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2017/18 Undergraduate Module Catalogue

LUBS1530 Business Analytics 1

10 creditsClass Size: 50

Module manager: Prof Bill Gerrard
Email: W.J.Gerrard@lubs.leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2017/18

Pre-requisite qualifications

A-Level Mathematics or Statistics Grade B

This module is mutually exclusive with

LUBS1540Introduction to Business Analytics

This module is not approved as a discovery module

Module summary

This module introduces you to business analytics - defined as the use of statistical analysis (and related techniques) to support an evidence-based approach to management decision making. The different stages in the analytics process are examined using case studies and practical applications.

Objectives

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
Upon completion of this module students will be able to:
- Distinguish and characterise the nature of business analytics
- Describe accurately the different stages in the analytics process
- Recognise and apply alternative analytical approaches used in business analytics
- Describe and contextualise the requirements for effective evidence-based practice in the business environment

Skills outcomes
Upon completion of this module students will be able to:
Transferable
- Write and communicate effectively
- Explain the importance of ethics, integrity and responsibility in undertaking data analysis

Subject Specific
- Research structured business problems with the ability to identify the critical factors involved
- Apply statistical tools accurately to analyse structured business problems
- Critically evaluate and interpret the results of data analysis in structured business problems


Syllabus

Indicative content
Evidence-based practice
The 3 D’s of business analytics
The Analytical DELTA model
Big-data analytics
Databases and data warehouses
Data visualisation
Alternative analytical approaches and applications
Web-based analytics
Case studies of analytics in action
Ethical issues in data analysis

Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Delivery typeNumberLength hoursStudent hours
Lecture111.0011.00
Tutorial91.009.00
Private study hours80.00
Total Contact hours20.00
Total hours (100hr per 10 credits)100.00

Private study

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

Opportunities for Formative Feedback

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.

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information


Coursework
Assessment typeNotes% of formal assessment
Essay2000 words100.00
Total percentage (Assessment Coursework)100.00

Resit will be 100% by coursework.

Reading list

The reading list is available from the Library website

Last updated: 04/12/2017

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