Mid-Module Assessment 

1 - Set exercises - 30

End-of-Module Assessment 

2 - Portfolio - 70

To view information for a specific offering, select a value from the drop down above.

The module will include: 
<ul><li>Machine learning concepts and techniques </li></ul>
<ul><li>Preparing and processing data </li></ul>
<ul><li>Working with machine learning models </li></ul>
<ul><li>Communicating and applying machine learning insights </li></ul>

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Self-directed - 

Directed Asynchronous - 

 Business Analytics: Methods, Models, and Decisions 

DAM420 - Machine Learning for Business Analytics

DAM420-YEAR-ONL-REM

This module has 8 blocks of online asynchronous learning. In each of these blocks there will be guided independent study plans for the module that you can engage, at your own pace, and around your other work and personal commitments. These study plans will typically consist of a mixture of videos, associated reading, problems and self-assessment quizzes that are appropriate for that part of the module. Support will be available by contacting the module team via the VLE or via e-mail.

It should be noted that the 150 hours below includes the time to prepare and submit the necessary assessments.

Machine Learning for Business Analytics

Interpret and communicate machine learning results. 

Skills

Demonstrate reflection on the value of machine learning for business.

Values and attitudes

Plan and use appropriate approaches in relation to machine learning models. 

Assess and utilise machine learning concepts and methods in business analytics. 

Knowledge

Formulate approaches for preparing data and applying machine learning techniques. 