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Introduction to Data Science in Economics

Module description

We are living in a data age. Businesses and governments are leveraging data to make better decisions. However, converting data into an actionable item is a challenge, particularly when there are multiple complex data sources and innumerable statistical methods and machine learning algorithms to choose from. To get insights from data in the current age 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.

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 at the start of the new 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:Introduction to Data Science in Economics
Module code:BEE1038
Module level:1
Academic year:2023/4
Module lecturers:
  • Dr Cecilia Chen - Convenor
Module credit:15
ECTS value:





BEE1022 or BEE1025

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


Duration (weeks) - term 2:


Duration (weeks) - term 3:


Module aims

This module will enable students to understand, apply and interpret findings from the commonly used data science techniques.

A student undertaking this module should have a good grasp of statistics.

ILO: Module-specific skills

  • 1. recognise the differences and similarities among various data science techniques using a variety of software.
  • 2. critically evaluate alternative approaches for collecting, managing and analysing data and how this data is used to support decision-making.

ILO: Discipline-specific skills

  • 3. recognise the most commonly used data analysis and research methods used in data science.
  • 4. demonstrate an understanding of the role of numerical 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
Schedule learning and teaching22 hoursLectures
Schedule learning and teaching5 hoursTutorials

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
Assignment and Empirical ProjectEmpirical Project1-6August/September reassessment period

Syllabus plan

  1. Introduction to data science
  2. Data preparation
  3. Data cleaning and integration
  4. Data manipulation
  5. Working with big data
  6. Basic programming skills

Note: The syllabus plan is tentative.

Indicative learning resources - Basic reading

Basic reading:

Grus, J. (2015) Data Science from Scratch, O’Reilly

Williams, G. (2017) One Page R – A Survival Guide to Data Science, Taylor & Francis Group (available online at

Williams, G. (2017) The Essentials of Data Science – Knowledge discovery Using R and Python, Taylor & Francis Group (available online at

Module has an active ELE page?


Origin date


Last revision date