AI in Finance Specialized Bootcamp

Financial data science is a constantly growing field, so is the demand for those able to apply suitable data science tools to finance. The ultimate aim of this course is to equip students with the necessary skills so as to understand, analyze, and interpret data science tools applicable to finance. To this respect, the course is designed to introduce apply data science tools to model financial problems.

This course is the first step towards a better understanding of the finance world with the help of data science. The topics to be covered in this class are financial APIs, regression analysis for finance, time series analysis, time value of money, simulation analysis, modern portfolio theory, and the financial data science is a constantly growing field, so is the demand for those able to apply suitable data science tools to finance. The ultimate aim of this course is to equip students with the necessary skills so as to understand, analyze, and interpret data science tools applicable to finance. To this respect, the course is designed to introduce apply data science tools to model financial problems.

This course is the first step towards a better understanding of the finance world with the help of data science. The topics to be covered in this class are financial APIs, regression analysis for finance, time series analysis, time value of money, simulation analysis, modern portfolio theory, and the applicability of unsupervised learning in finance.

Prerequisites

Students must be enrolled in the Data Science Program. Other students may be admitted with instructor permission. Students are expected to have beginner-level Python programming experience.

● Introduction to Main Financial Concepts

● Accessing the Financial Data Sources via APIs and Regression Analysis: A Quick Review

● Capital Asset Pricing Model and Arbitrage Pricing Model

● Modern Portfolio Theory and Best Risk-Return Portfolio: Theoretical Overview

● Forming the Portfolio with Best Risk-Return Trade-off

● Time Series Analysis: Theoretical Overview

● Time Series Analysis: Application

● Net Present Value and Internal Rate of Return

● Monte Carlo and Simulation Analysis: Theoretical Overview

● Monte Carlo and Simulation Analysis in Finance

● Making Sense of Unsupervised Learning in Finance: A Quick Overview

● Dimension Reduction and Clustering in Finance

● Anomaly Detection in Finance

Start learning the AI in Finance with outside of business hours schedule!

The 12 hours of schedule is as follows:

April 12 – 19 – 26 and May 3

Sundays, from 9:00 am to 12:00 pm

The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.

AI in Finance Specialized Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.

Tuition fee

Regular: $300

Early Bird: $300 (between , – , )

Payment process

After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.

Cancellation

If you’re not satisfied with the course you may cancel your application.

Abdullah Karasan

Abdullah Karasan is a principal data scientist at Magnimind Academy and an instructor for O’Reilly and Springboard. He’s published several papers in prestigious journals in the field of financial data science and is the sole author of Machine Learning for Financial Risk Management with Python (forthcoming December 2021). Born in Berlin, Germany, he holds a master’s degree from the University of Michigan-Ann Arbor and a Ph.D. in financial mathematics from Middle East Technical University.

He has several published scientific articles and is the author of the book in progress titled “Machine Learning for Financial Risk Management in Python”.