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 |
Business, governments and other organizations collect and store vast amounts of digitized information. This is Big Data: It is big in scale, scope and complexity. Data science concerns the analysis of Big Data. Starting from regression analysis, Data Science in Economics introduces principles and methods from Machine Learning to extract information from Big Data that enables economic analysis via prediction or casual analysis. Data Science in Economics uses R, a statistical software widely used in industry, government and academia. Familiarity with R is not a pre-requisite, but students unfamiliar with R should expect a steep learning curve.
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)
|
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
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: 100 |
| 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 )
|
| 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) | Data Science in Economics December Exam 2024 | 2:120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Have had the opportunity to develop and demonstrate knowledge and understanding of Machine Learning (ML) tools that may include regularized and tree-based prediction models in supervised ML and factorization methods in unsupervised ML.
- Have had the opportunity to develop and demonstrate investigative skills such as problem 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
Essential: Taddy, M. (2019) Business Data Science: Combining Machine Learning
Recommended supplementary reading: James, G., D. Witten, T. Hastie and R. Tibshirani (2021) ¿An Introduction to Statistical Learning with Applications to R,¿ Springer Verlag.
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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 | Ms Sam Stewart
Tel:
Email: v1sstew7@ed.ac.uk |
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