At 10 hrs/week
Get access to classroom immediately on enrollment
Learn about market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project.Trading with momentum
Learn the quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading.Breakout Strategy
Learn about portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs.Smart Beta and Portfolio Optimization
Learn about alpha and risk factors, and construct a portfolio with advanced optimization techniques.Alpha Research and Factor Modeling
Learn the fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals.Sentiment Analysis using NLP
Learn to apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals.Deep Neural Network with News Data
Learn advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data.Combine Signals for Enhanced Alpha
Learn to refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells.Backtesting
from industry experts
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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 a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine 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.
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Numbers don't lie. See what difference it makes in career searches.*
Career-seeking and job-ready graduates found a new, better job within six months of graduation.
Average salary increase for graduates who found a new, better job within six months of graduation.
Demand for quantitative talent is growing at incredible rates. Data-driven traders are now responsible for more than 30% of all US stock trades by investors (or about $1 trillion USD worth of investments, up from 14% in 2013). This scenario represents incredible opportunity for individuals eager to apply cutting-edge technologies to trading and finance.
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 trading and quantitative finance, this program will give you the opportunity to build an impressive portfolio of real-world projects. You will build financial models on real data, and work on your own trading strategies using natural language processing, recurrent neural networks, and random forests. You’ll also enjoy direct access to leading experts in the field, and get personalized project and career support.
To create the curriculum for this program, we collaborated with WorldQuant, a global quantitative asset management firm, as well as top industry professionals with prior experience at JPMorgan, Morgan Stanley, Millennium Management, and more. If your goal is to learn from the leaders in the field, and to master the most valuable and in-demand skills, this program is an ideal choice for you.
Graduates of this program will have the quantitative skills needed to be extremely valuable across many functions, and in many roles at hedge funds, investment banks, and FinTech startups.
Specific roles include:
If you’re a programmer, data analyst or someone with a strong quantitative background, this program offers you the ideal path to pursue a quant trading career and prepares you to seek out data science jobs across the financial ecosystem.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
The Artificial Intelligence for Trading Nanodegree program is designed for students with intermediate experience programming with Python and familiarity with statistics, linear algebra and calculus. In order to successfully complete this program, you should meet the following prerequisites:
Calculus and linear algebra
The Artificial Intelligence for Trading Nanodegree program is comprised of content and curriculum to support eight (8) projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. We estimate that students can complete the program in six (6) months working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.
To successfully complete this Nanodegree program, you’ll need to be able to download and run Python 3.7.