In-Person
Tiruvallur campus: daily sessions, live coding alongside trainer, peer review of analysis. Recommended especially for SQL and Python modules.
// FLAGSHIP PROGRAM
AI-Powered Data Science and Analytics
Turn raw data into decisions — and use AI to do it at a speed no manual analyst can match.
3 months · Structured modules + supervised project
In-Person
Online
Hybrid
Analytics you can act on, faster with AI
Data is everywhere. Most organisations collect more of it than they can make sense of. The people who can look at a dataset, ask the right questions, extract the right answers, and present them in a way a decision-maker can act on — those people are in short supply and high demand.
This program teaches you to be that person. Over three months you move through the complete analytics stack — Excel and SQL foundations through Python-based data analysis, statistical reasoning, machine learning for analysts, and Power BI dashboarding — with AI tools woven throughout to compress the time between question and answer.
You do not need to be an engineer. You do not need to write production code. You need to understand data, communicate findings clearly, and use the best available tools — human and AI — to do it faster and better than everyone else in the room.
This is not the AI and ML Engineering course. You will not be training deep learning models or building LLM APIs. You will do the work that matters most in analytics roles: taking messy real-world data, making sense of it, and turning it into something a business can act on.
Built for data thinkers and decision makers
This program is designed for:
- Commerce graduates — B.Com, BBA, MBA students and freshers — who want to move into analytics, finance operations, or business intelligence roles
- Science graduates — B.Sc. Mathematics, Statistics, Physics, Chemistry — who want to apply quantitative background to business problems
- Non-CS engineering graduates who want to pivot toward data and analytics rather than software development
- Working professionals in finance, operations, marketing, or HR who want to level up data skills significantly
- Anyone who lives in spreadsheets and wants to work faster, produce better analysis, and stop doing it manually
This program works particularly well alongside:
- The AI Workflow Design and Automation course — automating the reporting pipelines you build here
- The AI and ML Engineering course — a foundation if you want to go deeper into ML
No programming required to start
Hard prerequisites: Basic spreadsheet familiarity — open Excel or Google Sheets and done something in it. Basic comfort with numbers and percentages.
Soft prerequisites: Genuine curiosity about why things happen. The analytical mindset matters more than technical background. Experience in any business role where you have worked with data, even informally, helps.
Setup required before day one: Microsoft Excel or Google Sheets (both work), Python via Anaconda (free), VS Code or Jupyter Notebook, Power BI Desktop (free), Git and a GitHub account. Everything used is free.
From data foundations to AI-augmented reporting
Establishes the thinking framework before any tool is introduced. You define questions, understand data quality, and write reproducible analysis briefs.
Topics
- Analytics value chain: data → analysis → insight → decision → outcome
- Descriptive, diagnostic, predictive, prescriptive analytics; asking the right question
- Correlation vs causation
- Quantitative vs qualitative; structured vs unstructured; data quality dimensions
- Where data comes from in organisations; reading/writing data dictionaries
- Analytics workflow and documentation discipline; version control for analysis
Hands-on
- Document a messy dataset: write a data dictionary, assess quality, decide what questions it can answer, and define what must be cleaned.
Build spreadsheet speed and correctness: from lookup/logical/aggregation functions to pivot tables, Power Query, dashboards, and AI-assisted debugging.
Topics
- Lookup functions (VLOOKUP/HLOOKUP/INDEX-MATCH/XLOOKUP)
- Logical functions (IF/IFS/AND/OR/NOT)
- Aggregation + conditional aggregation (SUMIF/SUMIFS/COUNTIF/COUNTIFS/AVERAGEIF)
- Text and date/time functions for cleaning and reporting
- Dynamic arrays and modern formula tools (SUMPRODUCT, UNIQUE, SORT, FILTER)
- Named ranges, data validation, conditional formatting
- Pivot tables + charts, Power Query, data model basics
- Dashboard design principles for executives
Hands-on
- Build a complete sales analytics dashboard in Excel (Power Query → clean → pivot → executive metrics), then rebuild with AI assistance and compare time + quality.
