing chart types based on analytical questions.
Avoiding misleading visual representations.
Saving charts for presentations and reports.
How AI Helps Achieve 10× Productivity:
Generates plotting code from descriptions of the required chart.
Suggests visualisation types for different analytical questions.
Helps troubleshoot plotting errors and formatting issues.
Creates reusable functions for repeated business charts.
By the end: Interns can produce accurate, readable visualisations and ensure that chart design supports rather than distorts the underlying data.
Build interactive business dashboards while using AI to accelerate data preparation, calculations, and documentation.
Topics covered
Introduction to Power BI Desktop.
Importing data from Excel, CSV, and databases.
Data transformation using Power Query.
Relationships between tables and basic data modelling.
Creating measures and calculated columns.
DAX fundamentals, including SUM, COUNT, CALCULATE, and time-based calculations.
Building KPI cards, charts, tables, and slicers.
Creating interactive filters and drill-down views.
Designing dashboards for different business stakeholders.
Validating dashboard totals against source data.
Refreshing datasets and documenting data sources.
Understanding organisation-specific publishing and access permissions.
How AI Helps Achieve 10× Productivity:
Drafts DAX measures from business definitions.
Explains formula errors and suggests corrections.
Helps plan dashboard layouts based on stakeholder questions.
Generates documentation for measures and report logic.
By the end: Interns can build an interactive dashboard and validate its KPIs, relationships, and calculations before presenting it to stakeholders.
Translate business requirements into measurable KPIs and analytical workflows.
Topics covered
Understanding business goals and stakeholder requirements.
Defining measurable KPIs and their calculation rules.
Revenue, profit margin, growth rate, and average order value.
Customer retention, repeat purchases, and churn indicators.
Employee productivity and operational efficiency metrics.
Conversion rates and campaign performance.
Establishing baselines and comparison periods.
Selecting suitable dimensions and filters.
Documenting assumptions and metric definitions.
Distinguishing descriptive, diagnostic, and predictive questions.
How AI Helps Achieve 10× Productivity:
Converts written business requirements into candidate KPIs.
Suggests formulas and SQL queries for calculating metrics.
Drafts metric definitions and acceptance criteria.
Helps identify missing information that must be clarified with stakeholders.
By the end: Interns can convert business questions into clear, measurable analytical requirements rather than generating code without understanding the problem.
Convert analytical results into clear reports and actionable business communication.
Topics covered
Structuring an analytical report.
Writing executive summaries.
Identifying key findings and supporting evidence.
Explaining trends, comparisons, and anomalies.
Creating recommendations linked to observed results.
Generating report text from verified statistics.
Preparing charts and tables for business presentations.
Tailoring reports to technical and non-technical audiences.
Distinguishing findings, hypotheses, and recommendations.
Citing data sources, assumptions, and limitations.
Avoiding unsupported conclusions and fabricated explanations.
How AI Helps Achieve 10× Productivity:
Drafts report summaries from verified metrics.
Converts technical observations into business-friendly language.
Generates initial presentation outlines and explanatory text.
Helps standardise recurring reports and executive summaries.
By the end: Interns can produce concise, evidence-based reports that help stakeholders understand what the data shows and what actions may be worth considering.
Ensure that AI-generated analytics code is reliable, maintainable, and suitable for business use.
Topics covered
Common Python and SQL errors.
Reading error messages and stack traces.
Reproducing and isolating bugs.
Using AI to identify possible causes of errors.
Writing unit tests for analytical functions.
Testing empty datasets, missing values, and invalid inputs.
Validating SQL outputs and aggregation logic.
Comparing results with manually calculated samples.
Reviewing code for readability and maintainability.
Detecting inefficient operations and unnecessary complexity.
Handling exceptions and recording useful logs.
Understanding why generated code must be reviewed before execution.
How AI Helps Achieve 10× Productivity:
Explains error messages and proposes candidate fixes.
Generates initial unit tests and edge-case scenarios.
Reviews functions for potential defects and readability issues.
Suggests refactoring and performance improvements.
By the end: Interns can validate AI-generated code rather than assuming that code is correct simply because it executes successfully.
Automate recurring analytics tasks to reduce manual effort in corporate workflows.
Topics covered
Identifying repetitive reporting activities.
Designing reusable Python scripts.
Reading and combining regularly received files.
Automating cleaning and transformation.
Running SQL queries for recurring reports.
Generating Excel workbooks and charts.
Creating PDF or HTML summaries where required.
Adding logging, error handling, and validation checks.
Handling missing or delayed input files.
Scheduling scripts through approved operating-system or workflow tools.
Monitoring execution and notifying responsible users of failures.
Measuring manual versus automated processing time.
