“WorldQuant is committed to bringing opportunity to talent globally. This Nanodegree program is like a laser, it is focused exactly on what you need today to succeed.”— Igor Tulchinsky, Founder, Chairman and CEO, WorldQuant
In this program, you’ll analyze real data and build financial models for trading. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in quantitative finance, this program offers you the opportunity to master valuable data and AI skills.
We collaborated with WorldQuant and top industry professionals with prior experience at JPMorgan, Morgan Stanley, and Millennium Management to ensure you learn the latest AI applications in trading and quantitative finance.
Advance your finance knowledge, and build a strong portfolio of real-world projects. Build financial models with real data, and learn to generate trading signals using natural language processing, recurrent neural networks, and random forests.
Your assigned in-classroom mentor will provide feedback on your projects and support you throughout your learning journey. You'll also be part of a supportive peer community.
Connect with finance professionals who have worked at top hedge funds, investment banks, and Fintech startups, who can provide you with actionable insights and guidance.
Jonathan has previously held leadership roles such as Global Head of Equities at Millennium Management and Co-Head of Americas Equity Derivatives Trading at JPMorgan.
Kendall has been a quant trader and researcher at Citadel, Millennium Partners and JPMorgan. He has an MS in Financial Math from Stanford University.
Murat is a quant researcher at Radix Trading and has worked for JP Morgan and Citadel. He has a PhD in Statistics from Stanford University.
Justin has been an investment strategist in the Scientific Active Equity Group at BlackRock, and a quant research analyst at MUFG/HighMark Capital.
Harry has worked on algorithmic trading programs and risk management at Morgan Stanley and Apogee Fund Management, and as CTO at Carlyle Blue Wave.
Gordon Ritter is a Professor at NYU Courant and Tandon, Baruch College, and Rutgers. He is an elite buy-side quantitative trader and portfolio manager, and was named Buy-Side Quant of the Year 2019 by Risk.net.
Cindy is a quantitative analyst with experience working for financial institutions such as Bank of America Merrill Lynch, Morgan Stanley, and Ping An Securities. She has an MS in Computational Finance from Carnegie Mellon University.
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
Elizabeth received her PhD in Applied Physics from Stanford University, where she used optical and analytical techniques to study activity patterns of large ensembles of neurons. She formerly taught data science at The Data Incubator.
Eddy has worked at BlackRock, Thomson Reuters, and Morgan Stanley, and has an MS in Financial Engineering from HEC Lausanne. Eddy taught data analytics at UC Berkeley and contributed to Udacity’s Self-Driving Car program.
Brok has a background of over five years of software engineering experience from companies like Optimal Blue. Brok has built Udacity projects for the Self Driving Car, Deep Learning, and AI Nanodegree programs.
Parnian is a self-taught AI programmer and researcher. Previously, she interned at OpenAI on multi-agent Reinforcement Learning and organized the first OpenAI hackathon. She also runs a ShannonLabs fellowship to support the next generation of independent researchers.
Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied computer vision and deep learning to medical diagnostic applications.
Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
This program was built in collaboration with WorldQuant, and top professionals from leading financial institutions, to ensure your long-term success in quantitative finance. The skills you learn will prepare you for a wide range of quant finance jobs in hedge funds, investment banks, and FinTech startups.Designed to prepare you for career success in quantitative finance.
Create your professional portfolio with Udacity and open up a world of opportunities. Our hiring partners are eager to meet you.Create your portfolio and open up a world of opportunities.
Work with experienced career professionals to improve your job search, and impress recruiters. Get valuable feedback on your LinkedIn profile and your professional brand.Work with career professionals to impress recruiters.
40,000+ highly-skilled grads make up your new career community. Ready to collaborate, share referrals, or hire your own team? The Udacity Alumni Network is here for you!Connect with our global community to grow your career.
Learn the basics of quantitative analysis, and work on real-world projects from trading strategies to portfolio optimization.
Learn how to analyze alternative data, use machine learning to generate signals, and backtest top strategies.