| Unit name | Practical Data Science for Economists |
|---|---|
| Unit code | ECON20008 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Davies |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) | |
| Units you must take alongside this one (co-requisite units) |
For students taking this unit from BSc Economics
|
| Units you may not take alongside this one |
None |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
The spread of technology means that large amounts of data can be accessed through the internet from a desktop computer. This data ranges from real-time measures of economic activity to voting patterns, to local measures of pollution.
This unit will provide students an understanding of how to use techniques, such as web-scraping, to automatically access, and update data-sets built upon large, publicly accessible data to answer key empirical questions.
How does this unit fit into your programme of study?
This unit is available to students on the BSc Economics programme in second year, and as a single second-year option for students in the final year of joint honours programmes.
For BSc Economics students, this unit will build upon the material you have studied in Economic Data in year 1. For all students, this unit will provide the opportunity to apply the empirical methods you have developed in the econometrics in years 1 and 2 of your undergraduate programme.
Overview of content.
This unit explores ways of using large data sets to better understand the societies in which we live. The unit combines methods from programming and economics to work on real world problems.
Students will use Python to access data from on-line sources, GitHub to create on-line repositories, Python or STATA statistical package to analyse and manipulate data, before visualising their final piece of work using HTML, CSS and JavaScript as a live and interactive web page.
Topics include:
Students will apply concepts of descriptive data analysis as well as econometric techniques learned in the compulsory econometrics courses.
How will students, personally, be different as a result of the unit?
The skills developed in this unit are directly applicable to the workplace; students will develop an understanding of the sorts of data that are available, and techniques to be able to automatically access the data to maintain up-to-date data sources.
Learning outcomes
Students will be able to:
Teaching will be delivered through a combination of large and small group classes, supported by online resources
Tasks which help you learn and prepare for summative tasks (formative)
Tasks which count towards your unit mark (summative)
This project will consist of a portfolio of skills and a data analysis project. Each element assesses all intended learning outcomes.
When assessment does not go to plan
If students fail the unit such that credit points cannot be awarded at the first attempt, they will normally be provided reassessment in the failed element(s).
The reassessment tasks will be:
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECON20008).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