How AI Helps Achieve 10× Productivity:
Generates initial end-to-end automation scripts.
Converts repeated manual steps into reusable functions.
Helps add validation, logging, and error handling.
Drafts scheduling instructions and troubleshooting procedures.
By the end: Interns can create a repeatable reporting process that reduces manual work while retaining controls for failures and incorrect inputs.
Identify unusual business patterns and investigate them using analytical techniques and AI-assisted coding.
Topics covered
Understanding anomalies and outliers.
Rule-based anomaly detection.
Identifying unusual transaction amounts and sudden changes.
Moving averages and percentage-change analysis.
Comparing actual values with historical baselines.
Detecting seasonal and recurring patterns.
Using interquartile range and standard deviation for initial screening.
Visualising unusual observations.
Investigating possible data quality issues.
Distinguishing genuine business events from data errors.
Documenting alert thresholds and limitations.
How AI Helps Achieve 10× Productivity:
Generates code for initial anomaly-detection methods.
Suggests candidate thresholds and validation checks.
Produces summaries of unusual observations.
Helps create reusable functions for monitoring recurring metrics.
By the end: Interns can identify potential anomalies and produce evidence for further investigation without treating every unusual value as an error or fraud.
Understand introductory predictive analytics and use AI to accelerate the implementation of simple forecasting or prediction workflows.
Topics covered
Difference between descriptive, diagnostic, and predictive analytics.
Understanding features, targets, and training datasets.
Data preparation for predictive analysis.
Train-test splitting.
Introduction to linear regression and basic classification.
Forecasting fundamentals for time-series data.
Evaluation metrics such as MAE and RMSE for suitable regression tasks.
Classification evaluation using precision, recall, and confusion matrices.
Avoiding data leakage.
Understanding overfitting and underfitting.
Interpreting model limitations and uncertainty.
Separating model outputs from business decisions.
How AI Helps Achieve 10× Productivity:
Generates starter code for simple predictive workflows.
Explains model evaluation metrics.
Helps identify missing preprocessing steps.
Creates candidate experiments and test scenarios.
By the end: Interns can implement and evaluate a basic predictive analytics workflow without confusing a prediction with a guaranteed outcome.
Use AI to accelerate team collaboration, version control, and documentation in corporate analytics projects.
Topics covered
Git fundamentals: repositories, commits, branches, and merges.
Using GitHub for team collaboration.
Organising analytics project folders.
Maintaining reusable scripts and configuration files.
Writing meaningful commit messages.
Reviewing changes before merging.
Creating README files and setup instructions.
Documenting datasets, columns, metrics, and assumptions.
Using AI to explain unfamiliar code in a shared repository.
Reviewing AI-generated documentation for accuracy.
Managing configuration and environment variables.
Avoiding the inclusion of secrets or confidential datasets in repositories.
How AI Helps Achieve 10× Productivity:
Drafts project documentation and setup instructions.
Explains existing code to help new team members understand it.
Suggests clearer function names and code organisation.
Creates initial test documentation and change summaries.
By the end: Interns can maintain analytics work in a structured repository and collaborate without losing track of changes or exposing confidential information.
Use AI tools responsibly while protecting corporate information and ensuring analytical reliability.
Topics covered
Corporate policies for approved AI tools.
Risks of submitting confidential data to external AI services.
Personally identifiable information and data minimisation.
Using synthetic or anonymised data for demonstrations.
Protecting credentials, API keys, and database passwords.
Applying least-privilege access.
Reviewing AI-generated code before execution.
Risks associated with unsafe file operations and database modifications.
Avoiding unauthorised access and data extraction.
Verifying calculations, generated explanations, and recommendations.
Documenting assumptions, limitations, and data lineage.
Understanding bias and fairness considerations where relevant.
Human approval for consequential business decisions.
How AI Helps Achieve 10× Productivity:
Drafts validation checklists and security documentation.
Helps explain potential code risks.
Suggests safer alternatives for handling credentials and input data.
Supports the creation of repeatable review procedures.
By the end: Interns understand that productivity gains must not compromise data privacy, access controls, security, or the integrity of business information.
Combine Python, SQL, data cleaning, visualisation, and AI-assisted coding into a complete analytics application.
Topics covered
Converting a business problem into technical requirements.
Designing the application workflow and project structure.
Connecting to a database or importing business datasets.
Cleaning and validating input data.
Implementing reusable analytical functions.
Generating SQL queries with AI assistance.
Calculating KPIs and grouped summaries.
Creating charts and dashboard components.
Generating downloadable reports.
Adding input validation and error handling.
Testing calculations against reference results.
Documenting setup and execution instructions.
Reviewing the completed application for performance, security, and usability.
