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MKT46090

Academic Year 2025/2026

Big Data Analytics (MKT46090)

Subject:
Marketing
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr David DeFranza
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Every day, an increasing variety of new information is created at a larger volume and faster velocity than ever before. This information presents incredible opportunities for businesses but contending with its size and speed poses considerable technical and strategic challenges. Indeed, businesses capable of extracting knowledge from large data sets can achieve a considerable competitive advantage, and managers capable of facilitating this process will find they have an advantage in the job market. In this module, we will engage with business problems using data analytic thinking. Along the way, we will discuss the fundamental principles guiding the extraction of knowledge from information, challenges posed by very large data sets (including “Big Data”), and some of the most common techniques and technologies used to mine such data.

About this Module

Learning Outcomes:

Learning Objectives
• Explain the unique characteristics of and challenges posed by Big Data
• Apply industry standard best practices for the organization and documentation of large datasets
• Summarize the data mining process within the context of a business problem
• Identify an appropriate analysis method based on a description of the business problem and available data
• Assess the performance of a model or analysis based on common diagnostic metrics
• Explain foundational data mining methods and machine learning algorithms

Student Effort Hours:
Student Effort Type Hours
Lectures

36
Specified Learning Activities

114
Autonomous Student Learning

100
Total

250

Approaches to Teaching and Learning:
• Lectures
• Peer and group work
• Active/task-based learning
• Case-based learning

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Complete a series of data analytics applications assignments. Week 4, Week 8, Week 14 Alternative linear conversion grade scale 40% No
50
 No
Exam (In-person): Closed-book exam, cumulative exam at the end of the term. End of trimester
Duration:
1 hr(s)
 Alternative linear conversion grade scale 40% No
50
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Group feedback will be posted to Brightspace and discussed in class. Exams may include MCQs for which feedback is automated.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 14:00 - 16:50
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