All programmes > Data Science for Finance

Data Science for Finance (B-KUL-HMA99A)

6 ECTSEnglish39 First termCannot be taken as part of an examination contract
Roukny Tarik (coordinator) |  N. |  Roukny Tarik (substitute)
OC Business Administration FEB Campus Brussel

Aims

Upon completion of this course, the student is able to:

• Demonstrate capacity to handle the basics of a scientific programming environment

• Formulate and implement relevant approaches to the treatment and manipulation of sizable datasets.

• Detect and exploit opportunities for analytical insights from structured and unstructured data.

• Properly assess trade-offs in modeling choices and their implication for the interpretation of empirical results.

• Evaluate and discuss the use of data science tools in the context of financial applications.

Previous knowledge

• This is a beginner’s course for programming: no prior computer skills are required

• Knowledge of statistics and basic econometrics is required

• Knowledge of Financial Markets and Institutions and Corporate Finance is advised

Is included in these courses of study

Activities

6 ects. Data Science for Finance (B-KUL-HMA99a)

6 ECTSEnglishFormat: Lecture39 First term
N. |  Roukny Tarik (substitute)
OC Business Administration FEB Campus Brussel

Content

Introduction to Scientific Programming

  • Programming Environment
  • Notebooks
  • Basics of Python programming language
  • Application Programming Interface (API)

Data Manipulation 

  • Basic Analytics
  • Data Treatment
  • Data Visualization

Applying Data Science

  • Cross validation and bias-variance trade-off
  • Supervised learning
  • Unsupervised learning
  • Advanced: introduction to Machine Learning (e.g., decision trees, neural networks)
  • Applications in Finance

Course material

  • Course material will be made available on Toledo: slides, reader, etc.
  • Course material consists primarily of what has been taught during lectures
  • Recommended Reading and Additional articles will be provided on Toledo.

Evaluation

Evaluation: Data Science for Finance (B-KUL-H75932)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Project/Product, Report, Presentation
Type of questions : Multiple choice, Open questions, Closed questions
Learning material : Computer

Explanation


Evaluation caracteristics

The evaluation may include a final written as well as group-projects resulting in a class presentation. The exact evaluation details will be provided at the start of the course and in Toledo.

Determination final result

 

Information about retaking exams

The features of the evaluation and determination of grades are identical to those of the first examination opportunity, as described in the tab 'Explanation'.