Postgraduate Course: Data-driven Business and Behaviour Analytics (INFR11198)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||The course is an interdisciplinary introduction to the emerging field of quantitative behavioural analytics. Students will learn how to model human behaviour from data, by using a combination of mathematical and computational techniques. By combining theory and practice, this course will provide students with the necessary knowledge and skills to be able to independently draw insight from human-centred data in a broad range of contexts. Examples will be mainly drawn from finance and business, but could also include other areas such as healthcare and epidemiology.
The course will be delivered through a combination of lectures and workshops; students will be expected to complete both pencil-and-paper and programming-based exercises on their own time as well as during workshops. Students will complete a group project to assess their practical and writing skills, and also sit an exam.
The topics in the course will be covered in three sections, with indicative topics listed below:
1) Social Networks
* Introduction to network science
* Different types of social networks
* Metrics and communities
* Tools for network analysis
2) Agent-based modelling
* Rational and biased agents
* Modelling decision making with agents
* Case studies in business, finance, and economics
3) Data wrangling for human behaviour
* Sources of data
* Preliminary analysis and identification of best modelling options
* Twitter and social media
Students will develop their critical thinking and problem solving skills during workshops. Some workshops will involve pencil-and-paper exercises where students solve increasingly difficult problems (presented in a way similar to that of the exam) on network science, and mathematical modelling of human behaviour. In others, students will work on a dataset of their choice and will be guided through the whole process of modelling human behaviour from a practical point of view, applying the notions learned during classes. The skills here acquired will be then assessed during a group coursework, which will be similar to what covered in the workshops.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Only available to Informatics MSc students on the Advanced Technology for Financial Computing degree.
Course Delivery Information
|Academic year 2020/21, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 25,
Supervised Practical/Workshop/Studio Hours 15,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 50%
Practical Exam 0%
||Students will receive feedback from lecturer/tutors during tutorials, during which they will be presented with both pen-and-paper and coding exercises which will be similar to the exam and the coursework, respectively.
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
On completion of this course, the student will be able to:
- Critically analyse and explain human behaviour based on empirical observations.
- Apply a range of mathematical and computational modelling techniques to human-related data and decide which one is the most appropriate for a specific task.
- Model and simulate realistic social systems with independent or interacting individuals.
- Discuss the legal and ethical implications of working with human-related data.
- Present (written/oral) highly interdisciplinary work in an understandable and comprehensive manner to people with different backgrounds.
|Barabasi "Network Science" 2016 - Cambridge University Press |
Newman "Networks: an introduction" 2nd ed 2018 - Oxford University Press
Tesfatsion, Judd "Handbook of Computational Economics - Vol. 2: Agent Based Computational Economics" 2006 - North-Holland
|Graduate Attributes and Skills
||During this course, students will develop a number of personal attributes/generic transferrable skills, including, but not necessarily limited to:
- Problem solving
- Critical thinking
- Analytical thinking
- Information elicitation
- Information filtering
- Decision making
- Independent learning
- Verbal and written communication
- Cross-disciplinary communication
||Only available to Informatics MSc students on the Advanced Technology for Financial Computing degree.
|Keywords||Human behaviour,Social networks,Agent-based modelling,Data Science
|Course organiser||Dr Valerio Restocchi
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701