Module Code:

PS938

Module Name:

Integrated Behavioural and Data Science

Module Credits (CATS):

15

Module Convener

Thomas Hills

Module Teachers

Thomas Hills

Module Aims

PS938 will bridge the gap between data science and behavioural science, giving students examples of what researchers do in the combined area of behavioural and data science. Students will hear presentations from leaders in the field and learn how this work was created, from inspiration to publication. Students will also learn how to frame research questions of their own in light of behavioural theory and apply data science methodologies to address these questions.

The aims of the module are as follows:

1) To help students understand the breadth of research in behavioural and data science;

2) To help students understand how to design and implement behavioural and data science research;

3) To help students to recognize cutting-edge research questions in behavioural and data science;

4) To give students the confidence and know-how to develop research projects of their own;

5) To give students experience in communicating research findings in written form and spoken presentations.

Learning Outcomes

By the end of the module, students should be able to:

• Understand the practice of behavioural and data science as a domain, how practitioners frame questions and approach answering them.
• Design and implement behavioural data science research, from inspiration to submitted publication.
• Recognize cutting edge questions in behavioural data science.
• Communicate findings from data science to non-data science audiences in written or spoken form.

Assessed by:

• All elements of the assessment
  
  
• Presentation, project report, and in-class tests
• All elements of the assessment
• Presentation and blog article

Module Work Load

Module Length

10 weeks

Lectures

10 lectures of 2 hours each

Seminars

5 seminars of 2 hours each

Attendance

Attendance at lectures and seminars is compulsory

Module Assessment

Assessed work:

Project report – theory-driven analysis of data written up as a short report for publication

Research presentation – group presentation, based on project work

Blog post – short blog post on research published in the behavioural and data sciences during the three months of the term

Weighting:

25%

25%

25%

Exams:

2 Class Tests – short- and medium-length answers, highest mark taken as final grade

Weighting:

25%

Module Programme

The syllabus includes 3 parts:

1. Presentations from leaders in the field discussing published work that students will read and research in advance.
2. Presentations around the various aspects of practicing data science, from idea generation to implementation and written communication.
3. Students presentations focused on project development.