Nine modules make up the syllabus, covering 120+ learning hours, sequenced the way analysts actually approach a problem — pull the data, clean it up, break it down, make sense of it, visualise it, then explain it. Every module closes with a graded assignment on a genuine dataset, and the last one pulls it all together into portfolio work.
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Module 1 — Excel for Data Analytics
Advanced formulas (VLOOKUP, XLOOKUP, INDEX-MATCH, SUMIFS, COUNTIFS, nested IF); data cleaning with text functions, Find & Replace, Flash Fill and duplicate removal; Pivot Tables, PivotCharts, Slicers, Timelines; Power Query; data validation, conditional formatting, dashboard design; histogram, Pareto, combo and waterfall charts.
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Module 2 — SQL & Database Management
Relational database concepts, ER diagrams, normalisation; SELECT, WHERE, ORDER BY, GROUP BY, HAVING; all join types; subqueries and CTEs; window functions (ROW_NUMBER, RANK, LEAD, LAG); connecting SQL databases to Python.
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Module 3 — Python for Data Analytics
Python fundamentals; lists, tuples, dictionaries, sets, file operations; NumPy arrays and vectorised operations; Pandas Series/DataFrames — indexing, filtering, merging, grouping; handling missing values and duplicates; Matplotlib, Seaborn, Jupyter Notebook.
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Module 4 — Statistics & Business Mathematics
Descriptive statistics; probability fundamentals and data distributions; sampling methods and the Central Limit Theorem; hypothesis testing (p-values, confidence intervals, t-tests, chi-square, ANOVA); correlation and regression basics; A/B testing.
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Module 5 — Data Cleaning & EDA
Data quality assessment and profiling; handling missing values; outlier detection and treatment; feature creation and binning; univariate, bivariate, multivariate analysis; correlation heatmaps; structuring an EDA report.
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Module 6 — Power BI
Connecting to Excel, SQL, CSV and web sources; Power Query Editor, data modelling; DAX (calculated columns, measures, time intelligence); visuals, filters, slicers, bookmarks, drill-through; publishing to Power BI Service; row-level security basics.
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Module 7 — Tableau
Tableau architecture, data connection and blending; dimensions, measures, calculated fields; filters, groups, sets, hierarchies; parameters; reference lines, trend lines, forecasting; dashboards, actions, stories.
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Module 8 — AI & Gen AI for Data Analysts
Prompt engineering for data tasks; generating/debugging SQL and Python with AI assistance; Copilot in Excel and Power BI; AI-assisted insight summaries; verifying AI output for hallucinations and wrong aggregations; data privacy rules for AI tools.
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Module 9 — Capstone Projects & Career Preparation
End-to-end capstone project on a real business dataset; building a GitHub portfolio and dashboard showcase; resume writing and LinkedIn optimisation; SQL and case-study interview practice; mock interviews with feedback.
