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Carregando... Statistics for Data Scientists: 50 Essential Conceptsde Peter Bruce
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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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 Não foram encontradas descrições de bibliotecas. |
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Google Books — Carregando... GênerosClassificação decimal de Dewey (CDD)519.50285Natural sciences and mathematics Mathematics Applied Mathematics, Probabilities Statistical MathematicsClassificação da Biblioteca do Congresso dos E.U.A. (LCC)AvaliaçãoMédia:
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The first four chapters on Exploratory Data Analysis, Data and Sampling Distributions, Statistical Experiments and Significance Testing, and Regression and Prediction, respectively, were relatively straightforward and I was able to follow them fairly well. Most of the sections contain "Key Ideas" and "Key Terms" call-outs that I found quite helpful. I was bemused at the number of topics discussed only to be told, "but data scientists don't worry about this."
The final chapters on Classification, Statistical Machine Learning, and Unsupervised Learning felt like jumping into the deep end of the pool. I feel like I'd need a few more semesters of statistics before I was really comfortable with some of this stuff.
The R code snippets are fine, but jump straight to the author's github site and just download the examples. ( )