Coronavirus information for applicants and offer-holders

We understand that prospective students and offer-holders may have concerns about the ongoing coronavirus outbreak. The University is following the advice from Universities UK, Public Health England and the Foreign and Commonwealth Office.

Read our latest coronavirus information

MSc Business Analytics: Operational Research and Risk Analysis / Course details

Year of entry: 2020

Course unit details:
Data Analytics for Business Decision Making

Unit code BMAN60422
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit? No

Overview

This course covers the fundamentals of data analytics, data management, data preprocessing, predictive modelling, unsupervised learning, advanced analytics and big data in the context of supporting business decision making.

 

Pre/co-requisites

BMAN60422 Programme Req: BMAN60422 is only available as a core unit to students on MSc Business Analytics, and as an elective to students on MSc Operations, Project and Supply Chain Management, and MEng (Hons) Computer Science

BMAN60422 is only available as a core unit to students on MSc Business Analytics, and as an elective with permission to students on MSc Operations, Project and Supply Chain Management and other relevant MSc programmes.

 

Aims

The aim of this course is to provide students with an understanding of data analytics for business decision making. It will discuss a wide range of data analytical techniques, including data preparation and preprocessing, classification, clustering, predictive modelling, text mining, and visual analytics. Emphasis will be placed on the use of an industry-leading software tool, SAS.

 

Learning outcomes

At the end of the course unit, student should be able to:

•       Understand the fundamentals of data analytics and its application to business and management decision making,

•       Understand a variety of data analysis techniques, such as data preprocessing, classification and clustering, prediction and forecasting, association rule mining & text mining, etc.,

•       Discuss how visual analytics can be used to understand big data, extract insights and identify patterns,

•       Demonstrate the ability to use specialised software tools, such as SAS, to analyse large sets of data in real-world problems.

 

Teaching and learning methods

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

Assessment methods

50% Exam (closed book, 2 hours)

50% Group Coursework (4,000 words)

Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.

  • 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 coursework.

  • Written and/or verbal comments after students have given a group or individual presentation.

  • Generic feedback posted on Blackboard regarding overall examination performance.

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.

Jiawei Han, Micheline Kamber, Jian Pei; Data mining: concepts and techniques. Elsevier (3rd ed.), 2012.

Frank J. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Wiley, 2012

McKinsey Analytics, Analytics comes of age, McKinsey & Company, 2018.

INFORMS Analytics Magazine, http://www.analytics-magazine.org/

 

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Yu-wang Chen Unit coordinator

Additional notes

Informal Contact Method

  • Office Hours
  • Online Learning Activities (blogs, discussions, self assessment questions)

Return to course details