INMR95-Business Data Analytics

Module Provider: Henley Business School
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Email: m.kyritsis@henley.ac.uk

Type of module:

Ultimately, the aim of this course is to provide students with an understanding of how toÌýmanageÌýdata, analyse data,Ìýdevelop predictive models, and then useÌýpredictiveÌýmodels to develop future recommendations for business related problems. To satisfy this general aim, students willÌýacquire key knowledge and skills in:Ìý

• Accessing,Ìýstoring, and handlingÌýunivariate and multivariateÌýdataÌý


• Exploring and analysing dataÌý


• Visualising dataÌý


• Developing and comparing predictive modelsÌý


• FormulatingÌýdata-drivenÌýdecision makingÌýstrategiesÌý

Summary module description:

This moduleÌýintroducesÌýkey concepts,Ìýmethods, and tools for business data analytics. Data analyticsÌýareÌýa fundamental tool for any organisationÌýthat plansÌýto make strategic use of their dataÌýassets,ÌýandÌýenablesÌýdata-driven decision making.ÌýCore conceptsÌýthatÌýlend themselves to theÌýthree stagesÌýof data analytics (i.e., descriptive, predictive, and prescriptive) will be covered, including:Ìýdata management;Ìýdescriptive statistics; inferential statistics; exploratory data analysis; regression modelling; machine learning; programmingÌýdata-drivenÌýsolutions; and developing data-driven recommendations.ÌýThe workshops will give students experience in usingÌýan industry standardÌýprogramming language,Ìýas well as GUI-based tools,ÌýthusÌýproviding them with the opportunity to choose the most appropriate methodÌýfor their own future employability needs.Ìý

Aims:

Assessable learning outcomes:

On completion of this module, the student should be able to:Ìý

• Select and use appropriate statistical tools to analyse dataÌý


• Demonstrate effective use of data visualisationÌýtechniquesÌý


• Formulate data management strategiesÌýfor business data analyticsÌý


• CriticallyÌýanalyseÌýaÌýproblemÌýdomainÌýandÌýapply the data analytics approach toÌýsupportÌýdata-driven decision makingÌý

Additional outcomes:

The student should:Ìý

• Become familiar with the industry standard data analytics and visualisation toolsÌý


• Become familiar with concepts and tools in data managementÌý


• Become familiar with software development tools and approaches surrounding data analyticsÌý

Outline content:

Data ManagementÌý

1. Data types and sampling methodsÌý


2. Storing, handling, andÌýpreparingÌýdataÌýfor analysisÌý


3. Data visualisationÌýTechniquesÌý?

Descriptive AnalyticsÌý

1. Descriptive StatisticsÌý


2. Statistical InferenceÌý


3. Exploratory Data AnalysisÌý

Predictive AnalyticsÌý

1. Regression ModellingÌý


2. Machine LearningÌý

Prescriptive AnalyticsÌý

1. Programming for data analyticsÌý


2. Recommendations andÌýsolutions developmentÌý

Brief description of teaching and learning methods:

This module will be a combination of lectures, tutorialsÌýand practical workshopsÌýthat willÌýenable students to acquire key conceptsÌýand practical skillsÌýin data analytics. It assumesÌýno prior knowledge or experience in dataÌýanalytics,Ìýtherefore students are expected to do a fair amount of wider reading.ÌýData sets related to business problems will be provided as ‘case studies’ to individual students, who will then have to apply descriptive, predictive, and prescriptive analytics in order to form recommendations. The process will be documented and submitted as a report that is worth 100% of their grade.

Summative assessment- Examinations:

None

Summative assessment- Coursework and in-class tests:

InÌýSpring term, week 1, submissionÌýof an individualÌýreport ofÌý4,000 wordsÌýcomprising theÌýanalysis, model building, simulations, and recommendations for addressing the business question using a data-driven approach.ÌýÌý

Formative assessment methods:

Students will be given feedback on the progressÌýof theirÌýindividualÌýproject through tutorials and practical sessions.Ìý

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

50% in courseworkÌý

To pass the module, students must demonstrate satisfactory understanding of concepts as well as demonstration of basic use of tools for data analyticsÌýand interpretation of results.Ìý

For Merit level performance, students must demonstrate competence in formulating business data analytics solutions through suitable application of toolsÌýand critical appreciation of results.Ìý

ForÌýDistinction level performance, students must demonstrate a high level of competence in critical formulation of business data analyticsÌýsolutions and a highly competent application of appropriate tools and critical analysisÌýwhen interpreting theÌýresults.Ìý

Reassessment arrangements:

Resubmission of coursework report.Ìý

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Additional Costs (specified where applicable):

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Last updated: 8 April 2019