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 Big Data Analytics for Business
Code EBUS633
Coordinator Professor KS Lam
Operations and Supply Chain Management
hugolam@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2024-25 Level 7 FHEQ Second Semester 15

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 20

     5

     25
Timetable (if known)              
Private Study 125
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Examination. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment.  3    50       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Group assignment. There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment.  0    50       

Aims

The module aims to:

Introduce techniques for big data analytics and their applications for very large datasets in different organisations and settings;

Enable students to demonstrate in depth understanding and knowledge of the concepts, theories and developments associated with the subject area, and be able to critically and analytically discuss outcomes in a methodological, structured, logical and in-depth manner;

Enable students to demonstrate the ability to apply current tools and techniques in suitable depth and at the appropriate level.

Learning Outcomes

(LO1) Students will be able to analyse the role of big data analytics in an organisation;

(LO2) Students will be able to apply tools and techniques for big data analytics;

(LO3) Students will be able to perform basic big data processing and visualising tasks;

(LO4) Students will be able to evaluate case studies on how big data science aids and hinders business intelligence.

(S1) Adaptability
Students will learn how Big Data Analytics develop and change quickly.

(S2) Problem Solving Skills
Students will learn how Big Data Analytics are used to solve different business problems.

(S3) Commercial Awareness
The applications of Big Data Analytics in different business contexts will be discussed.

(S4) Organisational Skills
Students need to work together to complete the group assignment.

(S5) Communication Skills
Students will participate in class discussion.

(S6) IT Skills
Students will learn to perform some Big Data Analytics tasks.

(S7) International Awareness
The international context for Big Data Analytics will be discussed.

(S8) Lifelong Learning Skills
Students need to learn different tools for Big Data Analytics.

(S9) Ethical Awareness
The ethics of Big Data Analytics will be discussed.

(S10) Teamwork
Students will develop their teamwork skills when undertaking the group assignment.

Teaching and Learning Strategies

2 hour lecture x 10 weeks
1 hour lab x 5 weeks
125 hours self-directed learning

Students will be expected to undertake independent research, guided reading and wider reading around the subject.

Syllabus
 

Big data analytics: an overview
Exploring the progression from big (data) science to business intelligence and the implications for research/practice;

Big data analytics: tools and technologies
Overview of data analytics capabilities of technologies;

Storing big data
Outline of data stores and redundant data;

Processing big data
Mapping, linking, transforming big data;

Clustering big data
Mean shift, hierarchical, K-Means;

Visualising big data
Management consoles, dashboards and reporting tools;

Big data analytics: trends and projections
Discussing the opportunities and limitations of big (data) science.

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