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 2023-24 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
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual assignment. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment.  0    50       
Individual assignment. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment.  0    50       

Aims

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;

Demonstrate ability to apply current tools and techniques in suitable depth and at the appropriate level.

Learning Outcomes

(LO1) Analyse the role of big data analytics in an organisation;

(LO2) Identify tools and techniques for big data analytics;

(LO3) Perform basic big data processing and visualising tasks;

(LO4) Develop case studies on how big data science aids and hinders business intelligence.

(S1) Adaptability

(S2) Problem Solving Skills

(S3) Commercial Awareness

(S4) Organisational Skills

(S5) Communication Skills

(S6) IT Skills

(S7) International Awareness

(S8) Lifelong Learning Skills

(S9) Ethical Awareness

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.