Data Analytics for Business Decision Making

Unit code: BMAN60422
Credit Rating: 15
Unit level: Level 7
Teaching period(s): Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit?: N



Additional Requirements

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 MSc Advanced Computer Science and IT Management (SoCS)


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.



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.

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 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.

Assessment Further Information

50% Exam (closed book, 2 hours)

50% Coursework

Recommended reading

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,

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.

Study hours

  • Lectures - 20 hours
  • Practical classes & workshops - 10 hours
  • Supervised time in studio/wksp - 10 hours
  • Independent study hours - 110 hours

Teaching staff

Julia Handl - Unit coordinator

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