Python for Data Science
This section will introduce you to Python which is the primary language used to manage and analyze data. You will learn about functions and loops in Python and get hands-on experience with Jupyter Hub and Python libraries such as Pandas, NumPy and Scipy. You will also explore data visualization tools available in Python such as Matplotlib, Bokeh and Seaborn.

Exploratory Data Analysis and Model Preparation
This module will present the tools you need to clean and validate data, to visualize distributions and relationships between variables. You will understand essential exploratory techniques for summarizing data as well as how to prep data for training your machine learning models. With the tools and skills taught in this section of the course, you will be prepared to work with real data, make discoveries, and present compelling results using Python.

Machine Learning and Model Deployment
In this module, you will cover basic components of building and applying prediction functions with an emphasis on practical applications. You will learn all state of the art AI & Machine learning models such as classification, regression, KNN, Decision Tree and Random Forest as well as how to create, test, train and deploy these models. Several techniques, including supervised learning and theoretical aspects of machine learning will be explained and, finally, you will learn how to deploy machine-learning algorithms on Microsoft Azure Cloud Services.