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Earn your Master’s in Data Science in Finance from Purdue University

Boost your career in finance with data science expertise 

The financial industry has been transformed by data. Increasingly, effective financial analysts need data science expertise to bring value to their clients and companies. Purdue University's online Master of Science in Statistics 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 – all while studying from anywhere.  

Learn in-demand data skills and apply them on the job market 

Financial companies are seeking out employees with expertise in machine learning and data analysis. The Master’s in Data Science in Finance is an interdisciplinary collaboration between the Department of Statistics in Purdue’s College of Science and Purdue's Mitchell E. Daniels, Jr. School of Business that equips students with these in-demand skills, providing them with hands-on experience and preparing them for careers in the quantitative financial field.  

All courses are taught fully online by the same expert, renowned faculty who teach on-campus in the Danile's School of Business and the Department of Statistics. Students take courses on a flexible schedule designed to accommodate the needs of professionals who want to learn valuable data skills without pausing their careers.  

Students in the program will learn a broad base of data skills relevant to the financial industry, including:  

  • Data science and machine learning techniques specific to the financial sector  
  • Quantitative finance  
  • Investment and risk management strategies  
  • Statistical and computational skills for optimizing product development and evaluation 
  • Portfolio management in high-frequency markets  
  • Programming skills (including C++, C#, MATLAB, VBA, Python, and R) 

Time commitment and organization

The Master’s in Data Science in Finance was designed to be convenient for working professionals. Students complete 30 credits over two years with an approximate time commitment of 20 hours per week. All courses are delivered asynchronously, so they can be accessed from anywhere at any time.  

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. 

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