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 Management
Code EBUS622
Coordinator Dr Y Zhan
Operations and Supply Chain Management
Yuanzhu.Zhan@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First 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

 6

       12

6

 36
Timetable (if known) 60 mins X 1 totaling 12
  		60 mins X 1 totaling 6
  		      60 mins X 1 totaling 12
60 mins X 1 totaling 6
  		 
Private Study 114
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.  24 hours    40       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual report There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment.  -2000 words    60       

Aims

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

To demonstrate ability to answer specific questions on the subject area fully, critically, analytically in suitable depth and at the appropriate level.

Learning Outcomes

(LO1) Understanding what Big Data is and its relevance to Business.

(LO2) Recognise potential for use of Big Data analytics and output to Business areas, such as Marketing and Operations.

(LO3) Identify new business opportunities and business models for adopting Big Data initiatives, and challenges associated with their implementation.

(LO4) Understanding the legal and wider ethical issues involved in the gathering and use of personal information associated with Big Data, such as from Social Media applications and internet websites.

(LO5) Have some knowledge of systems and tools used for data mining and analysis.

(S1) Adaptability

(S2) Problem solving skills

(S3) Commercial awareness

(S4) Organisational skills, communications skills

(S5) IT skills

(S6) International awareness

(S7) Lifelong learning skills

(S8) Ethical awareness

Teaching and Learning Strategies

Hybrid delivery, with social distancing on campus.

1 hour online asynchronous learning per week x 12 weeks
1 hour online synchronous lecture per week x 12 weeks
1 hour face-to-face seminar every other week x 6 weeks
1 hour face-to-face peer-to-peer learning every other week (unscheduled) x 6 weeks
Self-directed learning x 114 hours

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

Syllabus
 

Introduction to big data management;

Big data analytics for competitive advantages;

Application of big data to support managerial decision-making;

Data management and organisational competencies to deploy big data;

Big data innovation: Artificial Intelligence, Machine learning, etc.;

Big data strategies for businesses.

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