Regression Modeling for Prediction Using Python: A Short Course

Python is a general-purpose programming language. It is open-source, powerful, and easy to use. Because of this, Python is one of the most popular languages in the world, and it has become indispensable in data science.

In this course, we will cover various types of regression models. These are supervised statistical techniques: you give them variables and an outcome, and they will try to find the best fitting set of parameters that most closely predicts the outcome from the variables.

You will learn how to fit regression models to data and how to use trained models for prediction. You will be introduced to multivariable and multivariate linear regression, logistic regression, and mediation analysis. To avoid over-fitting, you will also learn how to divide datasets into train and test sets, and how to implement different types of cross-validation.

The above will be implemented in scikit-learn, a Python package for machine learning. It is a powerful tool for data science, and its common interface will allow you to extend what you learn in this course to other models.

Starting October 24, we are offering this seminar as an 8-hour synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two 2-hour lecture sessions which include hands-on exercises, separated by a 30-minute break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

Join Dr. Dalmaijer for Unsupervised Statistical Learning Using Python on August 12-13 to learn how to use the scikit-learn package in Python to uncover subgroups and latent components in datasets with unsupervised machine-learning techniques.