◾ Learning Objectives
◾ Understanding the Domain
◾ Understanding the Dataset
◾ Python package for data science
◾ Importing and Exporting Data in Python
◾ Basic Insights from Datasets

◾ Identify and Handle Missing Values
◾ Data Formatting
◾ Data Normalization Sets
◾ Binning
◾ Indicator variables

◾ Descriptive Statistics
◾ Basic of Grouping
◾ ANOVA
◾ Correlation
◾ More on Correlation

◾ Simple and Multiple Linear Regression
◾ Model Evaluation Using Visualization
◾ Polynomial Regression and Pipelines
◾ R-squared and MSE for In-Sample Evaluation
◾ Prediction and Decision Making

◾ Model Evaluation
◾ Over-fitting, Under-fitting and Model Selection
◾ Ridge Regression
◾ Grid Search
◾ Model Refinement