Unit information: Data Science in 2021/22

Unit name	Data Science
Unit code	ECON30006
Credit points	20
Level of study	H/6
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Professor. Davies
Open unit status	Not open
Pre-requisites	Econometrics 1 (EFIM20011) and Econometrics 2 (EFIM20036) OR Applied Quantitative Research Methods (EFIM20010)
Co-requisites	none
School/department	School of Economics
Faculty	Faculty of Social Sciences and Law

Description including Unit Aims

The spread of technology means that large amounts of data can be accessed from a desktop computer. This data ranges from real-time measures of economic activity to voting patterns, to local measures of pollution. 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, the STATA statistical package to analyse and manipulate data, before visualising their final piece of work as a live and interactive web page.

Topics include empirical strategy design, fetching and scraping data, data cleaning and storage, as well as the automation of all these tasks. Students will apply concepts of descriptive data analysis as well as econometric techniques learned in the compulsory econometrics courses.

Intended Learning Outcomes

Students will be able to:

1. Design. To design an empirical strategy, linking a socioeconomic question to an on-line data sources so that trends can be tracked, and hypothesis tested.
2. Automation. To create a re-usable algorithm using code that will fetch or scrape data from the web programmatically, building a novel database over time, and storing for analysis.
3. Analysis. To evaluate this data, in light of the empirical question under investigation, interpreting descriptive statistics and simple statistical methods to test socio-economic questions.
4. Visualisation. To communicate the results of this analysis in a transparent, interactive and accessible manner.

Teaching Information

The course will be taught using

• lectures
• large computer lab sessions
• drop-in clinics

There will be weekly advice and feedback hours.

Assessment Information

Portfolio of assessments, feeding into a live webpage, equivalent to 5 pages of A4 (100%) Assesses all learning outcomes

Resources

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. ECON30006).

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 Faculty workload statement relating to this unit for more information.

Assessment
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. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.