Program Structure

Module 1: Setup + Data Analysis Workflow

• Jupyter/Colab setup; project folders and notebooks.
• Data workflow: import → clean → analyze → report.
• Reading data: CSV/Excel/JSON basics.
• Quick checks: head/info/describe, missing values.

Module 2: NumPy for Fast Computing

• Arrays, shapes, dtypes, broadcasting.
• Indexing, slicing, masking.
• Vectorized math and aggregations.
• Random sampling basics and reproducibility (seed).

Module 3: Pandas Fundamentals

• Series and DataFrame operations.
• Filtering, sorting, selecting columns/rows.
• String operations and categorical data (intro).
• Exporting clean datasets.

Module 4: Cleaning and Data Quality

• Missing data: isna, fillna, dropna.
• Type fixes: numeric/date parsing, coercion.
• Duplicates and consistency checks.
• Outliers: detection basics and safe handling.

Module 5: Aggregation and Reporting

• groupby: counts, means, shares.
• Pivot tables and cross-tabs.
• Window functions: rolling and expanding (intro).
• Building summary tables for KPIs.

Module 6: Combining Datasets

• merge: inner/left/right/outer joins.
• concat and append patterns.
• Key integrity: duplicates, missing keys, join audits.
• Reshaping: melt, pivot, stack/unstack (intro).

Module 7: Time-Series Analysis with Pandas

• Datetime conversion and indexing.
• Resampling (daily/weekly/monthly) and trend summaries.
• Lag features and simple seasonality checks (intro).
• Event-based analysis (basic).

Module 8: Speed, Memory, and Best Practices

• Vectorization vs loops; apply pitfalls.
• Memory-friendly types and categorical optimization.
• Reusable functions and clean notebooks.
• Reproducible outputs and documentation.

Hands-On Projects

• Project 1: Sales KPI analysis (clean → groupby → pivot).
• Project 2: Customer dataset join + cohort summary (intro).
• Project 3: Time-series analysis (resample + rolling trends).
• Project 4: Data quality audit report (missing/duplicates/outliers).

Final Assessment

• Timed practical test: clean + analyze a new dataset.
• Deliverables: notebook + final KPI tables + short insights summary.