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MSc Business Analytics: Operational Research and Risk Analysis
MSc Business Analytics: Operational Research and Risk Analysis

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

Year of entry: 2019

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, predictive modelling, pattern discovery, 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 MSc Advanced Computer Science and IT Management (SoCS)

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 classification, clustering, predictive modelling, text mining, and visual analytics. Emphasis will be placed on the use of an industry-leading software tool, SAS.

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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 methods

50% Exam (closed book, 2 hours)

50% Coursework

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

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/

Study hours

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

Teaching staff

Staff member Role
Julia Handl Unit coordinator

Additional notes

Informal Contact Method

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

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