Module Details
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
Code COMP336
Coordinator Dr DK Wojtczak
Computer Science
D.Wojtczak@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 6 FHEQ First Semester 15

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.

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

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.

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

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 36

   12

       48
Timetable (if known)              
Private Study 102
TOTAL HOURS 150

Assessment
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       
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       

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