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MKT46090

Academic Year 2024/2025

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
• Summarize the industry standard 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
• Develop a comprehensive data analysis proposal which addresses a specific business problem

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

Not yet recorded.

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. 

Not yet recorded
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