CE888-7-SP-CO:
Data Science and Decision Making
2020/21
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Sunday 17 January 2021
Friday 26 March 2021
15
21 July 2020
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC L2I112 Social Data Science,
MSC G41212 Artificial Intelligence and its Applications,
MSC G412JS Artificial Intelligence and its Applications
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 background and is not suitable for students without prior programming experience.
The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decisions using data.
The aim of this module is 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.
After completing this module, students will be expected to:
1. Understand the basics of the python data science stack (Pandas, Numpy, Sklearn) and Apache Spark.
2. Complement their statistical knowledge with resampling statistics (cross-validation, permutation tests, bootstrapping).
3. Incorporate artificial intelligence and machine learning knowledge within data science.
4. Be able to visualise and present models and data interpretations.
5. 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.
Syllabus
1. Introduction (2 hours)
* **Week's lab: warming up: online twitter sentiment analysis (3 hours)**
2. The outside world (I) - data sources and their integration (2 hours)
* **Week's lab: pandas - manipulating datasets in-memory (3 hours)**
3. The outside world (II) - the special case of big data (2 hours)
* **Week's lab: setting up and integrating apache spark (3 hours)**
4. From data and observations to models
* **Week's lab: practical ML: scikit-learn**
5. Practical aspects of model building (I) (2 hours)
* **Week's lab: XGBoost: boosting and trees (3 hours)**
6. Practical Aspects of Model Building (II) (2 hours)
* **Week's lab: keras: regression and deep learning (3 hours)**
7. Dealing with model uncertainty (I) experiments and bandits (2 hours)
* **Week's lab: bandits and contextual bandits (3 hours)**
8. Dealing with model uncertainty (II): the others! (2 hours)
* **Week's lab: adversarial bandits and belief formation (3 hours) **
9. Going Live: deployment & visualisation (2 hours)
* **Week's lab: cherryPy, matplotlib and bokeh (3 hours) **
10. Generative processes & visualisation (2 hours)
* **Week's lab: learning to generate tweets with RNNs (3 hours) **
Learning and Teaching Methods
This course consists of 50 contact hours consisting of 20 1-hour lectures and 10 3-hour labs
- Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. (2016) Deep learning, Cambridge, Massachusetts: The MIT Press.
- Chollet, François. (2018) Deep learning with Python, Shelter Island, NY: Manning Publications Co.
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 |
Weekly Lab Assignments |
|
15% |
| Coursework |
Project Proposal/Initial Report |
|
15% |
| Coursework |
Final Project |
|
70% |
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, Dr Ana Matran-Fernandez
CSEE School Office, email: csee-schooloffice non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
No
Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
Available via Moodle
Of 2463 hours, 0 (0%) hours available to students:
2463 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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