Module 1: Excel for Data Analytics (12 Hours)
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 and Timelines; Power Query for import/transform; data validation, conditional formatting, dashboard design; charts including histogram, Pareto, combo and waterfall.
Module 2: SQL & Database Management (18 Hours)
Relational database concepts, ER diagrams, normalisation; SELECT, WHERE, ORDER BY, GROUP BY, HAVING; all join types (inner, left, right, full outer, self, cross); subqueries and Common Table Expressions; window functions (ROW_NUMBER, RANK, LEAD, LAG); connecting SQL databases to Python.
Module 3: Python for Data Analytics (20 Hours)
Python fundamentals — variables, data types, loops, functions; lists, tuples, dictionaries, sets, file operations; NumPy arrays and vectorised operations; Pandas Series and DataFrames — indexing, filtering, merging, grouping; handling missing values and duplicates; Matplotlib, Seaborn and Jupyter Notebook.
Module 4: Statistics & Business Mathematics (12 Hours)
Descriptive statistics (mean, median, variance, standard deviation); 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 in a business context.
Module 5: Data Cleaning & EDA (12 Hours)
Data quality assessment and profiling; handling missing values (deletion, imputation, flagging); outlier detection and treatment; feature creation and binning; univariate, bivariate and multivariate analysis; correlation heatmaps and structuring an EDA report.
Module 6: Power BI (18 Hours)
Connecting to Excel, SQL, CSV and web sources; Power Query Editor, data modelling and relationships; DAX — calculated columns, measures, time intelligence; visual types, filters, slicers, bookmarks, drill-through; publishing to Power BI Service with scheduled refresh; row-level security basics.
Module 7: Tableau (10 Hours)
Tableau architecture, data connection and blending; dimensions, measures and calculated fields; filters, groups, sets and hierarchies; parameters and dynamic controls; reference lines, trend lines and forecasting; dashboards, actions and stories.
Module 8: AI & Gen AI for Data Analysts (8 Hours)
Prompt engineering for data tasks; generating and debugging SQL/Python with AI assistance; Copilot in Excel and Power BI; AI-assisted insight summaries and report drafting; verifying AI output for hallucinations, wrong aggregations and false confidence; data privacy rules for what should never be pasted into a public AI tool.
Module 9: Capstone Projects & Career Preparation (10 Hours)
End-to-end capstone project on a real business dataset; building a GitHub portfolio and dashboard showcase; analyst resume writing and LinkedIn optimisation; SQL and case-study interview practice; mock interviews with feedback.
