LUBS3012 Data Analytics for Business
Module manager: Xingjie Wei
Email: x.wei1@leeds.ac.uk
Taught: Semester invalid View Timetable
Year running
This module is not approved as an Elective
Module summary
This module will expand your understanding of how businesses and other organisations use data analytics to support a data-driven approach to management decision making. The topics covered include data management, business applications of data analytics, and the strategic requirements for effective analytics.
Objectives
The objective of this module is to increase students' understanding of how businesses and other organisations use data analytics to support a data-driven approach to management decision making.
Learning outcomes
Upon completion of this module, students will be able to:
1. Critically discuss the fundamentals of business analytics and data management concepts
2.Understand the principle of data analytics business applications
3. Determine and evaluate the strategic prerequisites for effective analytics.
4. Investigate unstructured business problems in order to identify the critical factors involved using data analytics business applications
5. Interpret and critically evaluate data analysis results in unstructured business problems.
Syllabus
Indicative content:
1. The foundations of business analytics
2. Data understanding and architecture
3. Data preparation
4. Data Modelling and predictive analytics: classification, regression and clustering
5. Model performance evaluation
6. Deployment
Teaching Methods
| Delivery type |
Number |
Length hours |
Student hours |
| Lecture |
11 |
1 |
11 |
| Practical |
10 |
1.5 |
15 |
| Private study hours |
174 |
| Total Contact hours |
26 |
| Total hours (100hr per 10 credits) |
200 |
Private study
Post-lecture reading
Seminar handout reading and preparation
Coursework revision
Opportunities for Formative Feedback
In each lecture, there will be about 5 to 10 mins for students to ask questions about the coursework and receive feedback from teaching staffs. In each practical sessions for each week, there will be interactive practical tasks and feedback will be available to students so they can improve week by week. Besides that, there will be ‘mini-sessions’ where students will have individual time to show the teaching staffs part of their on-going coursework report / programme / data processing, and teaching staffs will provide formative feedback (verbal or written), so students will have the opportunity to improve their work and coursework report before final submission.
Methods of Assessment
Coursework
| Assessment type |
Notes |
% of formal assessment |
| Report |
Max 4,000 word report on utilising data analytics methods on a specific dataset. |
100 |
| Total percentage (Assessment Coursework) |
100 |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading List
There is no reading list for this module
Last updated: 29/04/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team