Module manager: Sajid Siraj
Email: S.Siraj@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2017/18
A-Level Mathematics or Statistics Grade B
| LUBS1530 | Business Analytics 1 |
| LUBS2920 | Advanced Analytical Methods |
| LUBS2935 | Intermediate Business Analytics |
| LUBS3205 |
This module is not approved as a discovery module
This module will extend your knowledge about how businesses and other organisations use data analytics to support an evidence-based approach to management decision making. Topics covered include data management, business applications of data analytics, and the strategic and cultural requirements for effective analytics.
The module aims to extend the knowledge of students on how businesses and other organisations use data analytics to support an evidence-based approach to management decision making.
Learning Outcomes - Knowledge/Application
Upon completion of this module students will be able to:
1. Discuss the foundations of business analytics
2. Apply concepts of data management
3. Use business applications of data analytics
4. Identify and discuss the strategic and cultural requirements for effective analytics
Learning Outcomes – Skills
Upon completion of this module students will be able to:
Subject specific
1. Research unstructured business problems with the ability to identify the critical factors involved
2. Apply statistical and other quantitative methods accurately to analyse unstructured business problems
3. Critically evaluate and interpret the results of data analysis in unstructured business problems
Transferable
1. Write and communicate effectively
Upon completion of this module students will be able to:
1 Research unstructured business problems with the ability to identify the critical factors involved
2 Apply statistical and other quantitative methods accurately to analyse unstructured business problems
3 Critically evaluate and interpret the results of data analysis in unstructured business problems
Indicative content:
1. The foundations of business analytics
2. Data architecture
3. Data silos and integrated data systems
4. Advanced data visualisation
5. Operations research
6. Performance analytics
7. Predictive analytics
8. Investment analytics
9. Retail/market analytics
10. Strategic and cultural requirements for effective analytics
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Tutorial | 18 | 1 | 18 |
| Private study hours | 160 | ||
| Total Contact hours | 40 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Private study
3 hours reading per lecture = 66 hours
3 hours preparation per tutorial = 54 hours
Assessed coursework = 40 hours
Total private study = 160 hours
Student progress will be monitored principally by tutorial performance. Selected tutorial assignments will be submitted in advance and marked to provide feedback on student progress.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 3500 words | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by coursework.
The reading list is available from the Library website
Last updated: 25/01/2018
Errors, omissions, failed links etc should be notified to the Catalogue Team