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 4: Exploratory Data Analysis (EDA)#

  • Day 16: Introduction to EDA and Data Visualization in Python

    • Basics of exploratory data analysis and data visualization techniques.

    • Math Focus: Descriptive statistics and graphical representation of data.

  • Day 17: Implementing Descriptive Statistics for EDA in Python

    • Practical implementation of descriptive statistics in Python.

    • Math Focus: Measures of central tendency and dispersion.

  • Day 18: Visualization Techniques for Data Distribution in Python

    • Create various types of plots to visualize data distributions.

    • Math Focus: Histograms, box plots, and understanding data distributions.

  • Day 19: Correlation Analysis using Python

    • Explore correlation analysis and its implementation.

    • Math Focus: Correlation coefficients and interpreting correlation in data.

  • Day 20: Feature Selection and Importance in Python

    • Techniques for feature selection and understanding feature importance.

    • Math Focus: Information gain, Gini impurity, and feature importance metrics.