DBBA: Resource List

flittlet — Tue, 01 Aug 2023 16:43:12 +0000

DBBA: Resource List flittlet Tue, 01/08/2023 - 17:43

Data-driven Business and Behaviour Analytics Resource List

You can access the reading list for this course by selecting the link above. In order to view some resources on the list, you must be logged in with your EASE account.

For more information on getting the most out of your courses Resource List, have a look at this video.

ADDITIONAL NETWORK DATASETS
These are some datasets you can use to exercise but are not required for the course.

Stanford Large Network Dataset Collection

The KONECT Project

Network Data Repository | The First Interactive Network Data Repository (graphdatasets.com)

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DBBA: Course Materials

flittlet — Tue, 01 Aug 2023 16:43:10 +0000

DBBA: Course Materials flittlet Tue, 01/08/2023 - 17:43

Lecture Recordings

All lecture recordings should be accessed via Learn; you will need to log in using your EASE account. (Learn provides you with access to any lecture recordings available for this course. You will need to select the "lecture recording" link once, before you can access any direct links to a lecture recording.)

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DBBA: Data-driven Business and Behaviour Analytics

flittlet — Tue, 01 Aug 2023 16:43:10 +0000

DBBA: Data-driven Business and Behaviour Analytics flittlet Tue, 01/08/2023 - 17:43

Welcome to Data-driven Business and Behaviour Analytics

Learning Outcomes

On successful completion of this course, you should 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.

Course Outline

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.

Schedule and information

LECTURES

Tuesdays 15:10-16:00 (40GS_LG.11 40 George Square Lower Teaching Hub, Central)
Thursdays 15:10-16:00 (AT_Lecture_Theatre 1, Appleton Tower)
Fridays 15:10-16:00(AT_Lecture_Theatre 1, Appleton Tower)

TUTORIALS
***note: tutorials will start on week 3. Until then, the times may be subject to change to minimise clashes with other courses.***
Wednesdays 15:10-16:00 (LLTC_1.16 - Teaching Studio, Lister Learning and Teaching Centre, Central)

There will be no lectures and tutorials on week 7 and week 11, to let you focus on the coursework and the revision of the material.

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