Data Science. Logistic Regression
About the course
In this course, you will learn what is a logistic regression model, what it is used for, and how to fit a model to real data using RStudio.
The course is composed by two lessons:
- A Theory Lesson, where we will cover some fundamental theory on logistic regression and complete a detailed tutorial on logistic regression in RStudio. The theoretical concepts covered in Module 1.1 are not necessary to complete the exercises, but they will help you a great deal in understanding why things work the way they do. Module 1.2 will give you all the necessary information to complete the exercises included in the next lesson.
- A Practice Lesson, with many exercises to test your newly acquired skills! Every module of the Practice Lesson can be completed independently, so feel free to skip around and focus on what is more important or challenging to you. At the beginning of each exercise, we will tell if data and/or libraries need to be imported to complete it.
It is a good course that can be considered as a practical guide for logistic regression using R. The course gives a recommendation how to make a model selection as well as how to interpret correctly coefficients of a model. For better understanding, two real datasets are used for exercises. The course does not provide to you deep theory or mathematical substantiation. So, I consider this course as a nice supplement to such materials as https://stepik.org/524 (in Russian) or any appropriate textbook (e.g. "Hastie et al. - The Elements of Statistical Learning").