← All programs
Industry-AlignedEnrolling Now100% Placement Assurance

PG Program in Data Analytics with Generative AI

Learn data analysis, visualisation and reporting with the AI-assisted workflows modern analyst roles now expect, and finish with capstone projects built on real company data.

Duration6 Months
ModeLive Online
Learners10,000+
Enroll Now - ₹1,35,000Download Brochure
Next BatchJuly 18, 2026
Duration6 Months · Live Online
Industry Projects30+
EligibilityFreshers, Graduates, Experienced

What you’ll gain from this program

This PG Program in Data Analytics equips you with Excel, SQL, Python, Power BI and Tableau skills, and the analytical rigour to clean, question and present data, through real-world projects and expert guidance.

Excel & Advanced Spreadsheet Analysis
SQL Querying & Reporting
Python & pandas for Analysis
Power BI Dashboard Development
Tableau Visual Analytics
200+ Hrs Analytics-Focused Content
300+ Hrs Live Practical Sessions
Working Data Analysts as Mentors
Data Analytics Job Placement Support
Round-the-Clock Assistance
Data Analytics Curriculum Track
Skill Evaluation Tests
Learning Performance Analytics Dashboard
Professional Community Access
Self-Paced Learning Options
Global Alumni Community
Mock Interview Training
Data Analytics Certificates
Advanced LMS Platform
Live Data Analytics Case Studies
ExcelSQLPythonPower BITableau

PG Program in Data Analytics with Generative AI curriculum

A comprehensive curriculum designed by industry experts combining Excel, SQL, Python, Power BI, Tableau and statistics. Master data cleaning, exploratory analysis, visualisation and reporting with hands-on projects on real datasets.

📚 21 modules · learning roadmap

01Introduction to Data Analytics
  • What a data analyst actually does day to day
  • The analytics lifecycle from raw data to decision
  • Types of data and where it comes from
  • The modern analytics toolchain
  • Analyst career paths and specialisations
  • Industry trends and outlook
02Excel for Data Analysis
  • Structuring data for analysis
  • Essential formulas and functions
  • Lookups: VLOOKUP, XLOOKUP, INDEX-MATCH
  • PivotTables for fast aggregation
  • Charts and conditional formatting
  • First analysis report in Excel
03Advanced Excel & Power Query
  • Power Query for repeatable transformations
  • Dynamic arrays and modern functions
  • Data validation and error handling
  • Automating recurring analysis
  • Interactive Excel dashboards
  • Advanced Excel project
04Statistics for Analysts
  • Measures of centre and spread
  • Distributions and the normal curve
  • Sampling and sampling error
  • Confidence intervals in plain language
  • Hypothesis testing and p-values
  • Statistical reasoning case study
05Data Cleaning & Wrangling
  • Profiling and auditing a raw dataset
  • Missing data strategies
  • Duplicates, outliers and bad records
  • Type, format and category standardisation
  • Merging messy sources together
  • Cleaning pipeline documentation
06SQL Fundamentals
  • How relational databases store data
  • SELECT, filtering and sorting
  • Aggregation with GROUP BY and HAVING
  • All four join types in practice
  • Subqueries and CTEs
  • Query set for a reporting requirement
07Advanced SQL for Analytics
  • Window functions and running totals
  • Ranking, percentiles and deciles
  • Cohort and retention analysis in SQL
  • Time-series aggregation patterns
  • Query optimisation basics
  • Advanced SQL analytics project
08Python Foundations for Analysts
  • Python syntax, types and control flow
  • Working in Jupyter notebooks
  • Reading data from files, APIs and databases
  • NumPy arrays and vectorised thinking
  • Writing reusable analysis functions
  • Python fundamentals exercise set
09Data Analysis with pandas
  • DataFrames, indexing and selection
  • Filtering, grouping and aggregating
  • Merging, joining and reshaping
  • Time series handling in pandas
  • Cleaning workflows in code
  • pandas analysis project
10Exploratory Data Analysis
  • Framing the question before touching data
  • Distribution and relationship exploration
  • Segmenting to find the real signal
  • Explaining a metric movement
  • Common analytical traps and biases
  • Complete EDA case study
11Data Visualisation Principles
  • Matching chart type to the question
  • Encoding, scale and axis integrity
  • Colour use and accessibility
  • Reducing clutter and chartjunk
  • Annotating charts to carry the insight
  • Visualisation critique and redesign exercise
12Power BI Fundamentals
  • Connecting and shaping data sources
  • Building the data model
  • Core visuals and page design
  • Slicers, filters and drill-through
  • Publishing and sharing
  • Power BI report build
13DAX & Advanced Power BI
  • Measures versus calculated columns
  • Filter and row context explained
  • Time intelligence calculations
  • CALCULATE and context modification
  • Model and report performance
  • Advanced Power BI dashboard project
14Tableau for Data Analysts
  • Tableau data connections and extracts
  • Calculated fields and table calculations
  • Charts, maps and dual-axis visuals
  • Interactive dashboards and actions
  • Level of detail expressions
  • Tableau dashboard project
15Dashboards & Reporting Workflows
  • Designing a report for its audience
  • Self-serve versus curated reporting
  • Refresh schedules and data freshness
  • Report versioning and change control
  • Handling stakeholder change requests
  • End-to-end reporting workflow project
16Data Storytelling & Communication
  • Structuring an analysis narrative
  • Leading with the finding, not the method
  • Writing insight summaries executives read
  • Presenting to non-technical stakeholders
  • Defending an analysis under questioning
  • Analytics presentation project
17Business Metrics & Domain Context
  • Defining metrics that mean something
  • Funnel, retention and growth metrics
  • Revenue, cost and margin basics
  • Domain context: product, marketing, operations
  • Metric definitions and a shared dictionary
  • Metric design exercise
18Predictive Analysis Foundations
  • When a model beats a simple analysis
  • Linear and logistic regression basics
  • Train/test splits and overfitting
  • Evaluating model results honestly
  • Forecasting fundamentals
  • Introductory predictive project
19AI-Assisted Analytics Workflows
  • Where GenAI speeds up analyst work
  • Prompting for SQL, pandas and DAX
  • AI-assisted cleaning and documentation
  • Drafting insight summaries with AI
  • Verifying output and avoiding hallucinated numbers
  • AI-assisted workflow project
20Data Analytics Capstone Project
  • End-to-end project on a real company dataset
  • Question framing and data acquisition
  • Cleaning, analysis and visualisation
  • Insight write-up with recommendations
  • Stakeholder presentation and defence
  • Portfolio packaging of the capstone
21Analyst Career Readiness & Interview Prep
  • Data analyst resume creation and optimization
  • SQL and Excel interview preparation
  • Take-home case and dashboard tests
  • Behavioral interview training
  • LinkedIn networking for data professionals
  • 1 year Placement Support for Top Fellows

Internship program

  • Real-world Data Analytics Projects
  • End-to-End Data Cleaning & Analysis
  • Dashboard & Report Development
  • Insight Presentations to Stakeholders
  • Metric Investigation & Root-Cause Analysis
  • Industry Mentorship by Working Data Analysts

Soft skills program

  • Insight Presentation & Reporting Skills
  • Data Storytelling & Communication
  • Analytical Questioning & Problem Framing
  • Cross-Functional Collaboration
  • Attention to Detail & Data Integrity
  • Professional Networking & Relationship Building

Ready to start PG Program in Data Analytics with Generative AI?

Reserve your seat and a career expert will confirm your batch, fee and EMI options.

Apply NowCompare programs