Data Science and Decision Making
Computer Science and Electronic Engineering (School of)
Postgraduate: Level 7
Monday 15 January 2024
Friday 22 March 2024
14 February 2023
Requisites for this module
MSC G400CH Advanced Computer Science,
MSC G411CH Artificial Intelligence,
MSC H612CH Computer Engineering,
MSC H610CH Electronic Engineering,
MSC L2I112 Social Data Science
This is a research-led, MSc level course, where students are expected to develop a complete end-to-end data science application. The course blends data analysis, decision making and visualisation with practical python programming. The module assumes a reasonable programming and machine learning background and is not suitable for students without prior programming or machine learning experience.
The aims of this module are:
- To familiarise students with the whole pipeline of processing, analysing, presenting and making decisions using data.
- To equip students with the theoretical tools and practical understanding necessary to create end-to-end data science applications, all the way from the initial concept to final deliverable.
By the end of this module, students will be expected to be able to:
- Understand the basics of the python data science stack (Pandas, Numpy, Sklearn).
- Complement their statistical knowledge with resampling statistics (cross-validation, permutation tests, bootstrapping).
- Incorporate artificial intelligence and machine learning knowledge within data science.
- Be able to visualise and present models and data interpretations.
- Have a complete data science application available in an open source repository. Example application domains include, but are not limited to, data journalism, question answering, recommender systems, policy making, marketing and music generation.
Introduction to data science and the Python data science ecosystem
Summary and resampling statistics, the bootstrap and permutation tests
End-to-end modelling with supervised learning
Storytelling with unsupervised learning and dimensionality reduction
Decision making with bandits
Deep learning for images and text, variational autoencoders and generative adversarial networks
Transfer learning and dealing with data shift
This module will be delivered via:
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list
Assessment items, weightings and deadlines
|Coursework / exam
||Weekly Lab Assignments
||Final Project Demonstration
||Final Project Code
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Module supervisor and teaching staff
Dr Ana Matran-Fernandez, email: email@example.com.
Dr Ana Matran-Fernandez
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Dr Marios Angelopoulos
Available via Moodle
Of 759 hours, 0 (0%) hours available to students:
759 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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