The best Mathematical & Statistical Software books: Reddit reviews

From 3.5 billion Reddit comments

This book is a amazing: Discovering Statistics Using R by Andy Field

If you are doing self-study, it is easy to lose momentum. This book is hilarious, personal, and transcends the textbook genre.

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I'm not sure how advanced your statistical background is yet, but the best purchase I ever made was <strong>An Introduction to Statistical Learning: with Applications in R</strong> by Hastie et. al.

It gives you a basic, intuitive background on various machine learning methods without getting into nitty gritty probability or statistical theory. And it has really helpful problem sets at the end of each chapter that shows you how to apply each of them in R, and which packages you'll need.

Seriously, that thing is like my bible. The authors have made a pdf available on the internet as well, but I'd highly suggest springing for a hard copy. It's pretty cheap as far as textbooks go.

Other than that, I've never been one to learn through online courses or books. I'd second /u/veeeerain and just do a bunch of projects using datasets from sources like Kaggle. Maybe start a blog to keep a portfolio of all the cool things you do. ;)

I'd look at applying to S2DS if you can get in. C++ and ROOT have very little relevance nowadays in data science (or, ever, really). Knowing the fundamentals of some of the ML algorithms in ROOT wont hurt you, but you need to learn scikit-learn, numpy, scipy, etc in Python as a bare minimum.
Plenty of (free) courses on Coursera, too.
It's an extremely competitive market, and whilst some of the stuff she's done will be useful, she'll be (in many employers' eyes, at least) behind the people graduating with Masters in Data Science/Comp Sci.
Recommend this, too:
https://www.amazon.co.uk/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=asc\_df\_1461471370/?tag=googshopuk-21&amp;linkCode=df0&amp;hvadid=310848077451&amp;hvpos=&amp;hvnetw=g&amp;hvrand=3789835037830509153&amp;hvpone=...

Fields Andy, &quot;Discovering Statistics&quot; might be what you're looking for.

It provides great intuition as to what test you can use given your data and thoroughly explains the working of each without diving unnecessarily deep into the math. (As far as I remember, the usage of Greek symbols is kept to a minimum)

There are several variants of this book, each directed at a particular statistical software (such as R and SPSS). I'm currently using the version for R and can highly recommend it!

I learned in classes, so I may not be the absolute best person to offer recommendations because I did not self-teach. DataCamp was used for my first class that was taught in R, but if you're doing it yourself, it will not be free. Discovering Statistics Using R by Andy Field is also a common recommendation -- https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469

Reminds me of Andy Fields book where one of the stats examples was about putting eels up someone’s butt https://www.amazon.com/dp/1446200469/ref=cm_sw_r_cp_awdb_btf_t1_fJ5LFb9CAZ9BE?_encoding=UTF8&amp;psc=1

Is it this one? https://www.amazon.com/gp/product/1461471370?pf_rd_r=72698RKSQ7WPVPNHCFBW&amp;pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee

ahhh sure, self learning statistics was the best thing I did as a hobby. I don't know about your use case(professional or hobbyist) and how much time you have on hand , but what I did was after learning the basic python libraries numpy , matplotlib from the coursera specializations and youtube I mentioned in main thread , I took some break from learning and answered as much quality question as possible on stack overflow related to data analysis(was good for my numpy, matplotlib, seaborn and also my ego coz my reputation was increasing :) )and started a visualization blog.Although it wasn't necessary it helped me a lot and I had enough time. I didn't know R till then , and I started this stats book by Andy Field as I wanted more of application and somewhat less of mathematical derivations(ISLR is good too). It is in R , but I studied the theory portion from it and implemented all its code in Python by myself and doing a lot of research. After completing this 1000 page book in Python , I understood python is good for ml and data science but R is best when it comes to stats.I am presently arranging all the python code I learnt and did while doing this book to push on github both in R and Python(it's wonder no one has redone this book in python).

