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COURSE OUTLINE

Financial Technology (FinTech)

1. General

School

School of Finance and Statistics

Academic Unit

Department of Banking and Financial Management

Level of Studies

Undergraduate

Course code

ΧΡΗΤΕ

Semester

6th or 8th

Course Title

Financial Technology (FinTech)

Idependent Teaching Activities

Weekly Teaching Hours

Credits

Lectures
4
7,5

Course Type

Special background, Skills development

Prerequite Courses

Language of Instruction and Examinations

Greek

Is the course offered to Erasmus Students?

Url (Eclass)

https://eclass.unipi.gr/modules/auth/courses.php?fc=64

2. Learning Outcomes

Learning Outcomes

Students will be using their PCs all the time, allowing them to become more familiar with both the theory and its implementation using the appropriate software (Python, Power BI and sql). Prior knowledge of these programs is not required for attending this course. Goal of the course is to help students understand WHAT they want to develop, WHY they would need to develop it and then actually develop it.

General Competences
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Decision-making
  • Working independently
  • Teamwork
  • Production of free, creative, and inductive thinking

3. Syllabus

  • Big Data Analytics και Business Intelligence Tools (Power BI)
  • Statistical Learning, Machine Learning και Artificial Intelligence
  • Blockchain και Cryptocurrencies
  • Algorithmic Trading

Selected material is covered in the following CFA sections:

  • CFA Level I: Fintech in Investment Management
  • CFA Level II: Algorithmic Trading and High Frequency Trading

4. Teaching and Learning Methods - Evaluation

Delivery

Face to face at the Computer Lab

Use of Information and Communications Technology

Laboratory education, utilizing specialized software and algorithms, all course notes will be shared with students via the university’s e-class platform.

Teaching Methods

Activity

Semester Workload

Lectures
52
Study and analysis of bibliography
109,5
Laboratory Practice
26
Course total
187,5

Student Performance Evaluation

Students will be assigned 4 projects, one per main course unit, prepare a presentation and examined through open-ended questions

5. Attached Bibliography

Suggested Bibliography
  • Artificial Intelligence Applications in Financial Services, Oliver Wyman
  • An Introduction to Statistical Learning with Applications in R. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
  •  Blockchain Technology Overview, Dylan Yaga, Peter Mell, Nik Roby, Karen Scarfone
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