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MKT46240

Academic Year 2025/2026

Advanced Analytics & Big Data (MKT46240)

Subject:
Marketing
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr David DeFranza
Trimester:
Summer
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. Since much of this newly generated data captures the thoughts, opinions, and behaviors of individual consumers, marketers play an important role in addressing these challenges. Indeed, businesses capable of extracting knowledge from large data sets can achieve a considerable competitive advantage, and marketers capable of facilitating this process will find they have an advantage in the job market. In this module, we will engage with business problems, specifically those which might be faced by marketers, 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 manage and mine such data.

About this Module

Learning Outcomes:

1. Explain the unique characteristics of and challenges posed by Big Data
2. Apply industry standard best practices for the organization and documentation of large datasets
3. Summarize the data mining process within the context of a business problem
4. Identify an appropriate analysis method based on a description of the business problem and available data
5. Assess the performance of a model or analysis based on common diagnostic metrics
6. Explain foundational data mining methods and machine learning algorithms
7. Implement analysis methods using Excel, Python, and AI tools

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

144
Total

168

Approaches to Teaching and Learning:
This is a fast-paced class that will be delivered through a combination of lectures, case discussions and in-class exercises. The class heavily depends on students' preparation. It is vital to have read the cases and assigned materials prior to the class.

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): Continuous assessment including coding/analysis assignments and case analysis. Week 1, Week 2, Week 3, Week 4 Alternative linear conversion grade scale 40% No
50
 No
Exam (In-person): Final MCQ exam. End of trimester
Duration:
1 hr(s)
 Alternative linear conversion grade scale 40% No
50
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Continuous assessment assignments will receive either individual feedback or group/class feedback, post assessment. Final MCQ exam will receive online automated feedback.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Summer Lecture Offering 51 Week(s) - 40, 41, 42 Fri 10:00 - 12:50
Summer Lecture Offering 51 Week(s) - 40 Mon 10:00 - 12:50
Summer Lecture Offering 51 Week(s) - 40, 42 Tues 14:00 - 16:50
Summer Lecture Offering 51 Week(s) - 42 Wed 10:00 - 12:50
Summer Lecture Offering 51 Week(s) - 41 Wed 14:00 - 16:50
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