CE880-7-AU-CO:
An Approachable Introduction to Data Science
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
15
17 June 2022
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G41212 Artificial Intelligence and its Applications
This is a module, designed to be taken by PGT students in the Faculties of Humanities and Social Sciences to give them skills in AI and Data Science developed through themes presented by relevant experts from CSEE, DMS, Philosophy, Law, ISER. It is aimed at strengthening the quantitative skill set of those faced with data challenges in their future professional life.
Students are not permitted to undertake this module as part of a Special Syllabus request as these are bespoke modules with content that is created for specific pathways.
The aim of the module is to develop quantitative skills in the area of AI and Data Science to enable professional working in areas in which these topics are now being embedded. The module will enable those future professionals to take a knowledgeable approach to their use of AI and data science.
After taking this module, students will be expected to have the ability:
1. Work confidently with numbers and mathematical concepts
2. Critically evaluate the suitability of certain tools and use them to summarise, present, and compress data
3. Conceptually understand basic machine learning techniques and analyse their strengths and weaknesses
4. Assess evidence for causality
5. Formally analyse a decision making strategy
The use cases will be associated with one of 6 current planned themes. The suggested use cases within each theme are listed as possible examples.
1 - Introduction to quantitative thinking - theme: epistemology, truth, empiricism (Philosophy)
2 - Summarising, presenting and compressing data – theme: Pattern/concept formation in the brain (Philosophy/Psychology)
3 - Supervised and statistical learning – theme: Prediction, randomness and the scientific method (Philosophy)
4 - Causality – theme: Understanding social science data (Social Sciences, ISER)
5 - Ensemble Learning – theme: Group prediction and decision making (Psychology)
6 - Decision making and story telling – theme: Utility / value, optimal utility / value, happiness, reward (Philosophy)
Students will learn about the themes through an introduction to the theme and the relevant practical, both presented in pre-recorded lectures by relevant experts from the field of AI, Data Science, and theme discipline.
We aim at the lectures being delivered online, while the labs will be available in dual mode. 3-hour labs will take place weekly, and some assessment will be online through automated scripts. In the online version of the labs, students will interact with tutors/academics through zoom and forums, while in the off-line version labs will be delivered in CSEE labs. Moreover, a virtual laboratory is being prepared, where student will be able to log in from the homes and be able to work as if they were working on the laboratory computer. The students will also be asked to give an oral presentation to their peers and potentially asked to appraise their peers presentations.
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Mueller, J. and Massaron, L. (2019)
Python for data science for dummies. Second edition. Hoboken, NJ: For Dummies. Available at:
https://learning.oreilly.com/library/view/python-for-data/9781119547624/?sso_link=yes&sso_link_from=university-of-essex.
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Embarak, D.O. (2018)
Data analysis and visualization using Python?: analyze data to create visualizations for BI systems. 1st ed. Berkley: APress. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5601947.
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Kazil, J. and Jarmul, K. (2016)
Data Wrangling with Python. Sebastopol: O’Reilly Media, Inc, USA. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=4543981.
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Emmert-Streib, F. and Dehmer, M. (2019) ‘Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference’,
Machine Learning and Knowledge Extraction, 1(3), pp. 945–961. Available at:
https://doi.org/10.3390/make1030054.
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Sagi, O. and Rokach, L. (2018) ‘Ensemble learning: A survey’,
WIREs Data Mining and Knowledge Discovery, 8(4). Available at:
https://doi.org/10.1002/widm.1249.
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Liu, T., Ungar, L. and Kording, K. (2021) ‘Quantifying causality in data science with quasi-experiments’,
Nature Computational Science, 1(1), pp. 24–32. Available at:
https://doi.org/10.1038/s43588-020-00005-8.
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Ge
+?ron, A. (2019)
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. Second edition. Beijing: O’Reilly. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5892320.
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Shrestha, M.B. and Bhatta, G.R. (2018) ‘Selecting appropriate methodological framework for time series data analysis’,
The Journal of Finance and Data Science, 4(2), pp. 71–89. Available at:
https://doi.org/10.1016/j.jfds.2017.11.001.
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 |
Description |
Deadline |
Coursework weighting |
| Coursework |
Lab coursework |
|
60% |
| Coursework |
Case Study |
|
40% |
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.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Haider Raza, email: h.raza@essex.ac.uk.
Dr Haider Raza
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
Yes
Dr Colin Johnson
University of Nottingham
Dr Anthony Olufemi Tesimi Adeyemi-Ejeye
Available via Moodle
Of 42 hours, 20 (47.6%) hours available to students:
22 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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