Introduction to Business Analytics

Module description

This module will explore the role of information and analytics in supporting the development of strategies, and the practical techniques managers can use to design effective information flows.

Information is the lifeblood of business. Companies that manage information effectively can improve efficiency, be more responsive to market opportunities, achieve competitive advantage and operate more sustainably.  As businesses drive towards sustainable strategies, they are looking for better information to guide decisions.  A critical next step is to build information systems and data analytics capabilities that will turn raw data into actionable insights. This will enable companies to more effectively identify which actions are achieving their goals, detect risk or opportunity early, evaluate possible outcomes, allocate resources to achieve greatest returns, and measure the true impact of products.

Internationalisation:  the module will draw on recent scholarship in the areas of data and analytics published by researchers internationally (the UK, Europe, the United States) and case studies based on a variety of national contexts.

Employability: the module will offer an opportunity to acquire knowledge and develop analytical skills for those pursuing careers in planning and analytics.

Full module specification

Module title:Introduction to Business Analytics
Module code:BEM2031
Module level:2
Academic year:2019/0
Module lecturers:
  • Jesse Fagan - Convenor
Module credit:15
ECTS value:

7.5

Pre-requisites:

BEE1025 Statistics for Business and Management or BEE1022 Introduction to Statistics or BEA1012 Introduction to Statistics for Accountants or MTH1004 Probability, Statistics and Data or MTH2006 Statistical Modelling and Inference

Co-requisites:
Duration of module: Duration (weeks) - term 2:

11

Module aims

The module aims to enhance your understanding of the application of data in organisations, and to start the process of building your capability in designing, structuring, and analysing data.

Specifically we will consider:

  • How businesses use data to build, understand and report on their activities
  • How to apply current concepts in data and analytics to real examples
  • The use of ‘Design Thinking’ to create information management systems
  • The initial tools for analysing numbers and text

ILO: Module-specific skills

  • 1. Critically evaluate current approaches used for collection, management, communication and analysis of commercial, operational and sustainability data, and how this data is used to support decision-making.
  • 2. Apply “Design Thinking” techniques to the analysis of a specific business challenge and use these to identify required information flows.
  • 3. Use data visualisation techniques to share original content and insight with a general management audience
  • 4. Demonstrate familiarity with analytical tools available for the analysis of numerical and textual data and use these to find, derive and evaluate information.
  • 5. Discuss current developments and thinking in the information management industry, specifically around big data management, analytics, cloud and visualisation techniques.

ILO: Discipline-specific skills

  • 6. Describe key terms and concepts in data and information management and be able to apply these to a typical business situation

ILO: Personal and key skills

  • 7. Critical and reflective thinking.
  • 8. Demonstrate effective independent study and research skills

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
241260

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Workshops11 x 2 hoursLecture / Workshop
Guided Independent Study20Lecture / Workshop
Guided Independent Study40Preparatory reading prior to workshops and lectures
Guided Independent Study20Practice use of software and concepts from additional exercises and examples
Guided Independent Study24Individual reading and study time for development of report critique.

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Review of individual performance on group exercisesDuring workshops / tutorialsn/aVerbal
Outline plan for assessed reportOne pagen/aElectronic/Verbal

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Practical (take-home) coursework exercise15Approx. 2 hours duration1-8Electronic, written comments
Practical (take-home) coursework exercise15Approx. 2 hours duration1-8Electronic, written comments
Individual report703,000 words1-8Electronic, written comments
0
0
0

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Practical (take-home) coursework exerciseApprox. 2 hours duration1-8August resit period
Practical (take-home) coursework exerciseApprox. 2 hours duration1-8August resit period
Individual reportIndividual report1-8August resit period

Syllabus plan

  • Introduction to key concepts in data and analytics and their application to business
  • Practical aspects of data management
  • Applications of analytics 
  • Introduction to analytical tools, for example R / Python / SQL
  • Introduction to data visualisations

Indicative learning resources - Basic reading

A full reading pack is supplied to students for this module (on ELE)

Recommended book:

Provost, F. and Fawcett, T. (2013) Data Science for Business. Beijing: O'Reilly.

Seeing Theory. https://seeing-theory.brown.edu/

Web based and electronic resources:

R for Data Science: https://r4ds.had.co.nz/

R: https://www.r-project.org/

R-Studio: https://www.rstudio.com/products/rstudio/download/

R Swirl https://swirlstats.com/

Module has an active ELE page?

Yes

Origin date

23/03/2018

Last revision date

14/02/2019