• Collect and import data  
• Clean, prep, and format data  
• Manipulate data frames  
• Summarize data  
• Build machine learning regression models  
• Refine your models  
• Create data pipelines.  

Course Syllabus

• 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 
• Reading: Kernel Density Estimation (KDE) Plots for Model Evaluation, Completed
• 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

Learning Objectives

• Import, clean, and prepare data for analysis. 
• Use Pandas, DataFrames, NumPy, and SciPy. 
• Load, manipulate, analyze, and visualize data. 
• Build machine learning models  

Recommended Skills Prior to Taking this Course