Module overview
Linked modules
Pre-Reqs: MANG6556 OR MATH6182
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Interpret the output of advanced analytics techniques used for complex data analytics applications.
- Solutions and technologies specifically designed for handling and extracting patterns from big data.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Work with current software packages to create models using complex data sources.
- Identify the statistical models appropriate for analysing the various decisions with complex/big data.
- Assess the relevance of statistical package outputs to the decisions being addressed.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Critically analyse practical difficulties that arise when implementing advanced data analytics methods.
- Demonstrate an ability to use software for data analytics and to interpret its output.
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Lecture | 24 |
Independent Study | 126 |
Total study time | 150 |
Resources & Reading list
Internet Resources
Textbooks
Chollet, F. (2017). Deep Learning With Python. Manning Publications.
Hastie, T. Tibshriani, R. (2013). The Elements of Statistical Learning.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Feedback
- Assessment Type: Formative
- Feedback: Formative group feedback on students’ performance in in-lecture exercises will be provided verbally immediately after the exercises. If possible, students will use technological tools to answer questions in the exercises.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Coursework | 100% |