Introduction to Data Science for Finance (2225.YR.015295.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	2 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	André Brandao de Mello Souza	Economía, Finanzas y Contabilidad	ENG

Students must bring their own laptop with a working Python installation to class.

Students should have basic knowledge of Statistics and Linear Algebra.

This skill seminar provides finance and economics students with the required foundations for further studies in machine learning and data science.
We will cover the basics of programming with Python and provide an overview of state-of-the-art tools employed to tackle issues relevant to finance professionals.

1. Introduction to Python
2. Data handling, visualization and reporting
3. Linear regressions
4. k-Nearest Neighbors and CART.

y the end of the course, students will have a basic understanding and some experience with python programming. Students will also be exposed to some of the data science tools used in the financial industry.
The main learning objective for the course is to provide the foundational tools required for further studies in Machine Leaning and Data Science.

1. Introduction to Python

2. Data Handling, visualization and reporting

3. Linear Models for Prediction

4. k-Nearest Neighbors and Classification and Regression Trees

This is a hands on course in Python designed with financial applications in mind. Lectures are composed of a mix of Python programming (80%) and exposition of the foundational methodological details required (about 20%).

Hastie, Trevor; Tbishirani, Robert; and Friedman, Jerome. The Elements of Statistical Learning. 2nd edition, Springer.

James, Gareth; Witten, Daniela; Hastie, Trevor and Tbishirani, Robert. An introduction to Statistical Learning, 2nd edition. Springer.

Group	Teacher	Department
Year 1	André Brandao de Mello Souza	Economía, Finanzas y Contabilidad