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Data Science in Finance Certificate

Data Science has become a crucial tool in modern financial markets. Using machine learning, those in the financial industry can use statistical models to draw insights from their data in order to make strategic decisions, solve modern financial problems and increase profit.

The Data Science in Finance certificate is designed to give you the opportunity to invest in yourself by bridging the gap between data science and finance. You will gain the knowledge to incorporate machine learning in your analysis, including programming with Python, which will help streamline your processes, decrease risk and optimize and manage your portfolios.

This non-credit certificate program is a series of three courses available 100% online. Each course is seven weeks and includes video lectures and online discussions, with quizzes and a final exam during the final week. As you move through the program, designated office hours through WebEx will be available for the faculty members to provide assistance and answer questions regarding coursework. Courses can be taken individually; however, Machine Learning in Finance 1 must be taken first. If all three are completed, a Data Science in Finance certificate will be awarded. This program is part of a Data Science in Finance Online Master's degree program that will be offered spring 2021 and a noncredit-to-credit pathway will be available.

The registration deadline for the October 19 Machine Learning in Finance 1 course is October 12, 2020.

Data Science in Finance Courses Available Now

Machine Learning in Finance 1 (DSF541): Learning the Fundamentals

This overview course introduces basic concepts of probability and statistics necessary to study quantitative finance and machine learning. The course also investigates structuring your financial data in a way that is amenable to machine learning algorithms. Once completed, students will be equipped with the foundations of machine learning and have an understanding of their applications in finance and be prepared for Machine Learning in Finance 2.

Topics include probability theory, computational finance, the Black-Scholes model, financial data structuring and labeling, market microstructure, and basic Python programming.

Prerequisites include knowledge of undergraduate level of probability and statistics, but a student does not need to have knowledge of finance or machine learning. Experience in programming will be helpful, but is not required.

Learning outcomes:

  • Describe basic background knowledge of machine learning and its application in finance for a deeper study in the following courses, DSF542 and DSF543.
  • Identify the power and limitation of machine learning techniques in modern finance problems.
  • Recognize unique features of financial data and how to handle them.

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Data Science in Finance Courses Available Spring 2021

Machine Learning in Finance 2 (DSF452): Reinforcement Learning

This course focuses on reinforcement learning, an area of machine learning, and its application to modern finance problems. It will build on DSF 541 and prepare you for Machine Learning in Finance 3. Once completed, you will be able to recognize when reinforcement learning can be used to make strategic decisions based on existing data.

Topics include deep neural networks, Q-learning, Python programming, hedging and pricing, portfolio dynamics and optimization, and high-frequency data.

This course is a bridge to go to the third and last course covering comprehensive artificial intelligence (AI) methods in finance.

Prerequisites include knowledge of undergraduate level probability and statistics, and successfully completing the previous course, DSF 541.

Learning outcomes:

  • Describe basic background knowledge of deep neural networks and reinforcement learning.
  • Identify the power and limitation of reinforcement learning techniques in modern finance problems.
  • Recognize financial problems where we can use reinforcement learning as a cutting edge.

Machine Learning in Finance 3 (DSF453): Application with Data Science

This course will teach you how to apply artificial intelligence (AI) methods in finance. Building on machine learning techniques and finance concepts discussed in DSF 541/542, you will learn the most current challenges in financial markets and the machine learning approaches to use for those challenges. High-frequency markets and data-based trading strategies will be a main focus, including practical aspects of data analysis and computer programming. 

Topics include optimal execution, market making, pairs trading and statistical arbitrage, and order imbalance.

Prerequisites includes knowledge of undergraduate level probability and statistics, and successfully completing DSF 541 and DSF 542.

Meet the Faculty

Kiseop Lee

Kiseop Lee
Associate Professor of Statistics

Dr. Kiseop Lee's research includes stochastic models, liquidity risk, information asymmetry, and machine learning application in high-frequency data problems. He is an associate editor of six professional journals for mathematics, statistics and financial engineering. He has numerous papers published in academic journals such as the Journal of Banking and Finance, Journal of Futures Markets, Quantitative Finance and Cutting Edge in Risk, which is a top practitioner’s journal. He has worked as a consultant at Invest. 

Xiao Wang

Xiao Wang
Professor of Statistics

Dr. Xiao Wang's research interests focus on machine learning, deep learning, nonparametric statistics, and functional data analysis. Dr. Wang has published nearly 50 peer-reviewed papers in publications including the Annals of Statistics, Journal of the American Statistical Association (JASA), Biometrika, and Society for Industrial and Applied Mathematics (SIAM) publications, and at top conferences such as the International Conference on Learning Representations (ICLR), Association for the Advancement of Artificial Intelligence (AAAI), International Joint Conferences on Artificial Intelligence (IJCAI), and Artificial Intelligence and Statistics (AISTATS). Currently, He is serving as associate editor of JASA, Technometrics, and Lifetime Data Analysis.  

Who Can Benefit

Professionals in the following industries:

  • Investment banks and trading companies
  • Asset management firms and pension funds
  • Insurance companies
  • Financial software and consulting firms
  • Energy companies

Program Start: October 19, 2020

Registration Deadline: October 18, 2020

Tuition: $1,800 per course

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