MM333: Advanced Data Analytics for Business

Module code: MM333

Module provider: Digitalisation, Marketing and Entrepreneurship; Henley Business School

Credits: 20

ECTS credits: 10

Level: 6

When you’ll be taught: Semester 1

Module convenor: Dr Nico Biagi, email: nicolo.biagi@henley.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE MM1F28 (Compulsory)

Co-requisite module(s):

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

Module(s) excluded:

Placement information: NA

Academic year: 2026/7

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 25 March 2026

Overview

Module aims and purpose

The module aims to: 
• Enhance students’ statistical literacy and their ability to critically evaluate and apply statistical techniques in business contexts. 
• Enhance students’ ability to apply advanced methods including linear regression, logistic regression, moderation and mediation analysis, ANOVA (two-way and n-way), repeated measure ANOVA, multilevel modelling (MLM), and principal component analysis (PCA). 
• Enable students to apply these techniques to solve practical business problems and communicate findings effectively. 

Module learning outcomes

By the end of the module, it is expected that students will be able to: 
1. Demonstrate knowledge and understanding of advanced statistical concepts, techniques, and software. 
2. Select and apply appropriate methods to analyse complex business data.  
3. Evaluate assumptions, model fit, and results in context. 
4. Communicate findings in a business-appropriate and visually effective manner. 

Module content

The module covers the following topics: 

• Refresher on foundational statistics and data preparation 
•  Multiple linear regression: model building, diagnostics, and interpretation 
•  Logistic regression: model building, diagnostics, and interpretation 
•  Moderation and mediation analysis 
•  Two-way and n-way ANOVA, including interaction effects 
•  Repeated Measure ANOVA and Multilevel modelling (MLM)  
•  Factor analysis, with a focus on Principal Component Analysis (PCA) 
•  Model assumptions, evaluation of fit, and reporting standards in business analytics 
•  Communicating statistical insights using tables, graphs, and narratives 

Structure

Teaching and learning methods

The module will be delivered through a combination of lectures and practical workshops within each two-hour session, enabling students to acquire key concepts and practical skills in advanced statistical techniques. Data sets related to business problems will be provided as case studies to the students, who will then apply their learning to form data-driven recommendations. Solutions will be released in due course. Classes will be conducted in a partially flipped learning style, and students are encouraged to engage with material prior to coming to the lectures.

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

Students need to achieve an overall module mark of 40% to pass this module.

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 a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): 10% of the total marks available for that piece of work will be deducted from the mark for each calendar day (or part thereof) following the deadline up to a total of three calendar days;
• where the piece of work is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in you Individual Learning Plan), 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 calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan), no penalty shall be imposed;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three calendar days of the deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): no penalty will be applied;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a grade of Fail will be awarded.

Where a piece of work is submitted late after a deadline which has been revised owing to an extension granted through the Assessment Adjustments policy and process (self-certified or otherwise), it will be subject to the maximum penalty (i.e., considered to be more than three calendar days late). This will also apply when such an extension is used in conjunction with a DAS-agreed extension as a reasonable adjustment.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/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.

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. 

Exercises are given to students through Blackboard. The solutions will be covered in class. Revision sheets are also provided, and students are expected to complete them and ask questions.

Reassessment

Additional costs