Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher-quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data.
9 reviews for Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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Marina –
The book is amazing and very useful, for beginners also. The most valuable from my point of view is presence of code both for R and Python, which helps understand the syntax better for one language if you know another.
Stephen Martin –
This is a very good book to begin your DS stats journey with. I learned more from this book than I did in my DS grad school classes. It covers the basics you’ll need everyday in a practical way.
M. W. Hefner –
I’ve taken many stats classes, most of them using R, at the undergraduate and graduate level, and I really wish I found this book before I did. I picked this book up as a refresher, and not only did it succinctly describe all and a bit more of what I learned in those courses, but it has excellent “further readings,” great clarifying synonym lists when it defines “key terms,” and is very readable. Literally blown away.
Jonathan –
I had purchased a new physical copy of the book, and realized there were several pages that were blank and missing. I contacted O’Reilly about the problem and they were extremely quick with a resolution! They were able to give me a different copy so I could read it without the missing pages. The content of the book itself is good, except in all black and white, which doesn’t bother me personally but may bother someone else when it comes to the graphs. I think the R and Python content are both great, and it keeps the code concise and quick to the point. Great for R beginners, but for python users I would recommend a little more experience. As for the math parts, its great for those who are new to statistics and gives easy to read explanations, and a great refresher for those who just want to review some of the concepts. I especially like the sections provided for further reading, which have been helpful.
Cabiria –
I got this because I am taking a data analytics course that is not explained that well and I need to fill up my gaps in statistics. It is a good book
Farshad E. –
Good content/low quality print
denverteach –
Very good book- covers more than just implementing same old tactics.
Read and think –
What a great book! The authors did a marvellous jobs in packing an incredible amount of information in very little pages AND doing it in a very pleasant style that is direct, informative, and extremely clear.
I have read many books in statistics. I can tell you there are very very few written so well and so pleasant to read.
And to top it all, it is one of the very few book of statistics for non-mathematician that *correctly* explain the p-value and t-test. Many statisticians *still* don’t understand what that “significance test” really mean. But these authors do understand it very well and this is very important for anyone new to statistics to know this test correctly and in the hand of these authors they *will* learn it correctly.
Thanks a lot to the authors. You did a fabulous job.
José Luis –
Buen libro con un excelente contenido temático