University home > Unit and programme catalogues in 2023/24 > Programme catalogue > Faculty of Social Sciences and Law > School of Economics > Economics with Data Science (MSc) > Specification
Programme code | 9ECON001T |
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Programme type | Postgraduate Taught Degree |
Programme director(s) |
Kevin Tran
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Faculty | Faculty of Social Sciences and Law |
School/department | School of Economics |
Second School/department | School of Engineering Mathematics and Technology |
Teaching institution | University of Bristol |
Awarding institution | University of Bristol |
Mode of study | Full Time |
Programme length | 1 years (full time) |
This section sets out why studying this programme is important, both in terms of inspiring you as an individual and in considering the challenges we face. It describes how this degree programme contributes to:
The programme aims at training an economist with data science skills that allows the student to do big data analytics with economic reasoning using relevant economic data. The taught components of the programme will help students develop intellectual abilities to approach real world phenomena with sound economic reasoning, and to develop practical skills to analyse big data and to identify key variables for causal relations and predictions. Built upon the knowledge therein acquired, the group dissertation will in addition help students develop interpersonal skills and communication skills to be recognised as a professional economist and data scientist. The developed skills will cover both economic models from Economics and programming skills from Engineering.
This interdisciplinary program will introduce the language of both fields and find a unifying language for the program. students will get the opportunity to be proactive and be agents of their own learning, being unafraid to take risks and use their academic learning to help solve real industry problems in this inspiring and innovative program. The program aims to create a skills portfolio that will encompass proficiency with technical aspects of the course, programming, cloud computing, predictive analytics, and importantly soft skills to function effectively in work, communicating to different audiences, working with others, problem solving, analysis. The great mix of disciplines, related skills, and industry will keep the program current for new jobs that might not exist yet.
The learning outcome statements shown below for your programme have been developed with reference to relevant national subject benchmarks (where they exist), national qualification descriptors (see the Framework for Higher Education Qualifications) and professional body requirements.
Teaching, learning and assessment strategies are listed to show how you will be able to achieve and demonstrate the learning outcomes.
This programme provides opportunities for you to develop and demonstrate knowledge and understanding, qualities, skills and other attributes in the following areas:
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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• Problem-based learning combining lecture elements with examples and applications |
Methods of assessment (formative and summative) | |
Methods of assessment (formative and summative): • Self and peer assessments |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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|
Methods of assessment (formative and summative) | |
• Self and peer assessments |
Programme Intended Learning Outcomes | Learning/teaching methods and strategies |
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|
• Problem-based learning combining lecture elements with examples and applications |
Methods of assessment (formative and summative) | |
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Embedded within the curriculum |
The core and substantive component of the program is industry group projects to inspire students and enable them to competently apply skills. The students will develop essential professional skills and attributes and will be able to network and demonstrate creativeideas to industry audiences through presentations and feedback. |
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This section describes what is expected from you at each level of your programme. This illustrates increasing intellectual standards as you progress through the programme. These levels are mapped against the national level descriptors published by the Quality Assurance Agency.
Level M/7 - Postgraduate Certificate |
For a postgraduate Certificate, students are required to successfully complete 60 credit points of taught units in the programme. The structure of the degree programme has been designed to engage the student in a cumulative process of developing skills and knowledge through a sequence of complementary stages. In the first term, all Certificate, Diploma and Masters students develop foundational knowledge and understanding of the core Economic theories and their methodology; core programming and cloud computing skills to handle and manage big data; develop general intellectual skills and attributes necessary for that knowledge and understanding; and are required to develop several practical skills. |
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Level M/7 - Postgraduate Diploma |
For a postgraduate Diploma, students are required to complete all the taught units in the programme and obtain 120 credits. In the second term, all Diploma and Masters students acquire a deeper knowledge and understanding of core machine learning techniques and gain an understanding of the main subfields of Economics and their methodology. These units explore more specialised topics that nevertheless build on the material learned in the first term. The intellectual and practical skills learned in the first term are also developed, applied and extended. |
Level M/7 - Postgraduate Masters |
To be eligible for an MSc award, 180 credits from the taught modules plus the dissertation must |
For information on the admissions requirements for this programme please see details in the postgraduate prospectus at http://www.bristol.ac.uk/prospectus/postgraduate/ or contact the relevant academic department.
This interdisciplinary program is a unique one that combines the two disciplines at a specialist level. The program aims to provide practical programming skills to handle and manage big data with the exposure of Amazon Web Services, and provides the students with a unique opportunity to work on industry projects with economic applications in mind. Students will gain teamwork skills as well as skills to communicate complex ideas in an accessible way.
Unit Name | Unit Code | Credit Points | Status | |
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Mandatory Units: | ||||
Economic Analytics | ECONM0010 | 20 | Mandatory | TB-1 |
Econometrics with Python | ECONM0014 | 20 | Mandatory | TB-1 |
Large-Scale Data Engineering | EMATM0051 | 20 | Mandatory | TB-1 |
Machine Learning for Economics | ECONM0011 | 20 | Mandatory | TB-2 |
Dissertation (Group Project) | ECONM0017 | 60 | Mandatory | AYEAR |
Students select 20 credit points from one of the following: | ||||
Programme Evaluation with Data Project | ECONM0008 | 20 | Optional | TB-2 |
Econometrics beyond the mean | ECONM0007 | 20 | Optional | TB-2 |
Applied Financial Econometrics | ECONM0009 | 20 | Optional | TB-2 |
Students select 20 credit points from one of the following: | ||||
Health Economics with Data Application | ECONM0016 | 20 | Optional | TB-2 |
Empirical Industrial Organisation | ECONM0013 | 20 | Optional | TB-2 |
Labour Economics with Data Applications | ECONM0012 | 20 | Optional | TB-2 |
Development Economics with Data Application | ECONM0015 | 20 | Optional | TB-2 |
180 |
The pass mark set by the University for any level 7 unit is 50 out of 100.
For detailed rules on progression please see the Regulations and Code of Practice for Taught Programmes and the relevant faculty handbook.
An award with Merit or Distinction is permitted for postgraduate taught masters, diplomas and certificates, where these are specifically named entry-level qualifications. An award with Merit or Distinction is not permitted for exit awards where students are required to exit the programme on academic grounds. An exit award with Merit or Distinction may be permitted where students are prevented by exceptional circumstances from completing the intended award.
The classification of the award in relation to the final programme mark is as follows:
Award with Distinction*: at least 65 out of 100 for the taught component overall and, for masters awards, at least 70 out of 100 for the dissertation. **Faculties retain discretion to increase these thresholds.
Award with Merit*: at least 60 out of 100 for the taught component overall and, for masters awards, at least 60 out of 100 for the dissertation. Faculties retain discretion to increase these thresholds.
* The MA in Law has separate regulations for awarding distinction and merit.
** For the award of Distinction, the Faculty of Engineering requires at least 70 out of 100 for the taught component overall and, for masters awards, at least 70 out of 100 for the dissertation.
All taught masters programmes, unless exempted by Senate, must allow the opportunity for students to choose, or be required, to leave at the postgraduate diploma or certificate stage.
To be awarded a postgraduate diploma, students must have successfully completed 120 credit points, of which 90 must be at level 7.
To be awarded a postgraduate certificate, students must have successfully completed 60 credit points, of which 40 must be at level 7.
Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.
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