Introduction to Data Science in Economics
| Module title | Introduction to Data Science in Economics |
|---|---|
| Module code | BEE1038 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Cecilia Chen (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
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.
Module aims - intentions of the module
This module will enable you to understand, apply and interpret findings from the commonly used data science techniques.
Those undertaking this module should have a good grasp of statistics.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 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
On successfully completing the module you will be able to...
- 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
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 and subject to change:
- Introduction to data science
- Data preparation
- Data cleaning and integration
- Data manipulation
- Working with big data
- Basic programming skills
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 27 | 123 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Schedule learning and teaching | 22 | Lectures |
| Schedule learning and teaching | 5 | Tutorials |
| Guided Independent Study | 123 | Self directed learning |
Formative assessment
| 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 |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Mid-term examination | 30 | 1 hour | 1-6 | Verbal/ELE |
| Empirical Project | 70 | Take home assignment (21 notional study hours) | 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 |
|---|---|---|---|
| Mid-term examination | Empirical Project | 1-6 | Referral/deferral period |
| Empirical Project | Empirical Project | 1-6 | Referral/Deferral period |
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 50%) 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 50%.
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 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/)
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | BEE1022 or BEE1025 or BEA1014 |
| NQF level (module) | 4 |
| Available as distance learning? | No |
| Origin date | 11/03/2019 |
| Last revision date | 13/06/2024 |
