AI in Finance using Python (3cr)
Code
General information
- Enrollment
- 12.02.2026 - 31.12.2028
- Registration for the implementation has begun.
- Timing
- 12.02.2026 - 31.12.2028
- Implementation is running.
- Number of ECTS credits allocated
- 3 cr
- Institution
- Metropolia University of Applied Sciences, Karaportti 2
- Teaching languages
- English
- Seats
- 0 - 500
- Course
- C-10065-TX00GW44
Unfortunately, no reservations were found for the realization AI in Finance using Python C-10065-TX00GW44-3001. It's possible that the reservations have not yet been published or that the realization is intended to be completed independently.
Evaluation methods and criteria
Pass/Fail Grading criteria's can be find via workspace or you can ask them via viopesupport@metropolia.fi
Evaluation scale
Pass/Fail
Content scheduling
All of seven (7) Chapters have to be done in order.
Objective
This material is an introduction to using artificial intelligence (AI) in the frame of financing applications. First we cover the basic ideas of machine learning models. Then selected use cases from financing are discussed with practical examples using Python programming language. The reader is strongly advised not only to run sample code but tweak code parameters to see their full effect and meaning. The reader is assumed to have a working knowledge of Python. Basics of AI and machine learning are mostly assumed, also, but those follow along the lines of our previous course. Elementary understanding of financial concepts is also helpful but we will explain them as we go.
Content
1 Introduction 2 Data acquisition etc. 3 Timeseries analysis 4 Sentiment analysis 5 Risk management 6 Fraud detection 7 Portfolio optimization
Location and time
Course can be find via Metropolia's Viope World and it can be done in own pace.
Materials
Can be find via workspace.
Teaching methods
Course is 100% online (self-study) course which can be done in own pace. Course includes 7 Chapters which have to be done in order.
Employer connections
N/A
Exam schedules
Can be found via the workspace.
International connections
N/A
Completion alternatives
N/A
Student workload
Depends on Students starting level.