• Length:
    6 Weeks
  • Effort:
    8–10 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Advanced
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).

Learners pursuing the MicroMasters programme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics and PA1.2x Evaluation of Predictive Modelling on the verified track prior to undertaking this course.

About this course

In this course, you will learn about two key predictive modelling techniques: linear and logistic regression, and look at several relevant applications to see how they can be used in real-world scenarios.

Before delving into advanced modelling methods, you will benefit from gaining a solid understanding of these two basic modelling techniques. They are the workhorse of effective predictive modelling, based on whether a response is continuous or categorical. If you take this course as part of the MicroMasters programme, you will discover that many advanced techniques are actually adjustments to linear (logistic) regression.

The first half of the course focuses on linear regression. This technique allows you to model a continuous outcome variable using both continuous and categorical predictors. This enables you, for example, to predict product sales based on a number of customer variables.

In the second half of the course, you will learn about logistic regression, which is the counterpart of linear regression, when the response variable is categorical. You will also be introduced to naïve Bayes; a very intuitive, probabilistic modelling technique.

What you'll learn

In this course, you will:
  • Learn how to apply statistical predictive models 
  • Obtain theory on linear regression estimation and inference 
  • Learn how to use logistic regression models for binary classification 
  • Gain experience implementing theoretic models in statistical software packages  
Week 1: Simple Linear Regression
Week 2: Multiple Linear Regression
Week 3: Extensions and Applications
Week 4: Introduction to Naive Bayes
Week 5: Logistic Regression
Week 6: Estimation and Comparison

Meet your instructors

Dr Galina Andreeva
Senior Lecturer in Management Science
The University of Edinburgh
Dr Matthias Bogaert
Postdoctoral Research Fellow
KU Leuven

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Frequently asked questions

What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.

What software will I be required to use?
All coding activities on this course will be hosted on Vocareum. You will be able to access this free software directly within the edX platform. There is no requirement to purchase further software in order to complete this course.

What do I need to complete the course?
For successful completion of this course, you will need access to a computer or mobile device and a reliable internet connection.

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.