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Free ePub æ mobi Practical Statistics for Data Scientists ñ 50+ Essential Concepts Using R and Python ✓ feedmarkformulate

eBook É 50+ Essential Concepts Using R and Python è Peter Bruce

eBook É 50+ Essential Concepts Using R and Python è Peter Bruce Whats important and whats notMany data science resources incorporate statistical methods but lack a deeper statistical perspective If youre familiar with the R or Python programming languages and have some exposure to statistics this uick reference bridges the gap in an accessible readable formatWith this book youll learnWhy exploratory data analysis is a key preliminary step in data scienceHow random sampling ca Arrived in perfect condition and timely

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Free ePub æ mobi Practical Statistics for Data Scientists ñ 50+ Essential Concepts Using R and Python ✓ feedmarkformulate ✓ ❮PDF / Epub❯ ☃ Practical Statistics for Data Scientists: 50+ Essential Concepts Using R a N reduce bias and yield a higher uality dataset even with big dataHow the principles of experimental design yield definitive answers to uestionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniues for predicting which categories a record belongs toStatistical machine learning methods that learn from dataUnsupervised learning methods for extracting meaning from unlabeled data The content of this book gets 5 stars I especially appreciate the author including Python this time around However O'Reilly decided to print this book in black and white That isn't acceptable for a 50 book where you need to be able to distinguish between colored lines on chartsThankfully I have an O'Reilly subscription where I can view the digital book in color as I imagine the author intended

Peter Bruce è Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python text

Practical Statistics for Data Scientists 50+ Essential Concepts Using R and PythonStatistical 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 In my view this book’s strength is the deep knowledge of the authors added by the ability to explain key points in a few sentencesI love the freuent uestion and answer to “Is it important for Data Scientists?” Data Science is such a wide and deep topic that any pointers are extremely welcomeWho is this book for? I believe it’s for intermediate to advanced Data Scientists There’s so much “wisdom” that any reader should find value in the bookThe code snippets are in Python and R Sometimes those snippets are enough eg power analysis Sometimes the reader needs different sources to dig deeper eg bootstrapping where I highly recommend infer in R I believe this “compressed” approach is smart Data science is too wide and deep and we must be able to dig deeper on our ownIn other words for a beginner the code is often not enough to learn a new concept Experienced Data Scientists should be able to judge from the code snippet if it’s enough Personal highlights One of the best explanations on effect size I’ve ever seen page 135Sometimes the statistics community uses different terms than the machine learning community The authors seem to understand both page 143For example in the last 10 years or so we’ve seen a trend in statistics that favors data and simulations over classical probability theory and complex tests But why would we use permutations in a hypothesis test? On page 139 the authors explain in succinctly in two sentencesIn fact the authors have a deep knowledge of resampling and how to use simulations over classical testsThe authors don’t try to confuse you I’ve seen new books which used two pages to explain recall and then two pages to explain sensitivity In this book they don’t do it Recall is the same as sensitivity page 223Another example is “Power and Sample Size” In only four pages the reader probably gets a good idea of the four moving parts sample size effect size significance level and power This stuff is hard and explaining it well is even harderWhen cluster algorithms tend to give the same results and when notFunny “regression comes with a baggage that is relevant to its traditional role ”page 161Why would a Data Scientist care about heteroskedasticity? Page 183Kudos