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 COMP529
Coordinator Dr M Amen
Computer Science
Bakhtiar.Amen@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2020-21 Level 7 FHEQ First Semester 15

Aims

To introduce the student to middleware often used in Big Data analytics.
To introduce the student to implementing algorithms using such middleware.

Learning Outcomes

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

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

(LO3) Understanding of the impact of the middleware on how algorithms are articulated.

(S1) Numeracy/computational skills - Reason with numbers/mathematical concepts

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

Syllabus
 

Week 1: Introduction to Big Data, motivating real-world applications and assumed dependencies (including discussion on Operating System)  
Week 2: Setting up Middleware for batch analytics.
Week 3: Introduction to Probabilistic Modelling of large datasets (eg Latent Dirichlet Allocation).
Week 4: Scalable algorithms for analysing large datasets (ie Bayesian Network algorithms).
Week 5: Porting such algorithms to the Middleware.
Week 6: Real-world applications of batch analytics.
Week 7: Setting up Middleware for Streaming Analytics with a specific focus on installing, IBM’s Infosphere Streams and adding a streaming operator.
Week 8: Introduction to Sequential Bayesian Inference.
Week 9: Algorithms for analysing streaming data (eg Kalman filter).
Week 10: Porting such algorithms to Streams.
Week 11: Real-world applications of streaming analytics.
Week 12: Beyond separate batch and streaming analytics.

Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Teaching Method 2 - Tutorial
Description:

Due to Covid-19, in 2020/21, one or more of the following delivery methods will be implemented based on the current local conditions.
(a) Hybrid delivery, with social distancing on Campus
Teaching Method 1 - Lecture
Description: On-line asynchronous lectures
Teaching Method 2 – Tutorial / Laboratory Work
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

(b) Fully online delivery and assessment
Teaching Method 1 - Lecture
Description: On-line asynchronous lectures
Teaching Method 2 - Tutorial / Laboratory Work
Description: On-line synchronous/asynchronous sessions

(c) Standard on-campus delivery with minimal social distancing.
As our planning has already gone too far, even if the campus opens up, we will offer hybrid teaching
Teaching Method 1 - Lecture
Description: On-line asynchronous lectures< br/>Teaching Method 2 - Tutorial / Laboratory Work
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

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
(529) Final Exam  120 minutes    60       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
(529.2) Assessment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1  18 hours    20       
(529.1) Assessment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :1  18 hours    20       

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