Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Original price was: $79.99.$17.49Current price is: $17.49.
PDF 14,72 MB • Pages: 361
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.
12 reviews for Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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WC (verified owner) –
Glad to get the python scripts for the content. I was expecting a color print pages but this is black and white.
Carlos A. (verified owner) –
Lo compre hace un mes por menos del valor que tiene ahora incluyendo el “descuento de hot sale”.
El libro es bueno pero recomiendo esperar a fechas de baja demanda.
Farshad E. (verified owner) –
Good content/low quality print
H.P.J.M. (verified owner) –
This book explores traditional statistical concepts (median, correlation, distributions etc) before moving on to machine learning models (the traditional, statistical kind like logistic regression and trees, not neural networks). Both classification and regression tasks are explored.
The book is broken down into fairly digestible sections, where each section states the idea, before exploring it with both R and Python snippets and some recurring data sets. Data output is in R.
In general, the quality of writing is good and I particularly liked how the authors pointed out where something useful in classical statistics might not be particularly relevant for data science or machine learning (e.g. p-values). The book is very practical in that sense, and I appreciate the more nuanced details about some real-life problems you might face (like the “rare-class” problem).
But I think some things could be improved. First, the authors seem keen to state a formal, mathematical explanation of a concept but don’t always bring it to life with an example. For someone not trained in stats, that can be a little daunting (they don’t state what background knowledge they expect).
Second, I think they try to squeeze a bit too much in that isn’t really needed. For example, they talk about the F-statistic briefly but almost as a reference. I was left none the wiser as to how I can use it in my work.
My suggestions to the authors would therefore be to: bring the concepts to life a bit more and connect more of the dots. Otherwise, a worthwhile book if you are into data science.