Program Structure

Module 1: Python Setup + Basics

• Jupyter/Colab setup and project folders.
• Variables, types, operators, strings.
• Lists, tuples, sets, dictionaries.
• Loops, conditions, functions.

Module 2: Working with Files and Data

• Read/write CSV and text files.
• Intro to APIs and JSON (basic).
• Data cleaning basics: nulls, types, duplicates.
• Simple logging and error handling.

Module 3: NumPy for Fast Computation

• Arrays, indexing, slicing.
• Vectorized operations and broadcasting.
• Aggregations and basic math.
• Random sampling basics.

Module 4: Pandas for Data Analysis

• Series/DataFrame, filtering, sorting.
• Groupby summaries and pivot tables.
• Merging and joining datasets.
• Date/time handling and time-series basics.

Module 5: Visualization (Matplotlib + Seaborn)

• Line, bar, scatter, histogram, box plots.
• Heatmaps and pair plots (intro).
• Labels, legends, and exporting plots.
• Storytelling with charts.

Module 6: Exploratory Data Analysis (EDA)

• Distributions, skew, and transformations (intro).
• Outliers: detection and handling (basic).
• Correlation and simple feature ideas.
• EDA checklist and summary report.

Module 7: Statistics + ML Foundations

• Sampling and basic probability concepts.
• t-test/chi-square concepts (intro) and interpretation.
• ML workflow: split, train, validate, test.
• Models: linear/logistic regression, tree models (overview).

Final Project

• Choose a dataset (sales, finance, healthcare, research, public data).
• Deliverables: cleaned dataset + EDA notebook + visuals + insights summary.
• Optional: simple ML baseline model with metrics.