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

    FREE
    Add a Verified Certificate for $300 USD

  • 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 on the verified track prior to undertaking this course.

About this course

We can only succeed in capturing insights from data if we first know how to measure the effectiveness of models. In this course, you will learn appropriate measures that are used to evaluate predictive models in a variety of contexts. You will also learn about procedures that are used to ensure that models do not cheat through, for example, overfitting or predicting incorrect distributions. 
 
A predictive exercise is not finished when a model is built. It is important to construct a vast number of models, not only to find the best one, but to ensure that they point in the same direction. This course will equip you with essential skills and knowledge for understanding performance evaluation metrics, to determine whether a model is performing adequately or not.  
 
You will also discover the ways that different model evaluation criteria illustrate how one model excels over another, and how to identify when to use certain criteria over others. This is the foundation to performing successful predictive analysis.

What you'll learn

In this course, you will:
  • Develop knowledge of the most popular and effective measure and sampling strategies  
  • Implement effective measures and strategies to evaluate predictive models 
  • Use evaluation concepts on datasets to determine appropriateness and strength of techniques
Week 1: Evaluation Metrics and Feature Selection 
Week 2: Feature Selection and Correlation Analysis 
Week 3: Feature Selection with Decomposition Techniques 
Week 4: Sampling Techniques 
Week 5: Resampling Techniques 
Week 6: Case Study

Meet your instructors

Dr Johannes De Smedt
Dixons Carphone Lecturer in Business Analytics
The University of Edinburgh

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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.