How to Become a Quant: Core Topics

I have been asked many times to provide a list of core topics or knowledge required to be a quant. As I have mentioned in the past, I believe being a quant is a continual process and not an end. Due to the infinite amount of topics that could be considered required base knowledge I have avoided writing this article and making the YouTube video for quite some time. The list below is only my opinion and has fairly general topics however I do believe these are some of the base areas that should be covered in a Masters quant or financial engineering program. These topics will change over time. Since I graduated 3 years ago, machine learning has become a required skill.

The three main areas of being a quant are math/statistics, computer science, and finance however I will have separate sections for math and stats.

Stats:
• Regression (OLS, GLM, Logistic, and etc.)
• Time-series (ARIMA, GARCH, ECM)
• Nonparametric Regression (Splines, Kernel, Locally Weighted Regression)
• Data Exploration (Density Estimation, Normality Tests, Monte Carlo, Copulas
• Data Cleaning and Reduction (Cluster Analysis and Stats Theory)

Math:
• Calculus and Linear Algebra
• Optimization (Taylor Series, Markov Processes)
• ODE and PDE
• Stochastic Calculus (Martingales, Brownian Motion, Stochastic Integrals, Stochastic Differential Equations, Ito’s Lemma, Feynman-Kac)
• Binomial Asset Pricing

Computer Science:
• Stats Language (R, Python, SAS, Matlab, SPSS)
• Programming Language (Python, C++)
• Memory Management, Functions, Variables, Classes, Loops, If/Else Logic, Operators, Arrays, Reference and Pointers, best practices for writing code
• Implementation of math and stats knowledge in a program
• Machine Learning (Random Forest, Neural Networks, Decision Tree, Clustering, Dimensionality Reduction, Ensemble)

Finance:
• Equity (Stock Analysis, Diversification, Technical Analysis, Finance Theory)
• Fixed Income (Rate Curves, Pricing, Duration, TVM)
• Derivatives (Black Scholes, BDT, Stochastic Volatility Model, Volatility Smiles and Theory)
• Portfolio Optimization (CVaR, Efficient Frontier)
• Arbitrage Theory and Statistical Arbitrage
• Risk Management (VaR, Statistics, Credit Risk, Market Risk, Liquidity)

The topics above should be enough to start the journey of becoming a quant. Being a quant is a continual process and true quants will take the above as a starting point while continuing down different paths of interest. As you work in industry you will hone very specific skills however it is important to continue to explore other areas to strengthen your knowledge and to remain competitive in the job market.

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[nicorobin]

From time to time I come back to check on my path, I have just started, reviewing calculs and Taylor series and some other stuffs and I am so happy that I found a path drawn here in your channel, thank you so much

[xkr]

Been working as a quant in asset management. Good video to quickly divide/structure your prep especially when you're looking to switch jobs!

[Thahid]

Wow I watched this video when I was just coming out of my first year of bachelors. I really want to become a quant one day so i stuck to your methodology.

I’m happy to say that I’ll be starting next week as a data analyst for an asset management firm in London.

Let’s see how far I can get :)

[noahrubin]

100% the best quant channel out there!

[kennywong]

Very insightful sharing! Big thank you!

[ChuckEarnest]

Thanks for the video, I've seen a few of yours and I will continue watching as they seem to be very helpful. I am planning to start my BS in math this fall so I am trying to soak in any info like this I can find.

[EdwardYang-rdzi]

I would like to say your video is incredible and helpful! Thank you!

[meetrayvadera]

Your videos are really helpful. Thanks.

[mayavik]

Its not a destination, but a journey....

[jerrodrabenbauer]

Can you create an updated version of this video? Or is the relevance more or less the same

[simplylost]

Should we be more specific when we use the term quant ? Like im sure there are researcher, developer and trader ? And do each of them have different skill sets? What i heard when it comes to quant trader is that they are not expected to write a single line of code. Unless there are quants out there who hold those 3 roles simultaneously.

[tsingyang]

A very good summary, Thank you for the video

[menghrajpunjabi]

Excellent Video Dimitri. You mentioned that you are into model validation, would it be possible to share / make a video on AtoZ of model validation by taking an example of one model on risk side may be?

[imanuelgreenfeld]

Thank you for the video.
You said at the beginning that you're not becoming quant, rather it's a repetitive process of learning.
I have few years experience as a programmer in C++ and Python and I hold BSc and 2 MScs.
Neither is quantitative related.
So if I do MSc financial engineering or quantitative finance, will employers not give me the opportunity to work as quant since I have not done that previously?
In other words, do they want graduates fresh from university in their 20s?
Thank you

[paul]

Great video.
Im currently a cs major/ stat minor undergrad. I am planning on continuing my education, with a masters in cs/machine learning. I fall into the category of those with limited finance knowledge. Would an online course be sufficent in bringing my finance skills up to par or is a master in FE really a neccesity?

[Phsoco]

With regards to the Computer science requirements like the stats and programming languages, how would I go about meeting these requirements? Would I just look at books which involve learning Finance in relation to R for example? In a real world setting what would you need these languages for?

[smangalisomhlongo]

Hey Dimitri under Computer Science, what books do you recommend for matlab SPSS, R, python, Sas and machine learning?

[nmatsaba]

How long did you take to get where you are?

[michill]

Hi Dimitri, thanks for taking the time to provide this great content,

I'm heading into my final year of a mathematics, computer science and finance combined degree and have had exposure to a broad range of these topics, but I have a couple questions which I think would help me to select subjects next year.

(1) Would taking a course on big data mining be of much use for the potential fields a 'quant' would work in?
(2) If I choose a computer science course which is a semester long group based project I can graduate with a double major in computer science and statistics, but I am considering taking a level 4 course on statistical machine learning in its place. Given I have already completed a level 3 course on artificial intelligence would this higher level knowledge be beneficial or would obtaining the double major be viewed more favourably by employers?
(3) I have had brief exposure to PDE's in a second year course I did on DE's, but I have forgotten a great deal of what I have learned since then. To what level should I try to self teach myself on these topics to be up to standard as I don't have room to take a PDE's course next year.
(4) Do you think the qualifications I'll have in a years time will be enough to break into the industry as a 'quant' considering I will have all of this base knowledge covered?

Thanks again!

[tobiassteindl]

Hey Dimitri great video! How would you suggest to go about learning these topics, do you have any recommended resources?