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MS in Data Science in Finance: Overview

Master Data Science and Machine Learning to Boost Your Career in Finance

Data science and machine learning are becoming more integral to the financial industry every year. Purdue University's online Master's Degree in Data Science in Finance is designed to teach students how to solve problems in modern financial markets using artificial intelligence and state-of-art data science techniques. The innovative degree by Purdue University is unique in the way it integrates into the curriculum cutting-edge machine learning with data science and finance, preparing students to apply these powerful tools and boost their careers.

Create A Niche For Yourself In The Changing World Of Finance

Become a prime candidate for a broad variety of positions within the financial industry by earning Purdue's interdisciplinary Data Science in Finance Master's degree with its focus on developing highly sought-after expertise in machine learning. The goal of the Purdue program is to equip students with the tools necessary to advance a career in a quantitative financial field, as quants or in many other roles.

The two-year program provides learners with comprehensive and practical knowledge of data science and machine learning techniques used by the financial sector to develop investment strategies and manage risk, as well as the mathematical, statistical, and computational skills needed for the creation, implementation, and evaluation of models and products.

The online Master’s Degree in Data Science in Finance is a collaboration between the Department of Statistics in Purdue’s College of Science and Purdue's Krannert School of Management. The program leverages internationally recognized Purdue expertise in data science, machine learning, artificial intelligence, business, and finance to create an exciting new learning experience.

The courses are taught by the same world-class faculty in those disciplines as Purdue’s on-campus programs. The curriculum emphasizes current best practices and highlights the latest tools, techniques, strategies, and processes.

Gain Practical Knowledge and Apply it in the Real World

Students complete 30 credits over two years with a time commitment of approximately 20 hours per week. The curriculum is designed to teach more than textbook definitions of theory and strategy. It teaches practical skills that students can use, such as Python programming for machine learning.

Students will find that this 100% online program allows them to maintain their full-time career, spend time with their family, and still pursue a degree. Courses in the program are delivered asynchronously. Classes typically run eight weeks in length (½ semester = 1 module) except for project courses and some specialty courses. Each semester consists of two (2) eight-week modules.

The core courses in the program go beyond the generalities of financial models and products, and provide a thorough understanding of the quantitative aspects of:

  • Arbitrage pricing of derivatives in equity, fixed income, and credit markets
  • Portfolio management in a high frequency market
  • Optimal execution and market making in a high frequency market
  • Key financial algorithms of option pricing, optimization, simulation, and calibration (with programming in C++, C#, MATLAB, and VBA)
  • Statistical methodologies including multivariate data and time series analysis with computational experience in Python and R

Students complete courses in statistics, quantitative finance and machine learning. This program is versatile, allowing students to hone in on subjects of particular interest to them. A broad variety of elective courses offered by Purdue's Krannert School of Management complement the curriculum with a more general approach to market products and practices.

The learning objectives include:

  • Master state-of-art machine learning techniques useful for challenges in modern financial markets
  • Obtain computational skills necessary for real world applications
  • Comprehend current challenges in a high-frequency market
  • Learn algorithmic trading strategies
  • Obtain mathematical and statistical skills to model and analyze the finance market
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