AI integration
- Explain formulas, suggest/pinpoint errors, recommend pivot structures and chart types — with the discipline to explain every AI suggestion.
SQL at analyst depth: writing queries that answer business questions, including joins, window functions, and time-series logic — plus AI-assisted query explanation and documentation.
Topics
- Relational model, SELECT/WHERE/NULL handling, aggregates, CASE
- Joins: INNER, LEFT, RIGHT/FULL, self joins, multi-join
- Subqueries and CTEs
- Window functions: ROW_NUMBER/RANK/LAG/LEAD, running totals, partitioning
- Date/time and cohort queries
- Query planning intuition and common analyst patterns
AI integration
- Use AI to explain results, suggest query approaches, debug syntax — and verify every AI-suggested query against manually-checked samples.
Hands-on
- Series of SQL challenges on e-commerce data: retention/cohort, sales trends, ranking, funnel drop-off, all with interpretation statements.
Turn messy data into analysis-ready pipelines: Pandas for cleaning/EDA, visualisation, reusable scripts and parameterised notebooks — plus AI-assisted analysis with verification.
Topics
- Why Python for analysis; Jupyter workflows
- Pandas import/select/filter, missing values, type management
- Vectorised string cleaning, merge/join, groupby/aggregation
- Reshaping (pivot_table, melt/stack/unstack)
- Matplotlib + Seaborn + Plotly; visual design principles
- Reusable analysis scripts and scheduled report generation
- AI integration: explain errors, suggest operations and visuals, review code on small samples before running.
Hands-on
- End-to-end analysis project: import → clean → EDA → segmentation → visualisation → written brief deliverable.
Statistical reasoning that prevents confident wrong conclusions: descriptive stats, inferential basics, confidence intervals, hypothesis testing, and uncertainty communication.
Topics
- Descriptive statistics: centre, spread, distributions, outlier decision framework
- Sampling + confidence intervals; interpreting 95% confidence correctly
- Hypothesis testing, p-values misuse, Type I/II errors
- Analyst-used tests: A/B testing, t-tests, chi-square, correlation and the correlation-causation problem
- Power, multiple comparisons, communicating uncertainty to stakeholders
Hands-on
- Analyse three real A/B tests: identify statistical significance, confidence intervals, sample adequacy, and find the flawed test design.
Use ML as an analytical tool. You learn conceptual understanding, practical models with scikit-learn, and how to interpret predictions for business decision making.
Topics
- ML adds: prediction for what will happen; when ML is appropriate vs pivot tables
- Regression: linear, multiple regression, logistic regression workflow and metrics
- Classification: decision trees, random forests; metric interpretation
- Clustering: K-Means; customer segmentation interpretation
- Feature importance, limitations, fairness and bias responsibility
Hands-on
- Customer churn project: clean data → run logistic regression + random forest → compare → identify drivers → quantify business value → write deployment recommendation.
Build interview-worthy portfolio dashboards: Power BI Desktop + DAX + data modelling + publishing to service with scheduled refresh and RLS.
Topics
- Power BI fundamentals: desktop views, data view and model view
- Connecting data: Excel/CSV/SQL/web sources/APIs
- Power Query and data model: star schema, relationships
- DAX: calculated columns vs measures, context awareness, time intelligence, CALCULATE
- Report design: visuals, drill-through/tooltips, bookmarks and buttons
- Publishing: Power BI Service, scheduled refresh, row-level security
- AI integration: Copilot/DAX generation where available with understanding and verification.
Hands-on
- Build and publish a full BI dashboard (retail business analytics) to Power BI Service with scheduled refresh.
Use AI to amplify analytics: narrative generation, cleaning, interpretation, and reporting workflows — with a strict verification obligation.
Topics
- What AI does well vs poorly in analytics; hallucinated stats and plausible wrong narratives
- Verification obligation: check formulas/queries/claims against data
- Practical AI tools: Claude/ChatGPT, Copilot for Python, AI for SQL/Excel/Power BI
- Building narrative pipelines and report generation workflows
- Responsible AI: privacy, PII handling, India DPDP Act basics, attribution
Hands-on
- Build an AI-assisted monthly performance report pipeline using Python + narrative generation, generate charts, and compare to manual reporting (quality + time).
