9Natree · Tuesday · 9 min
[Review] Practical Statistics for Data Scientists (Peter Bruce) Summarized.
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Practical Statistics for Data Scientists (Peter Bruce)
- Amazon USA Store: https://www.amazon.com/dp/149207294X?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Practical-Statistics-for-Data-Scientists-Peter-Bruce.html
- Apple Books: https://books.apple.com/us/audiobook/data-analytics-for-beginners-a-practical-guide-to/id1255643144?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Practical+Statistics+for+Data+Scientists+Peter+Bruce+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/149207294X/
#Exploratorydataanalysis #Randomsamplingandresampling #Experimentaldesignandsignificancetesting #Regressionandclassification #Crossvalidationandclustering #PracticalStatisticsforDataScientists
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python is a technical reference and applied introduction to statistical thinking for data science. Written by Peter Bruce, Andrew Bruce, and Peter Gedeck, the second edition was published by OReilly in 2020 and expands the original R focused text with Python examples. Its purpose is not to provide a full theoretical treatment of mathematical statistics or a catalog of machine learning software. Instead, it identifies the statistical ideas that most directly affect how data scientists explore data, construct comparisons, build predictive models, and judge results. The book moves from descriptive and exploratory methods through sampling, experiments, statistical inference, regression, classification, machine learning, and unsupervised learning. Throughout, it frames methods in terms of practical decisions: what question is being answered, what assumptions are being made, and how an apparently plausible analysis can mislead. The result is a compact bridge between introductory statistics courses and the applied workflows used with modern analytical tools.
- Amazon USA Store: https://www.amazon.com/dp/149207294X?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Practical-Statistics-for-Data-Scientists-Peter-Bruce.html
- Apple Books: https://books.apple.com/us/audiobook/data-analytics-for-beginners-a-practical-guide-to/id1255643144?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Practical+Statistics+for+Data+Scientists+Peter+Bruce+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/149207294X/
#Exploratorydataanalysis #Randomsamplingandresampling #Experimentaldesignandsignificancetesting #Regressionandclassification #Crossvalidationandclustering #PracticalStatisticsforDataScientists
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python is a technical reference and applied introduction to statistical thinking for data science. Written by Peter Bruce, Andrew Bruce, and Peter Gedeck, the second edition was published by OReilly in 2020 and expands the original R focused text with Python examples. Its purpose is not to provide a full theoretical treatment of mathematical statistics or a catalog of machine learning software. Instead, it identifies the statistical ideas that most directly affect how data scientists explore data, construct comparisons, build predictive models, and judge results. The book moves from descriptive and exploratory methods through sampling, experiments, statistical inference, regression, classification, machine learning, and unsupervised learning. Throughout, it frames methods in terms of practical decisions: what question is being answered, what assumptions are being made, and how an apparently plausible analysis can mislead. The result is a compact bridge between introductory statistics courses and the applied workflows used with modern analytical tools.
