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 Dr KS Lam
Operations and Supply Chain Management
hugolam@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2019-20 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 12

 12

         24
Timetable (if known)              
Private Study 126
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
Assignment Report There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :2  3500 words    100       

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

Teaching Method 1 - Lecture
Description: 6 x 2 hour lectures
Attendance Recorded: Not yet decided
Notes: Lectures will be presented by academic members of staff across relevant areas and also by industry practitioners in the area of Big Data Analytics, Management and Systems.

Teaching Method 2 - Seminar
Description: 6 x 2 hour seminars
Attendance Recorded: Not yet decided
Notes: Computer lab-based seminars will introduce Big Data Analytics tools, such as Apache Hadoop and Terracotta (SOFTWARE AG).

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 such as Hadoop Distributed File System, HBase, Amazon S3;

MapReduce
Introducing the functions, specifications and algorithm design;

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.