| Unit name | Applied Data Science Project |
|---|---|
| Unit code | ECON30019 |
| Credit points | 40 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Professor. Wang |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
ECON20010 Intermediate Mathematical Methods for Economics and Data Science |
| Units you must take alongside this one (co-requisite units) |
None |
| 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 tools of data science are increasingly important within the workplace; this capstone unit allows you the opportunity to analyse authentic industry-relevant problems, using knowledge from both economics and data science.
How does this unit fit into your programme of study?
This capstone unit builds upon the core cornerstones of Data Science, Econometrics and Economic theory you have developed across your first two years of study. You will have the opportunity to apply the knowledge of programming, data analysis, and economic analysis to solve authentic real-world problems, inspired by employers.
Overview of content
The unit combines skills acquired in data science, economics and econometrics in order to analyse a specific research question. It provides a hands-on experience of analysing economic data. You will acquire experience in writing up results and producing a technical economic report. You will gain practical experience in using econometric software and in interpreting the obtained results. A range of authentic real-world problems, inspired by employers are available each year.
In phase 1, you will work in groups on a preliminary data project related to their main project. The groups will work independently, with support and guidance from their supervisors. Each group get the chance to understand their data and need to produce a technical group report. This phase also features taught sessions on conducting group work, research ethics, and data handling skills.
In phase 2, you will work individually on a substantive piece of independent work, building from their initial group report.
How will students, personally, be different as a result of this unit?
You will develop a range of skills and experience through this unit; in particular, you will gain employability-based skills, relating to collecting, cleaning, analysing and communicating data. In addition, you will gain experience of key employability related challenges, including working in groups, and data analysis.
Learning outcomes
By the end of this unit, you will be able to:
The dissertation is primarily driven by individual study. This will be supported by a combination of
Tasks which help you learn and prepare you for summative tasks (Formative)
Students will have the opportunity to discuss drafts of both the group- and individual-project with their tutor
Tasks which count towards your unit mark (summative)
Each individual will gain a mark for the group project, weighted by peer evaluation of contribution.
When assessment does not go to plan
Normally, there are no opportunities for reassessment in final-year units. Where students are taking this unit as a non final-year unit (such as an MSci), or where there are validated exceptional circumstances:
If students fail the unit such that credit points cannot be awarded at the first attempt, they will be given a reassessment to replace the failed element(s).
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. ECON30019).
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.
