MS3S32 - Big Data Engineering and Applications 01 Sep 2021 - 31 Aug 2027 | Version 1
Associated Module Information
| Module Code: | MS3S32 | ||
|---|---|---|---|
| Module Title: | Big Data Engineering and Applications | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Moizzah Asif, Ian Fitzell | ||
| Module Team: | Penny Holborn, Angelica Pachon, Rebecca Peters, Ieuan Griffiths | ||
| First Intended Intake: | SEP 2021 | Final Year of Intake: | 2024 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 6 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | |||
| HECOS Code Weighting: | |||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Sep 2021 |
| Valid To | 31 Aug 2027 |
Module Aims
To provide an understanding of the key concepts and techniques associated with the collection, storage and processing of Big Data
To be able to choose between and use various available big data storage, processing, management tools and architectures as needed.
To learn how to choose appropriate methods to query large datasets in order to extract information that can be used in big data applications.
Content Summary
Understanding Characteristics of Data in Big Data Environments:
The basic Big Data characteristics, models and sources will be examined. Looking at how it is stored and processed, with a focus on the architecture, components, operation and tools.
Fundamental Terminology & Concepts:
Key ideas that underpin the theory and application of Big Data solutions will be explored such as: Big data programming models, querying strategies and query optimisation for big data, exploiting high performance computing and cloud computing to serve big data needs.
Big Data applications, processing and management:
The application of appropriate tools techniques and algorithms in order to transform and query datasets, use high performance and cloud computing for Big Data requirements. High level tools and programming techniques that are associated with Big Data storage, processing, management and applications will be introduced and used, including Hadoop, Spark, Pig, Hive, R, Python and others.
Adoption and Planning
Exploring Big Data. Solutions implemented for IoT such as SCADA systems, Semantic Big Data and Big-Graphs.
Challenges such as ?Privacy-Preserving Record Linkage for Big Data.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 10 |
| Practical classes and workshops | 10 |
| Supervised time in studio/workshop | 6 |
| Work based learning | 74 |
| Directed Study | 28 |
| Formative Assessment - Independent | 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 |
|---|---|---|---|---|---|---|---|
| Portfolio | Portfolio 1 | Portfolio | 0 | 6000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Portfolio 1 | ✔ | ✔ | |