Learn in-demand technical skills and their workplace applications in this applied machine learning course.
A minimum of a second class Honours degree or equivalent. Applicants without these formal qualifications but with significant appropriate/relevant work/life experience are encouraged to apply.
This course is not suitable for those who studied the University of Stirling's Data Analytics course in 2021.
SFC fully funded places are available for this course. These will be awarded on a first-come, first-served basis to eligible applicants. Please submit your application as soon as possible to secure your funding.
You will learn how to apply machine learning to business and scientific applications. Both the practical aspects of the correct methodology and the theoretic underpinnings are covered so that you know what to do and why you are doing it.
At the end of the module, you should be able to identify the business objectives that can be addressed using data analytics, apply the correct methodology to address them, and report the results to the rest of the business.
If you can program, you can carry out the exercises in Python. Otherwise, you can use a graphical user interface, which requires no programming at all.
Structure and content
The main topics on the module are:
Data mining industry standards
- CRISP-DM and how to apply it
- Running a data driven project and reporting results
The theory of statistical machine learning
- Train / Validate / Test best practice
- The bias-variance trade-off
- Cost minimisation and regularisation
- Linear and Logistic Regression
- Decision Trees
- Clustering Algorithms
- Neural networks
Delivery and assessment
The content is delivered online with recorded videos, exercises and written notes. The assessment involves a practical assignment designed to replicate the type of commercial data analytics project you could expect to carry out in a data analytics role.
The skills taught on the module are in high demand and salaries are also high. The course is designed to teach you the skills and know-how you will need in an analytics role.