Course Structure#

Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)#

  • Focus: Data preprocessing methods and exploratory data analysis.

  • Topics: Data preprocessing, visualization, descriptive statistics.

Week 3: Data Preprocessing#

  • Day 11: Introduction to Data Preprocessing in Python

    • Explore the concepts and importance of data preprocessing.

    • Math Focus: Understanding data types, scales, and basic statistics in Python.

  • Day 12: Splitting Data into Training and Test Sets in Python

    • Techniques for splitting data into training and test sets.

    • Math Focus: Random sampling methods and stratified sampling principles.

  • Day 13: Handling Missing Data with Python

    • Techniques for detecting and handling missing data.

    • Math Focus: Imputation techniques and their mathematical rationale.

  • Day 14: Data Normalization and Scaling using Python

    • Learn about data normalization and feature scaling.

    • Math Focus: Z-score normalization, min-max scaling, and their mathematical foundations.

  • Day 15: Encoding Categorical Data in Python

    • Understand and implement categorical data encoding.

    • Math Focus: Binary and one-hot encoding, label encoding, and their mathematical implications.