Resources

Last resource added: Dec 1, 2023.

General	Description
Dimitri Bianco	YouTube channel talking about his journey getting a job in quant. He shares various resources, advices, and stories.
Reddit AMA	Ask Me Anything - a quant answering questions about their interview process, career, and background.
Various pages on GitHub for internships	Google "quant internship github" and you will see various pages on opportunities. This works for SWE and Data Science jobs as well.
PyQuant News	Twitter page dedicated to beginners in Quant. Offers advices and introductions to state-of-the-art tools.
How The Economic Machine Works	A YouTube video about how the economy works. Well explained by Ray Dalio.

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Interview Prep	Description
LeetCode Blind75	Beginner LeetCode questions for coding interviews or online assessments. Blind 75 is a good way to get started and covers a variety of topics.
NeetCode	Another LeetCode prep. NeetCode YouTube explaining variety of problems and topics.
QuantGuide	Practicing quant interview questions. Similar to LeetCode.
ZetaMac	Fast paced arithmetic drills. Firms like Optiver has this as part of their interview process.
StrataScratch	Learn and practice SQL questions. Real interview questions from F500 companies
Green Book	Book for quant-style interview questions. Often referred to as "Green Book".
Ace the Data Science Interview	Book for data science interview prep. Has relevant resources for quant interviews as well.
LaTeX Resume Format	Pick minimal LaTeX formats. Consistent formatting is a lot easier on LaTeX.

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Educational	Description
Statquest	One of the best educational YouTube channel on learning statistics, data science, and machine learning.
The Missing Semester of Your CS Education	A free, online, self-paced course from MIT. Teaches the terminal, bash, git, and other miscellaneous things that you typically don't learn in school, but can be useful to know prior to your tech/quant internships.
Seeing Theory	Great interactive visualizations for concepts in probability and statistics.

Note: Feel free to look around the GitHub page. There are various resources from past years as well.

Recommended Courses

Here are some upper level courses to take at BU.

Math and Statistics

• MA415 Data Science in R
• MA416 Analysis of Variance
• MA575 Linear Models
• MA577 Mathematics of Financial Derivatives
• MA583 Introduction to Stochastic Processes
• MA585 Time Series and Forecasting
• MA751 Statistical Machine Learning

Computer Science

• CS506 Data Science Tools and Applications
• CS541 Applied Machine Learning
• CS542 Principles of Machine Learning

Economics and Questrom

• EC445 Economics of Risk and Uncertainty
• FE459 Computational Techniques for Finance
• Any course on econometrics

Miscellaneous

• MET AD587 / PY538 Interdisciplinary Methods for Quantitative Finance
• PY541 Statistical Mechanics

Contributions

You must use the same format as previously added resources.

Resource - Short name of resource, with embedded link
Description - Short description of resource, what it can be used for

Contributing via Issues

You may submit resources or anything you find useful via GitHub issues. Please follow the template above and use the example issue as a guide.

Issues

If you spot a problem with the resources (i.e. outdated, bad link, etc.), please open a new issue and label it as either bug or invalid.