# Math/Stats rescources Recommendation

What would be the best Math/Stat resources to go beyond the data scientist path?

This year I have completed some early uni/college math and statistics coursses.

I am currently up to linear regression section.

Any recomendation

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This would help.

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Itâ€™s not clear to me what you mean with Math/Stat.

In any case, if you want to learn mathematics â€” real mathematics (not the one geared towards machine learning (which is frequently hand-wavy and in many cases just applied mathematics)) â€” properly and get a very solid foundation, my best recommendation is How to Prove It: A Structured Approach.

It was the most influential book in my career as a student, and it helped polish my reasoning mechanisms overall.
This book equips the reader with a skill set to understand the logical components of mathematics, to reason in that same manner, and it boosts critical thinking overall, I would say.

Hereâ€™s an excerpt from the authorâ€™s academic personal page:

Many high school students view mathematics as a collection of formulas to be used to calculate numerical answers. To succeed in college-level mathematics, they must learn to think of mathematics as involving reasoning, rather than merely calculation. In advanced undergraduate courses, they must learn to express their reasoning in the form of mathematical proofs. In my teaching, I try to help students make this transition from calculation to reasoning to proofs. I put particular emphasis on making sure students understand the meaning of mathematical language and the importance of using that language precisely.

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Very liked this books for understanding principles of statistic and linear algebra - simple and and accessible about complex things

1. Principles of Managerial Statistics and Data Science
Roberto Rivera 2020 John Wiley & Sons
Identifiers: LCCN 2019032263 (print) | LCCN 2019032264 (ebook) | ISBN
9781119486411 (hardback) | ISBN 9781119486428 (adobe pdf) | ISBN 9781119486497 (epub)

2. Linear Algebra and Its Applications SIXTH EDITION
David C. Lay Steven R. Lay Judi J. McDonald
2021 Pearson Education
ISBN 978-0135882801, 013588280X

You can find its in free access in the Internet

May be little outside, but all data science must have clean data.
Very useful book with practical example of regular expression for bash, Python, R, perl and Java:
REGULAR EXPRESSIONS Pocket Primer by Oswald Campesato
2019 ISBN: 978-1-68392-227-8
Strongly recommend

Very useful book for understanding KDE - comparison histograms and KDE
Nonparametric Kernel Density Estimation and Its Computational Aspects
by Artur Gramacki
2018 ISBN 978-3-319-71687-9
https://doi.org/10.1007/978-3-319-71688-6