• Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
• Use Pandas, DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
• Load, manipulate, analyze, and visualize dataset
• Build machine-learning models and make predictions with scikit-learn

Module 1 - Importing Datasets

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

Module 2 - Cleaning and Preparing the Data

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

Module 3 - Summarizing the Data Frame

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

Module 4 - Model Development

• Simple and Multiple Linear Regression
• Model EvaluationUsingVisualization
• Polynomial Regression and Pipelines
• R-squared and MSE for In-Sample Evaluation
• Prediction and Decision Making

Module 5 - Model Evaluation

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