Introduction to Data Science and scikit-learn in Python

This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data

• Employ AI techniques to test hypotheses using Python
• Apply machine learning models with Numpy, Pandas, and Scikit-Learn
• Use exploratory data analysis libraries effectively
• Build classification models to predict health conditions

• Basic computer and internet skills
• Ability to read course instructions in English and complete short practice activities