MSc Business Analytics: Operational Research and Risk Analysis / Course details
Year of entry: 2020
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Course unit details:
Data Analytics for Business Decision Making
|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|
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.
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.
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.
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
50% Exam (closed book, 2 hours)
50% Group Coursework (4,000 words)
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.
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/
|Scheduled activity hours|
|Practical classes & workshops||10|
|Independent study hours|
|Yu-wang Chen||Unit coordinator|
Informal Contact Method
- Office Hours
Online Learning Activities (blogs, discussions, self assessment questions)