Data Science and Decision Making

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Postgraduate: Level 7
Sunday 17 January 2021
Friday 26 March 2021
28 August 2019


Requisites for this module



Key module for

MSC L2I112 Social Data Science,
MSC G41212 Artificial Intelligence and its Applications,
MSC G412JS Artificial Intelligence and its Applications,
MSC G306JS Data Science and its Applications

Module description

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.

Module aims

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.

Module learning outcomes

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.

Module information


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

Learning and Teaching Methods This course consists of 50 contact hours consisting of 20 1-hour lectures and 10 3-hour labs


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Weekly Lab Assignments    15% 
Coursework   Project Proposal/Initial Report    15% 
Coursework   Final Project    70% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Haider Raza, email: h.raza@essex.ac.uk.
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



External examiner

Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
Available via Moodle
Of 50 hours, 20 (40%) hours available to students:
30 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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