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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.

Module aims - intentions of the module

This module will enable you 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.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 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

On successfully completing the module you will be able to...

• 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

On successfully completing the module you will be able to...

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

Syllabus plan

The following syllabus plan is indicative: 

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

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

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

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

Re-assessment notes

Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 40%

Indicative learning resources - Basic reading

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.

Key words search

Data Science, Data Manipulation, Data Analysis.