MQM2BDA: Business Data Analytics

Module code: MQM2BDA

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: No placement specified

Academic year: 2024/5

Available to visiting students: No

Talis reading list:

Last updated: 19 November 2024

Overview

Module aims and purpose

Ultimately, the aim of this course is to provide students with the skills to (a) explore univariate and multivariate data sets and describe data architecture and structures using modelling and visualisation techniques, (b) formulate analysis questions and hypotheses and use statistical methods to support or reject these hypotheses, (c) develop statistical models and machine learning algorithms that support data-driven business decisions, (d) critically evaluate the results of analyses and successfully summarise them graphically using appropriate visualisation techniques.

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

• Exploring and visualising complex data sets

• Applying principles of data driven analysis using inferential statistics and statistical modelling

• Developing and comparing predictive models

• Forming data driven decisions to solve commercial problems

Module learning outcomes

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

1. Formulate analysis questions and hypotheses, and apply appropriate statistical methods to either support or reject the hypotheses

2. Argue in favour of a suitable methodology for a data-driven analysis given the specific nature of the data set and analysis question(s).

3. Describe how key algorithms and models are applied in developing analytical solutions, as well as how these solutions can be beneficial to organisations

4. Discuss when to use parametric and non-parametric tests in order to conduct high-quality complex investigations.

5. Discuss how machine learning algorithms and models are applied in developing analytical solutions to commercial problems

6. Reduce complexity of large data sets by applying dimension reduction techniques, and present the results in a way that supports human understanding of complex data sets

7. Summarise data using visualisation techniques, and discuss how this approach reduces complexity of information on which decisions can be based

8. Partition data sets and use both regression modelling and machine learning to generate data-driven solutions to commercial problems.

Evaluate and compare fitted models and select the most appropriate model based on its performance

Module content

The key themes of the module are:

1. Cleaning, summarising and visualising data sets

2. Inferring results from a sample to the population using parametric tests

3. Inferring results from a sample to the population using non-parametric tests

4. Reducing dimensionality of data sets

5. Building Regression models and evaluating their performance

6. Training Decision Trees and Random Forests and evaluating their performance

7. Documenting the results of the analysis and developing data-driven solutions

Structure

Teaching and learning methods

This module will be taught in a blended learning approach, which mostly includes directed self-study, undirected self-study, and workshops. It assumes no prior knowledge or experience in statistics, 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 everything they learned to form data-driven recommendations. The coding and analysis will be documented and submitted as part of a report that is worth 100% of their grade.

Study hours

At least 14 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

“Students need to achieve an overall module mark of 50% to pass this module.”  

Summative assessment

Penalties for late submission of summative assessment

This module is subject to the Penalties for late submission for Postgraduate Flexible programmes policy, which can be found at:

https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmissionpgflexible.pdf

The Module Convenor will apply the following penalties to work submitted late:

• where the piece of work is submitted up to one calendar month 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; 
• where the piece of work is submitted more than one calendar month after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

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.

Students will be given feedback on the progress of their individual project through tutorials and practical sessions. Online quizzes will be made available to help assess students’ understanding of the subject. These are for their own benefit and will be marked automatically. The grade of formative assessment will not contribute towards the overall module mark.

Reassessment

Additional costs