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University of Edinburgh

Predictive Analytics Final Project

University of Edinburgh via edX


The final project brings together the skills and knowledge acquired throughout the MicroMasters programme. You will draw on your knowledge of data analysis techniques to demonstrate your capacity to deal effectively with current job market needs.

You will have the opportunity to demonstrate that you can crunch vast amounts of information to gain valuable insight, as well as use a range of approaches for extracting hidden information and building intelligence to assist with decision making.

You will also have to independently apply the methods and tools used to address common practical issues faced by data analysts today, and consolidate your understanding of the most effective methodologies used through hands-on experience. This final project will prepare you for a step change in career or set you up to pursue further study.

Please note, this course is only available to learners who have successfully completed all 4 MicroMasters courses on the verified track prior to undertaking this course:

  • PA1.1x Introduction to Predictive Analytics using Python
  • PA1.2x Successfully Evaluating Predictive Modelling
  • PA1.3x Statistical Predictive Modelling and Applications
  • PA1.4x Predictive Analytics using Machine Learning

Learners who successfully complete this final course as part of the MicroMasters programme can apply to the on-campus Masters in Business Analytics at the University of Edinburgh. Successful completion of the MicroMasters programme does not guarantee acceptance to the Master's but, if accepted, the 30 credits awarded from the MicroMasters program will be recognised as credit obtained towards the 180 credits required for the full MSc. Visit the University of Edinburgh Business Analytics Entry Requirements page for more information.


This course consists of the following assessments:

  1. A theory-based written exam, drawing on a holistic understanding of the four MicroMasters courses.
  2. A Jupyter notebook submission that reflects a real-life case study. This is typically delivered in the form of a proof-of-concept augmented with interpretation and a visual representation of results
  3. A 1,700-word reflective submission based upon the Jupyter notebook submission.

Taught by

Dr Johannes De Smedt

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