Become a Quantitative Analyst

Financial Timeseries Analysis & Optimization

What is this Course About?

Wall Street needs more quants and data scientists.

This course will allow you to build the essential initial programming skills and tool belt of statistical techniques required for quantitative analysis.

First, we'll teach you how to program with financial timeseries before diving deep into multivariate regressions using factor analysis to explain Berkshire Hathaway's performance.

Next, we'll examine the performance of 9 different hedge fund strategies and compare the risk and return characteristics of each type of fund.

Finally, we'll construct our own fund strategy using quadratic optimization to track a benchmark on a rolling basis, and we'll build our own backtesting engine in R to analyze our strategy.

Am I Ready for this Course?

Whether you're a hedge fund manager or a business student, this course is for you if you're looking to upgrade your game and begin investing intelligently.

We'll provide you with commented source code, guided video tutorials and high quality animations to help you understand every line of code and concept.

Become a Quant.


Course Curriculum


  Getting up to Speed with Financial Programming in R
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  Analyzing Hedge Fund Strategy Performance
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  Multivariate Rolling Regressions | How Does Warren Buffett Do It?
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How Does Warren Buffet Do It?

Use multivariate rolling regression techniques on market factors to explain Warren Buffet's returns


Create Your Own Indexing Strategy

Use quadratic optimization to create an indexing strategy and then build a rolling backtesting engine to compare your results to the benchmark


Your Instructor


Dakota Wixom
Dakota Wixom

Dakota is one of the top ranked online instructors teaching financial analytics, R, and Python programming to thousands of students around the world.

He currently works as the Chief Data Scientist at a venture debt company, focusing on building analytical models for asset-heavy companies and decision-making infrastructure for automated loan processes.

Dakota also worked as a Quantitative Analyst at an AI startup called Yewno, where he built complex signals and factors on global securities, which were sold to some of the most sophisticated hedge funds in the world.

Dakota has a Master's in Financial Analytics, and did his undergrad in Quantitative Finance at the Stevens Institute of Technology, where his work included research on risk averse two-stage stochastic programming problems for portfolio optimization.


Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I don't know how to program?
No worries. R is one of the easiest programming languages to learn, and we'll have you up and running in no time. We'll step you through every single line of code as we go, and we'll provide the downloadable source code for every lecture in case you get lost. If you can use Excel, you're not going to have a problem learning R with our help!
Do I get the source code?
You sure do! Every single section is comprised of multiple video tutorials with hours of content, as well as source code and useful links to help you along the way. We'll continue to add resources and answer any questions you might have in the course discussion boards.
Do I need to know stochastic calculus?
Absolutely not! Unless you're planning on developing a new options pricing strategy for 3X leveraged ETFs - stochastic calculus is not necessary for implementation, but of course it could help you understand some of the concepts at a deeper level.
Is this a replacement for a PhD or an MFE?
Of course not! There are certainly some quant jobs which will require these advanced degrees, but Wall Street and top hedge funds also consistently hire engineers, data scientists, physicists, and others who have a good background in technology and/or finance. The best possible candidate for this course will have a finance or tech background, and this course will help you to build a portfolio and hone your skills to apply directly to quantitative finance. Those without that background can still use this course to help you land entry level finance, tech, or analytics jobs at top banks, and differentiate yourself from the crowd.
Why is [cool topic] not in the curriculum?
We might already be working on adding it. We are continuously updating this course, and we're even considering hiring other teachers to help out. If you're looking for a topic that's not currently in our curriculum, or you're wanting to help out with the course, send us a message!
Other Questions?
Send us your questions!
Why is this in R and not Python, C++, C#, Q, F#, Scala, OCaml, etc. ?
R is a statistical programming language. Quite simply, it was built from the ground up to do this type of work. There are thousands of incredible R packages which you can leverage to perform financial calculations. You can even call Python and C++ from inside R if you need to.

Regardless, most quants can program in multiple languages. Once you learn how to apply quantitative concepts to make investment decisions, you can pick the best language for the job. Many financial institutions have entire teams dedicated to taking models from one language to another for deployment in their specific languages. In fact, some of these companies even write their own programming languages!

Get started now!