Think Stats: Exploratory Data Analysis

I really liked this book. The author did a really good job. It's a mixture of Python and statistics so some previous background in both will allowing you to benefit entirely from reading this book. Especially prior experience with Python will help you understand the code used by the author, as it is not a simple one. He often uses wrapper functions and class inheritance, so if this doesn't ring a bell, I suggest learning a bit of Python first. Otherwise you can skip the programming parts, but I think you will lose a large part of the book's value.Statistics here is more basic than Python code, for sure. But it does serve well as a introduction to statistical analysis. A software developer wanting to start learning statistics is probably a good candidate for this book. But not the other way around. After reading it I think I still prefer to use R to generate probability density plot, than Python.Anyway, it is almost a must read for anyone on their patch to data scientist career. It not long or super expensive, so if you are interested in stats and Python, just read it.

This book is a great introduction to statistics for any person with basic programming skills. The main issue, when you tackle any math-heavy stuff - is that you are bombarded with a huge amount of formulas, but can't "feel" the practical side of the subject.In this book, you have a simple way to practically and swiftly try each new concept through Jupyter Notebook interactive examples. Transform the data, draw a chart - observe results. The fastest feedback loop you can get learning any math stuff.If you want to get the best results from the book, spend some time and setup Jupyter Notebook, or just run them directly in Google Collab service. Just reading the book without practically running all the examples just a waste of time.

This is a very useful, for the intuitive programmer or mathematician who can program in python already, book, as a lead into statistics. I found that though it gives concrete wisdoms on principles of working with data, the book seems to me heavily on the appreciation of information with cognizant memorization in mind. Or simply, I personally got bored and needed to write a bunch of memos towards the end of the book, to continue repeating what I felt I wasn't interested in, because it was easier than enough. Speaking of this, the pedagogy looks to me that it turns to the idea of repetition of learning along a spectrum or curve where one increments there ability to perform statistical processing of statistically calculable theoretical knowledge and significant intuitively in this respect.The book got too long in the amount of information I expected myself to remember, somewhat brief on theory, - so dense and this may be due to my lack of discipline. It is not a sparse book, or incapable of teaching.Rather, the book is incredibly utilizable of an introduction to auto-didactic gains, expedient in the moment one chooses to endure this encoded travail or. A great work from the author, able to let the user show with one's already experienced hands that they can get on the series objects integrable or otherwise. Cf what students have learnt already in algebra for series. This is a stats book so there's graphing and interpretations or cyphering of data as a very practicable path.editing at 11/11/2023: I took a break from this book for a while, because the code was too complicated, hoenstly.The truth I found out is after spending time with bill lubanovic's book 'introducing python: modern computing in simple packages', that the book does help with serious data skills such as: parsing code, understanding an argument as you read it, so understanding the topic (stats) in general, comprehension of OOP with statistics and visa versa.So with that in mind, I found out that I got the idea of it providing code w/ formula to snap tables (contemplatively or otherwise) comprehending textually as a colloquial pythonista based business logic. it clarifies it's position halfway through to boost conscientious intel (objects etc.). like, what can be safely done with data as a decision in your thought process etc? that's what I think it's useful for, as a statistic solution, which is helpful.but with theory, truthiness, and amenity enjoy pushing around data at a broad level.(before I had finished the book, but left half the problems to miscommunication (python fluency))

I love this book. Not only does it illustrate the concepts well, but it's well-written (funny even) and very concise and informative. I bought it to review stats concepts and see the python programming examples, but I think it could serve as a first/ introduction to stats book as well. The author has a wonderful ability to really distill information and teach via examples. This book served me well and I still use it as a reference all the time.

this book covers all points nicely like multivariate analysis, graphics and others Code examples are given in all cases and data sets are carefully selected I benefitted a lot from this book in kaggle competitions thanks.

Please do not ignore the one star reviews. They are all around the premise that the book does not teach python as the author uses his own custom functions that cannot be used elsewhere. Instead check out data science form scratch, you could learn some stats and python there. In this book you learn neither

The book generally explains the concepts well, but could provide more details and more examples. I found the code actually very hard to understand, because the function and variable names are often missing clarity. If the code were improved, it would facilitate the reader's learning greatly.That being said, for the price on Kindle, I found the content and format extremely useful to start getting practical experience via the exercises. Since this is based on a very friendly and popular tool, Jupyter, it is a great introduction to that application.

Very accessible presentation of stats fundamentals and how to apply them. The accompanying thinkstats2 and thinkplot libraries are a common go-to tool for my work.

Super Buch für Einsteiger!

Muy bueno el libro y llego en el tiempo especificado y muy bien. Con algunos detalles en el libro, pero bien.

Só fala de Python. A grande maioria dos exemplos de notebook do python falham!

Good book with exercise and a progression in complexity.

A very good book for stat and EDA,well written and explained with Python codes..very helpful for aspiring data scientists.