ULMS Electronic Module Catalogue

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Advanced Analytics for Business
Code ULMS885
Coordinator Professor IG McHale
Operations and Supply Chain Management
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ Second Semester 10

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):


Modules for which this module is a pre-requisite:


Programme(s) (including Year of Study) to which this module is available on a required basis:


Programme(s) (including Year of Study) to which this module is available on an optional basis:


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 10



Timetable (if known)              
Private Study 72


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Individual written assignment Marked anonymously There is a reassessment opportunity Standard University penalties apply    100       


This module aims to:

Provide students with an understanding of what analytics is, and what it is used for;

Enable students to critically evaluate reports and data analysis produced by different functional and operational specialists (e.g., marketing, operations) and financial analysts;

Provide students with the skills to identify how data and analytics can be used to support their decision making;

Enable students to articulate and explain results of big data analysis.

Learning Outcomes

(LO1) Students will be able to discuss the role of analytics and challenges of using analytics in an organisation.

(LO2) Students will be able to critically evaluate the application of data analytics to a particular problem.

(LO3) Students will be able to perform basic data processing, analytic and visualisation tasks.

(LO4) Students will be able to recognise the limitations of data analytics.

(S1) Deal with complex issues systematically and creatively.

Students will develop this skill through the interactive seminars presenting students with the tools and frameworks to work through complex organisational issues, using data analysis to reveal patterns and solutions. In the written assessment, students will be required to present their own creative solutions to problems, identifying how they developed their solution and its implications for an organisation.

(S2) Communicate clearly with specialist and non-specialist audiences.

The seminars will present students with a range of data challenges, revealing how different functional areas process and present data analysis, and how this affects their decision-making.

(S3) Act autonomously in studying further analytical tools and concepts.

The individual written assessment is designed to stretch students beyond the application of concepts and tools introduced in seminars, and read more broadly in advanced analytics, both with respect to theoretical research and practitioner journals. The individual written assessment will require students analyse a provided dataset and prepare a written report on their analysis.

(S4) Solve problems using appropriate tools.

The interactive design of the seminars emphasises experiential learning as a core element of the module. Seminars provide students with a mix of short exercises to first test their basic understanding of new tools and concepts, followed by extended case studies to facilitate repeated analytical challenges requiring students determine the strengths and limitations of different analytical tools. The two pieces of assessment then deepen these feedback loops, providing formative and summative insight into their developing capabilities.

Teaching and Learning Strategies

2 hour lecture x 5 weeks
3 hour seminar x 5 weeks
36 minutes asynchronous or peer to peer directed learning x 5 weeks
72 hours self-directed learning



Key topics:

Big data and analytics: an introduction;

Data analytics software, including R/RStudio;

Visualising data with R;

Techniques for data analytics: machine learning (classification and clustering), regression;

Case studies, including from different sectors including, for example, aviation, sport and health management, will be used throughout the module.

Materials will be made available to students via VITAL and students will also be expected to read additional materials from the suggested and required reading list, using online library resources.

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.