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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Data-driven Business and Behaviour Analytics (INFR11198)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe 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.
Course description The course will be delivered through a combination of lectures and tutorials; students will be expected to complete both pencil-and-paper and programming-based exercises on their own time as well as during tutorials. Students will complete two projects to assess their practical and writing skills, and also sit an exam.

The topics in the course will be covered in two interconnected 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
* Financial networks

2) Agent-based modelling
* Rational and biased agents
* Modelling decision making with agents
* Calibration and validation of agent-based models
* Case studies in business, finance, and economics

Students will develop their critical thinking and problem-solving skills during tutorials. Some tutorials 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 real-world datasets 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 the courseworks, which will be similar to what covered in the tutorials.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Only available to Informatics MSc students on the Advanced Technology for Financial Computing or Data Science degrees.
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  30
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 25, Supervised Practical/Workshop/Studio Hours 15, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 156 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 50%
Practical Exam 0%
Coursework 50%
Feedback 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.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically analyse and explain human behaviour based on empirical observations.
  2. 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.
  3. Model and simulate realistic social systems with independent or interacting individuals.
  4. Discuss the legal and ethical implications of working with human-related data.
  5. Present (written/oral) highly interdisciplinary work in an understandable and comprehensive manner to people with different backgrounds.
Reading List
Menczer et al A first course in network science ¿ Cambridge University Press (2020)

Delli Gatti et al. Agent-based models in economics: a toolkit - Cambridge University Press (2018)
Additional Information
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
- Teamwork
- Verbal and written communication
- Cross-disciplinary communication
Special Arrangements Only available to Informatics MSc students on the MSc Advanced Technology for Financial Computing degree or the MSc Data Science degree.
KeywordsHuman behaviour,Social networks,Agent-based modelling,Data Science
Contacts
Course organiserDr Valerio Restocchi
Tel:
Email: V.Restocchi@ed.ac.uk
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk
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