Data Science in Economics
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.
Full module specification
|Module title:||Data Science in Economics|
|Duration of module:||
Duration (weeks) - term 2: |
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 Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled Learning and Teaching Activity||22||Lectures|
|Scheduled Learning and Teaching Activity||5||Tutorials|
|Guided Independent Study||123||Preparation for lectures, tutorials and assessments|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In Class Exercises||Fortnightly, in tutorials||1-6||Verbal/ELE|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Assignment||30||1 problem set with 10 questions||1-6||Verbal/ELE|
|Empirical Project||70||3000 words||1-6||Verbal/ELE|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Assignment||Assignment (30%)||1-6||August/September Reassessment Period|
|Empirical Project||Empirical Project (70%)||1-6||August/September Reassessment Period|
- 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.
Module has an active ELE page?
Last revision date