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5 Months · From raw data to decisions

Data Science & Analytics

Turn raw data into decisions: Excel, SQL, Python, statistics, Power BI and machine learning, ending in a portfolio of real analyses. For students and working professionals, in Chennai and online.

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  • 8Phases
  • 5Months
  • 100%Project Based
Excel◆ PivotTables◆ Data Cleaning◆ SQL◆ PostgreSQL◆ Window Functions◆ Python◆ Pandas◆ Jupyter◆ Statistics◆ A/B Testing◆ Regression◆

Phases 1–2

Excel & Data Thinking

Framing business questions, cleaning messy data, formulas, lookups and PivotTables to answer them quickly.

01
Phase 1

Excel & Data Thinking

Framing business questions, cleaning messy data, formulas, lookups and PivotTables to answer them quickly.

ExcelPivotTablesData Cleaning
02
Phase 2

SQL for Analysis

Querying real databases: joins, aggregations, CTEs and window functions in PostgreSQL.

SQLPostgreSQLWindow Functions

Phases 3–4

Python & Pandas

NumPy and Pandas for loading, reshaping and analysing data in Jupyter notebooks.

03
Phase 3

Python & Pandas

NumPy and Pandas for loading, reshaping and analysing data in Jupyter notebooks.

PythonPandasJupyter
04
Phase 4

Statistics & Experiments

Distributions, sampling, hypothesis tests, A/B testing and regression, explained with business examples.

StatisticsA/B TestingRegression

Phases 5–6

Visualisation & Storytelling

Charts that make a point with Matplotlib, Seaborn and Plotly, and presenting findings to people who are not analysts.

05
Phase 5

Visualisation & Storytelling

Charts that make a point with Matplotlib, Seaborn and Plotly, and presenting findings to people who are not analysts.

MatplotlibSeabornPlotly
06
Phase 6

Power BI

Data modelling, Power Query and DAX, and publishing dashboards that teams actually open.

Power BIDAXPower Query

Phases 7–8

Machine Learning for Analysts

Scikit-learn for classification, regression and forecasting, with feature engineering and honest model evaluation.

07
Phase 7

Machine Learning for Analysts

Scikit-learn for classification, regression and forecasting, with feature engineering and honest model evaluation.

Scikit-learnForecastingXGBoost
08
Phase 8

Capstone & Portfolio

An end-to-end analysis on a real dataset, shared as a Streamlit app and a written report, plus interview preparation.

StreamlitPortfolioInterviews

Curriculum

Every phase, in order.

8 phases over 5 months. Each one ends with something you've built and can show, not a quiz you've passed.

01
Phase 1

Excel & Data Thinking

Framing business questions, cleaning messy data, formulas, lookups and PivotTables to answer them quickly.

ExcelPivotTablesData Cleaning
02
Phase 2

SQL for Analysis

Querying real databases: joins, aggregations, CTEs and window functions in PostgreSQL.

SQLPostgreSQLWindow Functions
03
Phase 3

Python & Pandas

NumPy and Pandas for loading, reshaping and analysing data in Jupyter notebooks.

PythonPandasJupyter
04
Phase 4

Statistics & Experiments

Distributions, sampling, hypothesis tests, A/B testing and regression, explained with business examples.

StatisticsA/B TestingRegression
05
Phase 5

Visualisation & Storytelling

Charts that make a point with Matplotlib, Seaborn and Plotly, and presenting findings to people who are not analysts.

MatplotlibSeabornPlotly
06
Phase 6

Power BI

Data modelling, Power Query and DAX, and publishing dashboards that teams actually open.

Power BIDAXPower Query
07
Phase 7

Machine Learning for Analysts

Scikit-learn for classification, regression and forecasting, with feature engineering and honest model evaluation.

Scikit-learnForecastingXGBoost
08
Phase 8

Capstone & Portfolio

An end-to-end analysis on a real dataset, shared as a Streamlit app and a written report, plus interview preparation.

StreamlitPortfolioInterviews

Career Deliverables

What you walk out with.

Opportunities waiting for you — and the evidence to go after them.

01

Deployed projects

Live URLs and a public repo, not screenshots.

02

Verifiable certificate

Checkable by any employer at any time.

03

Interview practice

Technical and HR rounds with written feedback.

04

Placement support

Referrals and applications until you land the role.

Start the
Data Science track.

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