This course covers the fundamentals of data analytics, data management, predictive modelling, pattern discovery, advanced analytics and big data in the context of supporting business decision making.
Masters (MSc) in Business Analytics: Operational Research and Risk Analysis
With MSc Business Analytics, you will learn the skills to ensure that processes run smoothly, particularly in the face of challenges and opportunities arising from the global reach of business. You will gain vital knowledge and practical skills to become a professional operations, project or supply chain manager in a globalised environment.
Read about other business Master's degree courses at Alliance MBS >>
MSc Business Analytics: Operational Research and Risk Analysis
Year of entry: 2019
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|
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 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 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.
50% Exam (closed book, 2 hours)
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.
Data Analysis, Springer, 2012.
Max Bramer, Principles of Data Mining, Springer, 2013.
Michael R. Berthold, David J. Hand, Intelligent Data Analysis: An Introduction, Springer, 2007.
Paolo Giudici, Silvia Figini, Applied Data Mining for Business and Industry, 2nd Edition, 2009.
Gerhard Svolba, Data Quality for Analytics Using SAS, SAS Institute, 2012
Frank J. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Wiley, 2012
Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins and Nina Kruschwitz, Big Data, Analytics and the Path from Insights to Value, MITSloan Management Review, Vol.52, No.2, 2011.
INFORMS Analytics Magazine, http://www.analytics-magazine.org/
|Scheduled activity hours|
|Practical classes & workshops||10|
|Supervised time in studio/wksp||10|
|Independent study hours|
|Julia Handl||Unit coordinator|
Informal Contact Method
- Office Hours
Online Learning Activities (blogs, discussions, self assessment questions)
"I’ve always felt welcome – the staff here offer endless encouragement and support. I’ve had a really good time in Manchester and I think that the University, together with Alliance MBS, has contributed greatly to my experience. This course was quite demanding, but with enough free time to get to enjoy new friends as well as personal hobbies."