Module Specification

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.
1. Module Title Big Data Analytics
2. Module Code COMP336
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr DK Wojtczak Computer Science D.Wojtczak@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 36

  12

      48
17.

Private Study

102
18.

TOTAL HOURS

150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

COMP116 Analytic Techniques for Computer Science
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. Aims
 

To introduce students to advanced methods and algorithms used in Big Data analytics.

To introduce students to software environments that enable developing solutions for Big Data problems.

To introduce students to implementing algorithms using such software environments.

 
28. Learning Outcomes
 

(LO1) Understanding of algorithmic approaches for Big Data analysis and handling batch and streaming data.

 

(LO2) Understanding of the software environments that can be used to enable algorithms to scale up to analysis of large datasets.

 

(LO3) Devising a most suitable algorithm for solving a Big Data problem.

 

(S1) Numeracy/computational skills - Reason with numbers/mathematical concepts at advanced level.

 

(S2) Communication (oral, written and visual) - Following instructions/protocols/procedures

 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Yes

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Yes

Teaching Method 3 - Software Lab
Description:
Attendance Recorded: Yes

 
30. Syllabus
   

Week 1: Introduction to Big Data, motivating real-world applications.
Week 2: Batch Analytics Part I.
Week 3: Batch Analytics Part II.
Week 4: Introduction to Network Science and Network Science algorithms for data analysis.
Week 5: Linear Algebra approaches and algorithms for data analysis.
Week 6: Introduction to key clustering algorithms and approaches.
Week 7: Introduction to probabilistic modelling of large datasets. Sampling techniques and exploratory analysis on large data sets.
Week 8: Scalable algorithms for analysing large datasets.
Week 9: Real-world applications and examples of using the above methods and algorithms.
Week 10: Introduction to Sequential Bayesian Inference and Bayesian approaches for data analysis including Kalman filter.
Week 11: Streaming Analytics
Week 12: Beyond separate batch and streaming analytics, challenges and advanced approaches to data analysis including data fusion.

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

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (336) Final Exam This is an anonymous assessment. Assessment Schedule (When) :Semester 1 exam period 120 60
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (336.1) Assessment 1 Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 (week 4) 18 20
  (336.2) Assessment 2 Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 (week 6) 18 20