Big Data Analysis module (AC51011)
20
AC51011
Semester: 1
About the Module
This module looks at how we can store, manipulate and analyse big data. We define big data essentially as data that is high in volume, is captured at high velocity and contains high variety (in terms of structured and unstructured parts). We will look into the tools and techniques both for storage of this data, such as distributed databases and filesystems, and for processing this data, such as Apache Hadoop, Apache Spark, Apache Hive etc. A part of the module is also dedicated to programming in Scala, a functional language with built in concurrency that allows easy and rapid development of parallel data analytics.
Credit Rating
There are 20 SCQF points available on this module.
Module Timetable
| Week | Subject |
|---|---|
| 1 | |
| 2 | |
| 3 | |
| 4 | Introduction to Big Data |
| 5 | Infrastructure for Big Data Analytics |
| 6 | Distributed Databases |
| 7 | Database Presentations |
| 8 | Big Data Processing Frameworks |
| 9 | Concurrent and Distributed Functional Programming |
| 10 | Advanced Data Processing Frameworks |
| 11 | Revision |
Assessment and Coursework
Coursework counts for 40% of the final module mark.
Two class tests count for 40% of the final module mark.
The final 20% of the final mark is covered by weekly quizzes.
Assignments
Marking criteria are provided on My Dundee for all assignments so that you know what we are looking for when we are marking your coursework. Please ensure that you refer to these when completing assignments.
| Title | Week Given | Week Due | Effort Expected (hours) | Value (%) |
|---|---|---|---|---|
| NoSQL presentation | 4 | 7 | 20 | 10 |
| Big Data Processing | 8 | 12 | 20 | 20 |
| Hadoop essay | 8 | 12 | 10 | 10 |
Resources
All course material is available on My Dundee. This includes copies of lecture materials, practical exercises, and assignments. The reading list for this module can be accessed from My Dundee and provides recommended materials for completing the module.