| Unit name | Data science for economics |
|---|---|
| Unit code | EFIMM0095 |
| Credit points | 15 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Vincent Han |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
ECONM1022 Econometrics |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one | |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
This course is designed for MSc students to learn the theory and practice of machine learning methods. Throughout the course, we overview the most popular machine learning methods, such as lasso, random forest, support vector machine, and neural network. During lectures, we will learn the basic idea, strengths, and weaknesses of each method. During labs, we will learn how to implement each method using a computer programming tool, Python, and real data. Students will learn the basic knowledge of using Python during labs. The goal of this course is to provide students with practical knowledge to conduct empirical research that involves big data. At the end of the course, students will be equipped with basic skills that will be useful in the future when working in academia and public and private sectors.
At the end of this course, students will be able to perform the following advanced computing task independently:
Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities
Coursework (50%)
Exam (2hr) (50%)
both assessments assess all ILOs
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. EFIMM0095).
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.
