Introduction to Data Science in Economics
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.
Full module specification
|Module title:||Introduction to Data Science in Economics|
BEE1022 or BEE1025
|Duration of module:||
Duration (weeks) - term 2: |
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 Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Schedule learning and teaching||22 hours||Lectures|
|Schedule learning and teaching||5 hours||Tutorials|
|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 and Empirical Project||Empirical Project||1-6||August/September reassessment period|
- Introduction to data science
- Data preparation
- Data cleaning and integration
- Data manipulation
- Working with big data
- Basic programming skills
Note: The syllabus plan is tentative.
Indicative learning resources - 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 http://togaware.com/onepager/)
Williams, G. (2017) The Essentials of Data Science – Knowledge discovery Using R and Python, Taylor & Francis Group (available online at https://essentials.togaware.com/)
Module has an active ELE page?
Last revision date