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MKT46240

Academic Year 2026/2027

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

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Collaboration

 The ability to learn from others; to understand and respect the needs, perspectives and actions of others (empathy); to understand, relate to and be sensitive to others (empathic leadership); to deal with conflicts in a group; and to facilitate collaborative and participatory problem solving. Not addressed

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Proficient

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Expert

Strategic

 The ability to collectively develop and implement innovative actions that further sustainability at the local level and further afield. Not addressed

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Not addressed

Self-awareness

 The ability to reflect on one’s own role in the local community and (global) society; to continually evaluate and further motivate one’s actions; and to deal with one’s feelings and desires. Competent

Normative

 The ability to understand and reflect on the norms and values that underlie one’s actions; and to negotiate values, principles, goals, and targets, in a context of conflicts of interest and trade-offs, uncertain knowledge and contradictions. Proficient

Anticipatory

 The ability to understand and evaluate multiple scenarios for the future – possible, probable and desirable; to create one’s own visions for the future; to apply the precautionary principle; to assess the consequences of actions; and to deal with risks and changes. Proficient

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Expert

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Expert

Wellbeing

 Wellbeing is having the resources and skills to meet life's challenges, including attributes such as personal development skills, resilience, stress management, strengths, lifestyle skills, nutrition, physical activity, sleep, alcohol/substance use, academic skills, time management, goal setting, interpersonal skills, group work, communication. Advanced Beginner

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

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 6 Alternative linear conversion grade scale 40% No
50
 No
Exam (In-person): Final 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) - 43, 44, 45 Mon 10:00 - 12:50
Summer Lecture Offering 51 Week(s) - 43, 44 Thurs 10:00 - 12:50
Summer Lecture Offering 51 Week(s) - 43, 44, 45 Tues 10:00 - 12:50
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