Data Science and Decision Making
Computer Science and Electronic Engineering (School of)
Postgraduate: Level 7
Monday 13 January 2020
Friday 20 March 2020
28 August 2019
Requisites for this module
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 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.
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
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
||Weekly Lab Assignments
||Project Proposal/Initial Report
Module supervisor and teaching staff
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
Dr Robert Mark Stevenson
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).
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