How AI Helps Achieve 10× Productivity:
Breaks the project into smaller implementation tasks.
Generates starter code for each component.
Helps debug integration problems across modules.
Drafts documentation and tests alongside implementation.
By the end: Interns can develop a complete analytics workflow using AI as a coding assistant while retaining responsibility for the code and results.
Evaluate whether AI-assisted development actually reduces work time without lowering analytical quality.
Topics covered
Establishing a baseline for manual task completion.
Measuring the time required for coding, debugging, testing, and documentation.
Comparing manual and AI-assisted workflows.
Tracking the number of corrections needed in generated code.
Measuring test coverage and analytical accuracy.
Evaluating output quality and maintainability.
Identifying tasks where AI provides limited benefit.
Reusing effective prompts and approved code templates.
Improving workflows through iterative measurement.
Calculating time saved and productivity improvement.
Understanding the difference between faster code generation and faster delivery of a correct solution.
How AI Helps Achieve 10× Productivity:
Reduces initial implementation and documentation effort.
Accelerates troubleshooting and repetitive coding.
Helps standardise recurring workflows.
Supports continuous improvement through reusable prompts and templates.
By the end: Interns can measure actual productivity improvements instead of assuming that every AI-assisted task is automatically ten times faster. Important Productivity Principle:** A 10× improvement is an aspirational target, not a guaranteed outcome. Results depend on task complexity, data quality, tool access, verification requirements, and the developer's experience.
Build an end-to-end business analytics solution that demonstrates how AI-assisted development can accelerate corporate reporting and decision support.
Topics covered
By the end: Project source code. Data ingestion and cleaning scripts. SQL queries and database schema, where applicable. KPI calculations and analytical outputs. Dashboard or visualisation components. Automated Excel report. Test cases and validation results. Project README and setup instructions. Documentation of AI-assisted development tasks. A comparison of manual and AI-assisted task completion time.
Live client projects — 5+ across the program
Across the Career-Ready program you work on all real client projects, contributing to each at a level appropriate to your current skills. By the final project, you're working at near-professional capacity.
Live client project
TalentBeacon — Intelligent Workforce Analytics Platform
Headcount analytics, attrition risk scoring, compensation equity analysis, and hiring velocity dashboards.
PythonPandasPower BISQLStreamlit
Live client project
Employee Attendance and Workforce Analytics System
Attendance anomaly detection, shift compliance tracking, absenteeism trend analysis, and automated summary reports.
PythonNumPyPandasMatplotlibPostgreSQL
Live client project
MailSentry Insight — Email Intelligence & Classification Platform
Inbound support ticket triage, customer sentiment tracking, intent extraction, and automated routing analytics.
PythonScikit-learnNLTKSeabornSQLite
Live client project
Project Cost and Budget Variance Analytics
Milestone expenditure tracking, burn-rate forecasting, cost-overrun alerts, and executive financial dashboards.
PythonPandasPower BIExcel AdvancedSQL
Live client project
Intern Performance Analytics Dashboard
Multi-dimensional skill progression, evaluation score distribution, assessment benchmarks, and cohort reporting.
PythonTableauPower BISQLiteSeaborn
Live client project
Software Defect Analytics Platform
Defect density measurement, root cause clustering, mean-time-to-resolution (MTTR) analytics, and QA sprint dashboards.
PythonPandasStatsmodelsPlotlyMySQL
Live client project
Voice of Interns — Training Feedback Analytics Platform
Survey sentiment mining, feedback NPS tracking, curriculum topic effectiveness scores, and automated insights generation.
PythonNLPVADERStreamlitPostgreSQL
Everything included in your enrollment
✓
400+ live training hours — 5 hours daily, every weekday✓
100+ Immersive VR sessions + Unlimited VR revision for up to 2 full years (exclusive flagship privilege)✓
All 4 programs: internship + career course + industry projects + AI development✓
AI tools configured on your laptop (ChatGPT-4, Gemini, Copilot, Cursor — value ₹15,000+)✓
Internship + course certificate from WhiteandBox.com✓
Offer letter — verified, 0 background check issues✓
Letter of Recommendation (performance-based)✓
LinkedIn profile building, portfolio guidance, and soft skills training✓
Digital certificate with QR code verification✓
Free access to client summits and webinars (with certificate)✓
1:1 daily mentorship from senior developers✓
Remote, hybrid, or in-office at BangaloreCareer-Ready Data Science & Analytics Flagship Program
400+ live hours · 4 complete programs · 70+ modules · Full placement support. The program serious developers choose when they want job outcomes, not just a certificate.
₹38,000
₹17,850
50% off · limited seats
You save ₹20,150 at current offer — includes AI tools worth ₹15,000+ configured free on your laptop