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Data Science in Economics

Module description

Data science has revolutionised many sectors in which economists work such as banking and finance. With that, the role of data scientists in such sectors has become increasingly important. Becoming a successful data scientist requires a combination of skills such as computer programming, statistical understanding and knowledge of the predictive algorithms. In this module, students will learn to apply some of the popularly used data science techniques that a student in an economics-related program need in order to become a full-fledged data scientist.
The assessment structure on this module is subject to review and may change before the start of the new academic year. Any changes will be clearly communicated to you before the start of term and if you wish to change module as a result of this you can do so in the module change window.

Full module specification

Module title:Data Science in Economics
Module code:BEE2041
Module level:2
Academic year:2023/4
Module lecturers:
  • Dr Damian Clarke - Lecturer
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 1:


Duration (weeks) - term 2:


Duration (weeks) - term 3:


Module aims

This module will enable students to obtain high-level understanding as well as strong hands-on experience in retrieving, munging, presenting and drawing inference from data using the most commonly used data science techniques.

A student undertaking this module should have a good grasp of probability, statistics, and linear algebra.

ILO: Module-specific skills

  • 1. Efficiently manipulate, retrieve, present, and make robust inference from data
  • 2. Critically evaluate alternative approaches for collecting, managing and analysing data representing complex systems

ILO: Discipline-specific skills

  • 3. Show proficiency in dealing with the most common data analysis and research methods used in data science
  • 4. Demonstrate the role of statistical evidence in business and economics

ILO: Personal and key skills

  • 5. Demonstrate logical problem solving skills
  • 6. Exemplify analytical thinking and independent study skills

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activity22Lectures
Scheduled learning and teaching activity5Tutorials
Guided independent study123Preparation for lectures, tutorials and assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class exercisesFortnightly, in tutorials1-6Verbal/ELE

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Assignment301 problem set with 10 questions1-6Verbal/ELE
Empirical project703000 words1-6Verbal/ELE

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
AssignmentAssignment (30%)1-6August/September reassessment period
Empirical projectEmpirical project (70%)1-6August/September reassessment period

Syllabus plan

  • Data Retrieval
  • Data Wrangling
  • Data (statistical) Inference
  • Intermediate-level Data Visualisation
  • Social Network Analysis

Indicative learning resources - Basic reading

Kazil, J., & Jarmul, K. (2016). Data wrangling with python: tips and tools to make your life easier. O'Reilly Media, Inc.

Molinaro, A. (2005). SQL Cookbook: Query Solutions and Techniques for Database Developers. O'Reilly Media, Inc.

Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.

Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets (Vol. 8). Cambridge: Cambridge university press.

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