Undergraduate Course: Data Science in Economics (ECNM10109)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Data Science in Economics introduces principles and methods from Machine Learning that enable economic analysis of Big Data. |
| Course description |
Businesses, governments and other organizations collect and store vast amounts of digitized information. This is Big Data: It is big in scale, and it is big in scope and complexity (high-dimensional, nonstandard format). Data science concerns the analysis of Big Data and is fast becoming an essential part of an economist's toolkit. Taking standard linear regression analysis as a starting point, Data Science in Economics introduces principles and methods from Machine Learning that enable economic analysis of Big Data. Machine Learning is focused on extracting information from complex data and automatically building good predictions. All analysis is conducted in R, an open-source statistical language for data analysis that is widely used in industry, government, and academia.
Please note: All students enrolled on this course will be required to bring a laptop with them to tutorial sessions, in order to take part fully in teaching. If you do not own a laptop, the School encourages you to explore the University¿s laptop loan scheme: https://www.ed.ac.uk/information-services/library-museum-gallery/using-library/borrowing-a-book/borrowing-laptops
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Essentials of Econometrics (ECNM10052)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Visiting students must have an equivalent of at least 4 semester-long Economics courses at grade B or above for entry to this course. This MUST INCLUDE courses in Intermediate Macroeconomics (with calculus); Intermediate Microeconomics (with calculus); Probability and Statistics; and Introductory Econometrics. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 6,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
170 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Problem Sets - 20%
Degree exam - 80% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Have had the opportunity to develop and demonstrate a knowledge and understanding of key tools in machine learning that may include regularized and tree-based prediction models for regression and classification in supervised Machine Learning, factorization methods for dimension reduction in unsupervised Machine Learning, analysis of text as data, how Big Data and Machine Learning can aid causal inference.
- Have had the opportunity to develop and demonstrate research and investigative skills such as problem framing and solving and the ability to assemble and evaluate complex evidence and arguments.
- Have had the opportunity to develop and demonstrate communication skills in order to critique, create and communicate understanding.
- Have had the opportunity to develop and demonstrate personal effectiveness through task-management, time-management, dealing with uncertainty and adapting to new situations, personal and intellectual autonomy through independent learning.
- Have had the opportunity to develop and demonstrate practical/technical skills such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and general IT literacy.
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Reading List
| Taddy, M. (2019) Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, McGraw Hill |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | big data,data science |
Contacts
| Course organiser | Prof Jesper Bagger
Tel:
Email: jbagger@exseed.ed.ac.uk |
Course secretary | Mr Sean Barr
Tel: (0131 6)50 6661
Email: sean.barr@ed.ac.uk |
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