IS4S765 - Big Data and Analytics 01 Sep 2022 - 31 Aug 2028 | Version 2
Associated Module Information
| Module Code: | IS4S765 | ||
|---|---|---|---|
| Module Title: | Big Data and Analytics | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Cyber Security | ||
| Module Leader: | Paul Jarvis | ||
| Module Team: | Robert Berry | ||
| First Intended Intake: | Final Year of Intake: | ||
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100370 - information management | 100755 - data management | |
| HECOS Code Weighting: | 50 | 50 | |
Document Version Information
| Version | 2 |
|---|---|
| Valid From | 01 Sep 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
To provide an understanding of the key concepts and techniques associated with the collection and analysis of Big Data. To learn how to choose appropriate storage methods for datasets, as well as how to manage, process and query large datasets in order to extract information that can be used to inform decision making processes to meet business goals. To be able to plan and implement a secure data analytics system.
Content Summary
Understanding Characteristics of Data in Big Data Environments:
The basic Big Data types, characteristics and sources will be examined, looking at how it is stored, processed and analysed, with a focus on the architecture, components, operation and tools of Big Data.
Fundamental Terminology & Concepts:
Key ideas that underpin the theory and application of Big Data solutions will be explored such as the data analytics lifecycle, business intelligence, types of data analysis and key performance indicators (KPI).
Managing, Processing and Analysing Big Data:
The application of appropriate tools techniques and algorithms in order to transform and query datasets. Problem solving techniques will be examined and used to detect patterns and draw conclusions from data. High level tools and programming techniques that are associated with Big Data storage and analytics will be used, including Hadoop, Pig, Hive, Mahout, R and others.
Adoption and Planning:
Exploring how Big Data solutions are implemented in business contexts, with considerations to business justification, data procurement, governance requirements and performance challenges.
Analytics :
Analytics techniques such as:
- Classification trees
- Regression
- Clustering
- Association Rules
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To appraise and contrast strategies for dealing with Big Data |
| LO2 | To demonstrate an ability to apply Big Data concepts in non-trivial contexts |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Report 2 | A report on the application of data analytics techniques to appropriate datasets | 0 | 2000 | 50 | No | 40 |
| Asynchronous Assessment | Report 1 | A report that discusses Big Data storage and processing approaches and demonstrates appropriate data analysis | 0 | 2000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Report 2 | ✔ | ✔ | |
| Report 1 | ✔ | ✔ | |