So , tl;dr ,I did this specialization , and then read Andy Field's R book.And I referred to kaggle and fivethirtyeight along with other sites sometimes for datasets and articles.

I've seen Larry Wasserman's All of Statistics recommended as an intro to statistics for mathematically competent readers (e.g. it's the textbook for the Berkeley masters'-level intro statistics class). I haven't read it personally though so can't speak directly to quality or the level of rigor.

I’m a huge fan of this book, the examples are in JavaScript, python, and ruby, but if you know one you’ll be able to follow them all. This book has helped me a lot. I recommend after you finish the section you go on leetcode and tackle some easy problems and later on some mediums

https://www.amazon.com/Common-Sense-Guide-Structures-Algorithms-Second/dp/1680507222

CLRS is very verbose

but this is great

Yes. This book: Applied Predictive Modeling

I read most of this book https://www.amazon.com/gp/product/1680507222/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&amp;psc=1

and then googled/youtubed the little bit it didn't cover.

I highly recommend doing it that way. It's a great, easy to digest book on the topic and I feel I got a great functional understanding of DSA from it.

A Common-Sense Guide to Data Structures and Algorithms, Second Edition: Level Up Your Core Programming Skills 2nd Edition
by Jay Wengrow

https://www.amazon.com/Common-Sense-Guide-Structures-Algorithms-Second-dp-1680507222/dp/1680507222/ref=dp_ob_title_bk

This is the one, unfortunately its not entirely in ruby though if that's what you were looking for.

Also, if curious, here is a presentation by Andy Field on these sorts of models: https://www.youtube.com/watch?v=UDnGqW0S4cA

He is a book which is very accessible to undergraduates and walks you through how to do them in R: https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469

I believe he also has a book using SPSS

Try Andy Field’s book _ Discovering Statistics Using IBM SPSS Statistics_. You don’t have to necessarily be an SPSS user to follow along, because he does a really good job explaining the concepts. Also, _ Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences_ was basically the stats bible in my PhD program (I/O Psychology).

if you just want to get a basic idea of DS&A i'd recommend "The Bible of Algorithms and Data Structures: A Complex Subject Simply Explained". it's not an in depth book and is pretty short, but will give you a simple overview of different types of data structures before you go into the course so that way you have a little exposure.

you can also opt for a more in-depth book if you want to get ahead. Personally i find reading written text to be the optimal choice for learning as opposed to videos. videos can definitely be better if you're having trouble with the actual implementation part, but i find reading text to be a good way to memorize terminology which will definitely help your understanding moving forward.

https://www.amazon.com/dp/1680507222?psc=1&amp;ref=ppx\_yo2ov\_dt\_b\_product\_details

^ i purchased this book off amazon for like $30, i haven't gotten very far in the book yet but it has great reviews and all the explanations are easy to understand and aren't overly verbose

Of course this is all subjective and not everyone learns the same way, so i'd try out various resources and find what works best for you. Best of luck, I also take my DS class next semester and am trying to get a bit of a head start as well.

To be able to improve upon developing scalable algorithms, you must face a lot of problems that involves around the use of different data structures.

For book reco, I have this book and hands down sa author neto hindi complicated explanations niya sa mga data structures. You'll learn Big O here up to Graphs.

Well most of the time sa industry Naman Array and Hash Maps Ang common. So focus on that.

Tapos practice at least 3 leetcode easy questions per week.

https://www.amazon.com/Common-Sense-Guide-Structures-Algorithms-Second/dp/1680507222/ref=mp_s_a_1_2?crid=15SIKOZN8846U&amp;keywords=algorithms&amp;qid=1648134150&amp;sprefix=al%2Caps%2C1618&amp;sr=8-2

A Common–Sense Guide to Data Structures and Algorithms

First: I strongly recommend this book as introduction to algorithms and data structure, the writer did an amazing job simplifying a lot of concepts and tricks with great examples.
https://www.amazon.ca/gp/product/1680507222/

Second: Companies use algorithm as a test in their interview while their work has nothing to do with algorithms and if you search their code base you will not find any work related to algorithms; these companies simply don't know how to conduct an interview so they take the easy way throwing an algorithm and evaluate the candidate based on the algorithm result, the real life job of the .Net developer related more on how to write clean, maintainable, extendable code, the questions should be more on the design, security and the understanding of how .Net works.

