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Module Catalogue

EC349-15 Data Science for Economists

Department
Economics
Level
Undergraduate Level 3
Module leader
Nathan Canen
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry

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Introductory description

This introductory data science module will introduce core economics students to a wide array of data sources and types and how to work with them. It is intended to provide students with foundation data science skills, working in R.

Module aims

The module will introduce students to the meaning of data science, working practically with data in R. Students will learn how to source, manipulate and analyse large data flows, extract knowledge and insights from large, noisy data, and understand how to use these data types to answer certain economics questions. Students will learn to apply data science theorems and algorithms to solve problems using the most suitable software and statistical tools for data processing.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Topics typically could include, but are not limited to:

  1. Introduction: Defining data science, what data scientists do, the data they use, and the limitations of data science.
  2. The data science methodology (E.g., CRISM-DM, TDSP, Domino, etc.)
  3. Data sources and types – rectangular vs non-rectangular data (e.g., Textual data, multimedia data, spatial-temporal data, click stream data, etc.).
  4. Working with data in R
  5. Data extraction and acquisition
  6. Getting data into shape (mining, wrangling and manipulation)
  7. Statistical methods with big data
  8. Data visualisation and analysis
  9. AI Applications in Data Science (E.g., Supervised Machine learning, Unsupervised Machine Learning, Deep learning, etc.).
  10. Data science tools: (E.g., Working with Git, RStudio, Tidyverse, etc.)
  11. Data science application in economic analysis – Literature evidence.

Learning outcomes

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

  • Students will have an understanding of the data science methodology, the various data science tools available, and how to answer economic questions using various data types.
  • Students will learn to source for non-economic data, clean, manipulate, visualise, and analyse these data using programming techniques in the relevant software package (R) for real-world inspired scenarios.

Indicative reading list

  1. Grolemund, G. & Wickham, H. 2016. R for Data Science. Available online: https://r4ds.had.co.nz/index.html
  2. Hastie, T.; Tibshirani, R.; Friedman, J. H.; & Friedman, J. H. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). New York: Springer. Ebook and Print Book Available in Warwick Library
  3. James, G.; Witten, D.; Hastie, T.; & Tibshirani, R. 2021 (2nd Ed.). An Introduction to Statistical Learning with Applications in R. Springer. Available online: https://www.statlearning.com/
  4. Silge, J. & Robinson, D. 2017. Text Mining with R: A Tidy Approach. Available online: https://www.tidytextmining.com/
  5. Nosratabadi, S.; Mosavi, A.; Duan, P.; Ghamisi, P.; Filip, F.; Band, S.; Reuter, U.; Gama, J.; & Gandomi, A. 2020. Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), p.1799.
  6. Provost, F. & Fawcett, T. 2013. Data science and its relationship to big data and data-driven decision making. Big data, 1(1), pp.51-59.

Research element

Apply programming and data analysis skills to define and analyse economic and policy problems, formulate concepts and hypotheses, and show how they are tested in the policy context.

Interdisciplinary

The specific data science skills developed can also be extended beyond the economics discipline. Particularly, this module draws heavily from the field of programming and information technology, and interacts with the socioeconomic and political aspects of evaluating policy and practice.

Subject specific skills

Students will have the opportunity to develop skills in programming for data analysis; problem solving; computational economics; statistical modelling; analytical thinking, reasoning, and application; critical, creative and strategic thinking; mathematical computing skills; and data presentation and interpretation skills.

Transferable skills

Students will have the opportunity to develop: Data skills; Application of mathematics and statistics in economic analysis; IT skills; Written communication skills; Oral communication skills; Mathematical, statistical and data-based research skills.

