Machine Learning

Applications for June 2022 entry are now closed. 

Learn in-demand technical skills and their workplace applications in this applied machine learning course.

Please be aware that this course is not suitable for those who studied the University of Stirling's Data Analytics course in 2021.

Key facts

  • Module codeITNPBD6
  • Start date 13 Jun 2022
  • Duration12 weeks
  • Credit value SCQF 20
  • SCQF level Level 11
  • Fees £1,056
  • Mode of study part time
  • Delivery online

Applications for June 2022 entry are now closed. 

Overview

In the era of Big Data, machine learning and data analytics are vital to the success of any organisation. From simple sales forecasts to the AI behind self-driving cars, data are helping to drive continuous improvement. The techniques are powerful, but need to be used with a full understanding of the subject. It is vital to understand best practice, and how an analytics project fits with the business objectives.

This is a technical course, but it also has a very applied focus. You will learn the theory behind machine learning techniques such as regression, decision trees and neural networks, and you will learn how to apply them to different kinds of data. What’s more, you will learn how to conduct a machine learning project from start to end in a business setting.

The course is taught by lecturers who have worked in both industry and academia in data science roles. They bring their experience of what it is like to work on commercial data analytics projects and prepare you to do the same.

Entrance requirements

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.

Please be aware that this course is not suitable for those who studied the University of Stirling's Data Analytics course in 2021.

Funding

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.

Objectives

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

Analytics techniques

  • 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.

Module coordinator

Employability

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.

What next?