Module manager: Prof Bill Gerrard
Email: w.j.gerrard@lubs.leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2016/17
LUBS1535 Excel for Business Analytics Or LUBS1525 Analytical Methods
| LUBS1530 | Business Analytics 1 |
This module is approved as a discovery module
This module introduces business analytics defined as the use of statistical analysis and related techniques to support an evidence-based approach to management decision making.
The module aims to give students an introduction to business analytics defined as the use of statistical analysis and related techniques to support an evidence-based approach to management decision making.
Learning Outcomes - Knowledge/Application
Upon completion of this module students will be able to demonstrate accurate, in-depth and thorough knowledge of:
1. The nature of business analytics
2. The different stages in the analytics process
3. The alternative analytical approaches used in business analytics
4. The requirements for effective evidence-based practice in the business environment
Learning Outcomes - Skills
Upon completion of this module students will be able to:
Subject specific
1. Research structured business problems with the ability to identify the critical factors involved
2. Apply statistical tools accurately to analyse structured business problems using Excel
3. Critically evaluate and interpret the results of data analysis in structured business problems
Transferable
1. Write and communicate effectively
2. Demonstrate an awareness of ethics, integrity and responsibility in undertaking data analysis
Upon completion of this module students will be able to:
1. Research structured business problems with the ability to identify the critical factors involved
2. Apply analytical tools accurately to analyse structured business problems
3. Critically evaluate and interpret the results of data analysis in structured business problems
Indicative content:
1. Business analytics and evidence-based practice
2. Davenport’s five-stages model of analytical competitors
3. Understanding the data architecture of an organisation
4. Using Microsoft Access
5. Cleaning up the data
6. Further data visualisation with Microsoft Excel
7. Choosing the best modelling approach
8. Delivering the deliverables
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 11 | 1 | 11 |
| Tutorial | 9 | 1 | 9 |
| Private study hours | 80 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Private study
2 hours reading per lecture = 22 hours
2 hours preparation per tutorial = 18 hours
Assessed coursework = 40 hours
Total private study = 80 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 |
|---|---|---|
| Essay | 2000 words | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Resit will be assessed by the same methodology as the first attempt.
The reading list is available from the Library website
Last updated: 29/04/2016
Errors, omissions, failed links etc should be notified to the Catalogue Team