Study time

Type Required
Lectures 10 sessions of 2 hours (13%)
Seminars 4 sessions of 1 hour (3%)
Demonstrations 1 session of 1 hour (1%)
Private study 125 hours (83%)
Total 150 hours

Private study description

Individual study will be required in order to prepare for seminars (which will be practical labs), review lecture notes, prepare for forthcoming assessments and examinations, and to undertake wider reading around the subject.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group D1
Weighting Study time Eligible for self-certification
Individual Project 40% No

Final economics-related data science project report in R with reproducible findings and well explained codes to be checked for plagiarism. Word count excludes codes.
Final Exam 60% No

A paper which examines the module content from both theoretical and technical perspectives and ensures learning outcomes are achieved

  • Students may use a calculator
Assessment group R1
Weighting Study time Eligible for self-certification
Resit Exam 100% No
  • Answerbook Green (8 page)
Feedback on assessment

The Department of Economics is committed to providing high quality and timely feedback to students on their assessed work, to enable them to review and continuously improve their work. We are dedicated to ensuring feedback is returned to students within 20 University working days of their assessment deadline. Feedback for assignments is returned either on a standardised assessment feedback cover sheet which gives information both by tick boxes and by free comments or via free text comments on tabula, together with the annotated assignment. Students are informed how to access their feedback, either by collecting from the Undergraduate Office or via tabula. Module leaders often provide generic feedback for the cohort outlining what was done well, less well, and what was expected on the assignment and any other common themes. This feedback also includes a cumulative distribution function with summary statistics so students can review their performance in relation to the cohort. This feedback is in addition to the individual specific feedback on assessment performance.

Past exam papers for EC349

Pre-requisites

EC203-30 Applied Econometrics OR
EC226-30 Econometrics 1

To take this module, you must have passed:

Courses

This module is Optional for:

  • Year 2 of TECA-L1PA Postgraduate Taught Economics (Diploma plus MSc)
  • UECA-3 Undergraduate Economics 3 Year Variants
    • Year 3 of L100 Economics
    • Year 3 of L116 Economics and Industrial Organization
  • UECA-4 Undergraduate Economics 4 Year Variants
    • Year 4 of L103 Economics with Study Abroad
    • Year 4 of LM1H Economics, Politics & International Studies with Study Abroad
  • Year 3 of UECA-LM1D Undergraduate Economics, Politics and International Studies
  • UPHA-L1CA Undergraduate Economics, Psychology and Philosophy
    • Year 3 of L1CA Economics, Psychology and Philosophy
    • Year 3 of L1CC Economics, Psychology and Philosophy (Behavioural Economics Pathway)
    • Year 3 of L1CD Economics, Psychology and Philosophy (Economics with Philosophy Pathway)
    • Year 3 of L1CE Economics, Psychology and Philosophy (Philosophy and Psychology Pathway)
  • UPHA-L1CB Undergraduate Economics, Psychology and Philosophy (with Intercalated Year)
    • Year 4 of L1CG Economics, Psychology and Philosophy (Behavioural Economics Pathway) (with Intercalated Year)
    • Year 4 of L1CH Economics, Psychology and Philosophy (Economics with Philosophy Pathway) (with Intercalated Year)
    • Year 4 of L1CJ Economics, Psychology and Philosophy (Philosophy and Psychology Pathway) (with Intercalated Year)
    • Year 4 of L1CB Economics, Psychology and Philosophy (with Intercalated Year)
  • Year 3 of USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
  • Year 3 of UMAA-GL11 Undergraduate Mathematics and Economics

This module is Option list A for:

  • Year 3 of USTA-Y602 Undergraduate Mathematics,Operational Research,Statistics and Economics

This module is Option list C for:

  • UPHA-V7ML Undergraduate Philosophy, Politics and Economics
    • Year 3 of V7MP Philosophy, Politics and Economics (Bipartite)
    • Year 3 of V7ML Philosophy, Politics and Economics (Tripartite)
  • UPHA-V7MM Undergraduate Philosophy, Politics and Economics (with Intercalated year)
    • Year 4 of V7MS Philosophy, Politics and Economics (Bipartite with Economics Major) (with Intercalated Year)
    • Year 4 of V7MQ Philosophy, Politics and Economics (Bipartite) with Intercalated Year
    • Year 4 of V7MM Philosophy, Politics and Economics (Tripartite) (with Intercalated year)

This module is Option list D for:

  • Year 3 of UPHA-V7ML Undergraduate Philosophy, Politics and Economics