Library
12 items in the Thuztra Learn library carry the topic careers: 12 reference articles. They are gathered here so one idea can be met in every form it takes, which is usually the difference between recognising a term and being able to use it.
12 of them have pages of their own, linked below. Everything else is listed with the summary its author wrote.
Nine books for quant interviews, grouped by the stage they help with, plus what the Green Book and Heard on the Street each are, from the author and catalogue records. No book problems copied.
How to choose quantitative finance interview prep by the stage you are at: what each stage needs, a self-test for each, and what six prep sites say about themselves.
Not from a top college? What Jane Street, Citadel Securities, Optiver and WorldQuant say on their own pages about degrees, GPA, tests and who can apply, plus what is free to practise on today.
The subjects to learn, the Indian degrees and entrance tests that teach the maths, free courses from Indian institutes, and Indian market data to practise on. Every link goes to the official page.
The subjects, UK and European master's programmes that teach the maths, a free course, public data to practise on, and a self-test, with official links.
Seven methods for quant probability interview questions, from Bayes' rule to first-step analysis, each with a worked question and problems to practise.
The five kinds of quantitative finance course, what each is good for, what named providers say on their own pages, a section for learners in India, and checks to run before you pay.
Eight quantitative finance projects in Python, from an option pricer to a yield-curve PCA. Each has the maths, free data, a check that proves it works, and the trap that makes it worth showing.
The full quantitative finance syllabus in seven layers, from school maths to stochastic calculus, risk and code, with prerequisites and a self-test for every layer.
What to learn, in order, for a quant trader, researcher, developer or analyst. One shared start, four branches, and a plain list of which steps Thuztra Learn teaches and which it does not.
Same statistics and code, different data: weak signals, drifting rules and rivals who trade away what you find. Two worked examples, and how to move across.
What a software engineer, a quant developer and a quant researcher each build, how each knows the work is right, and one worked pricing test.
The ideas this one is most often carried alongside, ranked by how much of each collection the two share.