Apply your predictive modelling acumen in a business case setting.
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
- PA1.2x Evaluation of Predictive Modelling
- PA1.3x Statistical Predictive Modelling
- PA1.4x Machine Learning-Based Predictive Modelling
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.
What will you learn
In this course, you'll learn:
- How to effectively analyse vast amounts of data to gain valuable insight
- A range of techniques to extract hidden information
- How to build intelligence to assist with decision making
- How to address common, current data analysis issues
- The most effective methodologies through hands-on experience