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.