• How to 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 datasets with pandas, an open-source library
• Build machine-learning models and make predictions with scikit-learn, another open-source library

It includes following parts:

Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

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