INMR95: Business Data Analytics

Module code: INMR95

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 1

Module convenor: Dr Markos Kyritsis, email: m.kyritsis@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 19 November 2024

Overview

Module aims and purpose

This module introduces key concepts, methods, and tools for business data analytics. Data analytics are a fundamental tool for any organisation that plans to make strategic use of their data assets, and enables data-driven decision making. Core concepts that lend themselves to the three stages of data analytics (i.e., descriptive, predictive, and prescriptive) will be covered, including: data management; descriptive statistics; inferential statistics; exploratory data analysis; regression modelling; machine learning; programming data-driven solutions; and developing data-driven recommendations. Finally, workshops will give students experience in using an industry standard programming language.Ìý

To satisfy this general aim, students will acquire key knowledge and skills in:Ìý

1. Accessing, storing, and handling univariate and multivariate dataÌýÌý

2. Exploring and analysing dataÌýÌý

3. Visualising dataÌýÌý

4. Developing and comparing predictive modelsÌýÌý

5. Formulating data-driven decision-making strategiesÌý

Module learning outcomes

By the end of the module, it is expected that students will be able to:Ìý

1. Select and use appropriate statistical tools to analyse dataÌý

2. Demonstrate effective use of data visualisation techniquesÌý

3. Formulate data management strategies for business data analyticsÌý

4. Critically analyse a problem domain and apply the data analytics approach to support data-driven decision making

Module content

Outline content:Ìý

Data ManagementÌýÌý

1. Data types and sampling methodsÌýÌý

2. Storing, handling, and preparing data for analysisÌýÌý

3. Data visualisation TechniquesÌýÌý

Ìý

Descriptive AnalyticsÌýÌý

1. Descriptive StatisticsÌýÌý

2. Statistical InferenceÌýÌý

3. Exploratory Data AnalysisÌýÌý

Ìý

Predictive AnalyticsÌýÌý

1. Regression ModellingÌýÌý

2. Machine LearningÌýÌý

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Prescriptive AnalyticsÌýÌý

1. Programming for data analyticsÌýÌý

2. Recommendations and solutions developmentÌýÌý

Structure

Teaching and learning methods

This module will be a combination of lectures, seminar and practical workshops that will enable students to acquire key concepts and practical skills in data analytics. It assumes no prior knowledge or experience in data analytics, therefore students are expected to do a fair amount of wider reading. Data 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. The pedagogical approach used for this module is a partial flipped classroom paradigm where students are encouraged to engage with formative assessments prior to classes.Ìý

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

50% in coursework.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Online quizzes are made available to students every week to help ensure that they are keeping up with the course material. These are automatically marked and can be repeated as many times as needed. Students are encouraged to do the quizzes before coming to class in order to ask questions.Ìý

Reassessment

Additional costs