End-to-end analytics portfolio piece: raw data → dashboard → written recommendations → stakeholder presentation.
Format
- Project scoped with your trainer in Module 8. You choose a domain and a business question.
- Deliverables: data acquisition/quality, cleaning + documented Pandas notebook, EDA with visual insights, stats/ML with interpretation, and a Power BI dashboard plus AI-assisted narrative.
Assessment
- 20-minute stakeholder presentation: findings + recommendations only (no full methodology walkthrough unless asked).
Example project types
- Retail/customer behaviour, education performance, finance/BFSI risk analysis, logistics delivery performance, or public data impact analysis for Tamil Nadu.
A portfolio-grade analytics system
You do real analysis and publish real dashboards. Then you present the business decision you would drive.
The project must include a documented problem statement, data acquisition + quality assessment, cleaning and transformation in a Pandas notebook, EDA with at least six visualisations and insight statements, at least one statistical test or ML model with interpretation, and a Power BI dashboard with interactive views.
You also produce an AI-assisted narrative report summarising findings and recommendations, plus a stakeholder presentation focused on outcomes and confidence — not methodology walkthrough unless asked.
Hands-on deliverables
- Power BI dashboard published to cloud
- Python analysis notebook with GitHub story
- Written recommendations you can defend +
- Stakeholder presentation and confidence answers
The analytics stack you will use
Spreadsheets: Microsoft Excel, Google Sheets (Power Query, pivot tables, dynamic arrays, dashboards)
Databases and SQL: MySQL, MySQL Workbench, SQLite (for lightweight practice)
Python stack: Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn, SQLAlchemy
Business intelligence: Power BI Desktop, Power BI Service, DAX
AI tools throughout: Claude, ChatGPT, GitHub Copilot, Gemini (for Sheets integration)
Development environment: VS Code, Git, GitHub, Google Colab (compute-intensive exercises)
Interview-ready analytics profiles
Data Analyst
Business Analyst
Junior BI Developer
Analytics Engineer
MIS Executive
Reporting Analyst
Operations Analyst
Finance Analyst (data skills)
Marketing Analyst
What distinguishes our graduates in interviews: they can take a dataset, frame an analytical question, clean the data, run analysis, visualise findings, and present recommendations end to end — with a Power BI dashboard published live and a Python analysis notebook on GitHub.
They have presented findings to a panel and answered hard questions about confidence and caveats.
Companies hiring these profiles from Tamil Nadu: every BFSI institution in Chennai; consulting and advisory firms; e-commerce and D2C brands; logistics and supply chain organisations; SaaS companies with business intelligence needs. GCC ecosystem in Chennai hires data and analytics regularly.
Salary context: data analyst roles in Chennai at BFSI and GCC employers typically start at ₹4–7L for freshers with demonstrable skills and a portfolio. ML module adds credibility for roles tagged data science even at junior level.
| Walk in with | Walk out with |
|---|---|
| Basic spreadsheet use | Advanced Excel/Sheets — Power Query, pivot tables, dynamic arrays, dashboards |
| No SQL | Analytical SQL — joins, window functions, CTEs, time-series queries |
| No Python | Pandas-based analysis pipeline, visualisation, basic ML models |
| No statistics | Confidence intervals, hypothesis testing, A/B test design and analysis |
| No BI tool experience | Power BI dashboard published live, DAX proficiency |
| No AI tool discipline | AI-assisted analysis with verification habits and responsible use practice |
| No project portfolio | End-to-end analytics project — dashboard, notebook, stakeholder presentation |
| A fresher | An analyst who can be useful from their first week |
Same curriculum, same assessment
Online
Live instructor-led sessions via Zoom or Google Meet. Screen sharing works well; trainer shares datasets and demos while you work on the same analysis tasks.
Hybrid
In-person for Python and Power BI modules if possible, online for the others. Recordings available for review — not as a replacement for live attendance.
All three modes deliver the same curriculum, the same project, and the same assessment. No mode is a reduced version.
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