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 |
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Year | CATS Level | Semester | CATS Value |
Session 2022-23 | Level 6 FHEQ | First Semester | 15 |
Aims |
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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 |
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(LO1) Understanding of algorithmic approaches for Big Data analysis and handling batch and streaming data. |
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(LO2) Understanding of the software environments that can be used to enable algorithms to scale up to analysis of large datasets. |
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(LO3) Devising a most suitable algorithm for solving a Big Data problem. |
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(S1) Numeracy/computational skills - Reason with numbers/mathematical concepts at advanced level. |
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(S2) Communication (oral, written and visual) - Following instructions/protocols/procedures |
Syllabus |
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Week 1: Introduction to Big Data, motivating real-world applications. |
Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Teaching Method 2 - Tutorial Teaching Method 3 - Software Lab |
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 |
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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 |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |