BSc Management (Marketing)

Year of entry: 2022

Course unit details:
Business Data Analytics

Unit code BMAN24621
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 1
Offered by Alliance Manchester Business School
Available as a free choice unit? No


The course covers a variety of data analytics techniques, including data management and preparation, data preliminary analysis and preprocessing, feature selection and engineering, predictive modelling, clustering, ensemble learning, association analysis, etc. 


Unit title Unit code Requirement type Description
Quantitative Methods for Business and Management BMAN10960 Pre-Requisite Compulsory
Fundamentals of Data Analytics BMAN11060 Pre-Requisite Compulsory
BMAN24621 has pre-requisites of: BMAN10960 or BMAN11060. Only available to students on: Mgt/Mgt Specialism; IMABS and IM. Core for BSc ITMB.

Pre-requisite course units have to be passed by 40% or above at the first attempt unless a higher percentage is indicated below:

BMAN10960 Quantitative Methods for Business & Management except BSc Mathematics and Management & Maths Stats & OR.



To provide students with an understanding  of data analytics for business  and management.

To help develop skills in the use of industry-leading software tools, mainly SAS packages.


Learning outcomes

At the end of the course students should be able to:

•       Understand the fundamentals of data analytics and its applications to real life business problems,

•       Understand a variety of data analytics techniques, including data pre-processing, feature selection, predictive modelling, unsupervised learning, etc., and,

•       Demonstrate the ability to use specialised software tools to analyse large sets of data in different business contexts.



•       Data management and preparation,

•       Data preliminary analysis,

•       Data preprocessing,

•       Feature selection and engineering

•       Predictive modelling

•       Clustering analysis

•       Ensemble learning

•       Association analysis

•       Semi-supervised techniques

•       Visual analytics and big data analytics


Teaching and learning methods

Two-hour lecture and two-hour lab per week (see detailed schedule below) for 11 weeks, directed reading and computer based support.


Assessment methods

100% individually assessed coursework


Feedback methods

•       Informal advice and discussion during lectures or seminars.

•       Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.

•       Written and/or verbal comments on assessed or non-assessed work.

•       Generic feedback posted on Blackboard regarding overall examination performance.

In addition to the central unit evaluation questionnaire, student are encouraged to give feedback through emails and conversations at anytime, and questionnaire near the end of the semester


Recommended reading

Galit Shmueli, et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley & Sons, 2018.

Max Bramer, Principles of Data Mining, Springer, 2013.

Other reading materials will be shared via Blackboard.



Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 22
Independent study hours
Independent study 156

Teaching staff

Staff member Role
Yu-Wang Chen Unit coordinator

Additional notes

Pre-requisites: BMAN11060 Fundamentals of Data Analytics for BSc ITMB, and BMAN10960 Quants for Business and Management (except BSc Mathematics and Management & Maths Stats & OR.) or equivalent for other BSc programmes

Co-requisites: None

Dependent courses: None

Programme Restrictions:

  • BSc Information Technology Management for Business
  • BSc Management and Management (Specialisms),
  • BSc International Management with American Business Studies,
  • BSc International Management,
  • BSc Mathematics and Management,
  • Maths Stats & OR

For Academic Year 2021/22

Updated: March 2021

Approved by: March UG Committee

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