Module overview
Having learnt the basic techniques and principles of business analytics in semester 1 modules, this module will introduce you to a number of advanced applications of business analytics in practice. These include data management using database engines, GLMs with embedded variable selection, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.
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:
- Solutions and technologies specifically designed for handling and extracting patterns from big data.
- Interpret the output of advanced analytics techniques used for complex data analytics applications.
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.
- Assess the relevance of statistical package outputs to the decisions being addressed.
- Identify the statistical models appropriate for analysing the various decisions with complex/big data.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Demonstrate an ability to use software for data analytics and to interpret its output.
- Critically analyse practical difficulties that arise when implementing advanced data analytics methods.
Syllabus
The topics covered include:
- Data Management: Introduction to databases, both traditional and unstructured, queries and SQL, Introduction to NoSQL and unstructured data management.
- Sources of different types of data (e.g. unstructured, network, etc.)
- Using advanced analytics model (e.g. advanced generalized linear models, random forests, deep learning, etc.) to conduct analysis with various types of data to solve different business problem settings
- Implementing Big data technologies in practice
The precise topics covered may change slightly in response to what is determined to be the most relevant based on academic and industry practice.
Learning and Teaching
Teaching and learning methods
The module is delivered through pre-course reading and both theory and applied lectures. The various concepts will be illustrated using real-life data and advanced software. In addition there will be some in-lecture exercises.
Type | Hours |
---|---|
Independent Study | 126 |
Lecture | 24 |
Total study time | 150 |
Resources & Reading list
Internet Resources
Textbooks
Hastie, T. Tibshriani, R. (2013). The Elements of Statistical Learning.
Chollet, F. (2017). Deep Learning With Python. Manning Publications.
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% |