EC910-7-AU-CO:
Computational Economics
2024/25
Economics
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
20
14 October 2024
Requisites for this module
(none)
(none)
(none)
(none)
EC911
MSC L14112 Economics with Data Science
This module is your gateway to the exciting intersection of economics and technology. Designed for students with no prior programming experience, it offers a comprehensive introduction to three key areas:
- Fundamentals of R Programming: Unlock the power of R programming through hands-on instruction and practical exercises. From basic syntax to advanced data manipulation, you'll gain the skills needed to harness the full potential of this versatile language.
- Agent-Based Computational Economics (ACE) Modelling: Delve into the realm of ACE modelling, where you'll learn to construct and simulate intricate financial networks to gain insights into dynamic economic systems.
- Machine Learning Applications: Harness the power of machine learning algorithms to tackle real-world economic and financial challenges, equipping yourself with invaluable tools for predictive modelling and data-driven decision-making.
Through engaging lectures and interactive laboratory sessions, you'll gain practical experience in leveraging computational techniques to tackle real-world economic problems. Join us and embark on your journey to becoming a tech-savvy economist!
The aim of this module is:
- To empower students with the computational skills and analytical techniques needed to excel in modern economic and financial analysis.
By the end of this module, students will be expected to be able to have:
- Developed a strong command of R programming fundamentals.
- Acquired a deep understanding of ACE modelling and its implications for financial network analysis.
- Gained practical experience in applying machine learning techniques to solve real-world economic and financial problems.
No additional information available.
This module will be delivered via:
- 10x 2-hour lectures and 10x 2-hour laboratory sessions, immersing yourself in a dynamic learning environment.
Engage with expert instructors, collaborate with peers, and unlock your full potential in Computational Economics.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Participation |
|
5% |
| Coursework |
Term Paper |
|
95% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Ran Gu, email: ran.gu@essex.ac.uk.
Lectures and Labs: Dr Ran Gu
For further information, send an email message to pgteco@essex.ac.uk.
No
No
No
Dr Domenico Moro
university of Birmingham
Lecturer
Available via Moodle
Of 28 hours, 28 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
Economics
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