Algorithms and data structure still an important skill for .Net dev, it helps to understand the underlying structure of the framework and how to deal with database properly. but it's not that crucial in a way they impose it in the interview: (solve algorithm in 10 minutes)

If you read my comment I gave you two books I recommend:

https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

(Try this book.)[https://www.amazon.com/Common-Sense-Guide-Structures-Algorithms-Second/dp/1680507222]
It found it to be very well written in a way that’s easy to understand for beginners. It starts off with basics about how RAM and stack works (enough information to understand efficiencies of algorithms). Then he goes through different algorithms and methods of getting the optimum solution for the problem. It’s also not language agnostic guide, as in he picks a different language for each example and breaks it down so that you can recreate same example in the language you are familiar with.

Is this the specific text for C949? Gotta say I love that they use books that are actually affordable. I like a tangible book, bought the D191 text for like 30 bucks.

A Common-Sense Guide to Data Structures and Algorithms, Second Edition: Level Up Your Core Programming Skills https://www.amazon.com/dp/1680507222/ref=cm_sw_r_apan_glt_fabc_T4K89K5T3Q5ASRDMZYM6

Data Science seems to have the more robust career path. I recommend supplementing your coursework with as much of both as you can. Strongly recommend the following: Statistical applications, applied math, programming in R and/or Python, PowerBI, and this book.

I went the Biostatistics route for grad school and am now working as a data scientist/biostatistician. Very happy with where I am professionally.

I'm a statistician (well epidemiologist) and I approve this message. This book is great , but probably more advanced that you need. Even just any intro to stats book should really help teach you probability and statistical fundamentals to analyze your data

A Common-Sense Guide to Data Structures and Algorithms by Jay wengrow:

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https://www.amazon.com/Common-Sense-Guide-Structures-Algorithms-Second-dp-1680507222/dp/1680507222/ref=dp\_ob\_title\_bk

I never thought that what I was doing was a good idea. I was just trying an experiment originally based on some quantitative research that I did. I know this might sound crazy, but if you really want to trade options successfully, it can pay to learn R and how to perform a variety of statistical tests. (If you're interested in statistics [or weightlifting] this is a very good book: https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/.) Finding an edge is kind of like searching for a needle in a haystack.

The challenge is that it's hard to get high-quality data, and even when you can, it's expensive. I took a chance and ran with my findings, based on course-grained, free, data. It worked, but I don't have the confidence to do this again. Also, the analytical work is slow, frustrating, and hard. It's the kind of thing that statisticians who work for investment banks, hedge funds, and prop trading firms do. I tried to fight fire with fire.

You can't just link to some random research paper and hope we'll read through that to understand what you want to do. State "the" equation clearly and also, clearly, what you want to do with it (e. g. the range of parameters you're interested in, the precision you need, &c.).

That being said, Euler's method is very rudimentary and there are much better ones, computationally efficient and with well-studied error estimates and stability. You'll have to dig into some numerical analysis book like Numerical Recipes just to avoid rooky mistakes (and look bad if you publish…).

Last but not least, Python certainly already has canned routines/libraries to integrate differential equations and you'll be better served using them. So dig into that too.

Good luck!

Hi, this book is an amazing resource for learning MATLAB and programming concepts in general. I highly recommend. To me, it's the ultimate MATLAB guide or "bible" so to speak.

https://www.amazon.com/Matlab-Practical-Introduction-Programming-Problem/dp/0124058760