<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[author results for Gedeck, Peter,]]></title><description><![CDATA[author results for Gedeck, Peter,]]></description><link>https://gateway.bibliocommons.com/v2/libraries/sitka/rss/search?query=Gedeck%2C%20Peter%2C&amp;searchType=author&amp;origin=core-catalog-explore&amp;view=grouped</link><generator>RSS for Node</generator><lastBuildDate>Sun, 08 Mar 2026 10:00:50 GMT</lastBuildDate><item><title><![CDATA[AI-assisted Statistics for Data Scientists]]></title><description><![CDATA[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 third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows. This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects. Conduct exploratory analysis of data to improve quality and model outcomes Apply sampling and experimental design to reduce bias and answer questions with clarity Use regression to understand data-generating processes and detect anomalies Build predictive models using classification, clustering, and unsupervised learning with unbalanced data.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C129942914</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129942914</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[eng]]></category><dc:creator><![CDATA[Bruce, Peter]]></dc:creator><pubDate>Sat, 31 Jan 2026 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129942914049</comments><format>EBOOK</format><subtitle>50+ Essential Concepts Using R, Python, &amp; GenAI</subtitle><language>eng</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9798341666283/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Statistics for Data Science and Analytics]]></title><description><![CDATA["Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It involves using mathematical models and techniques to make sense of data and draw conclusions from it. Statistics can be applied to a wide variety of fields, including science, medicine, economics, engineering, social sciences, and more."--]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C129293706</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129293706</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[eng]]></category><dc:creator><![CDATA[Bruce, Peter C.]]></dc:creator><pubDate>Fri, 31 Jan 2025 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129293706049</comments><format>EBOOK</format><subtitle></subtitle><language>eng</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9781394253814/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Statystyka praktyczna w data science]]></title><description><![CDATA[Metody statystyczne są kluczowym narzędziem w data science, mimo to niewielu analityków danych zdobyło wykształcenie w ich zakresie. Może im to utrudniać uzyskiwanie dobrych efektów. Zrozumienie praktycznych zasad statystyki okazuje się ważne również dla programistów R i Pythona, którzy tworzą rozwiązania dla data science. Kursy podstaw statystyki rzadko jednak uwzględniają tę perspektywę, a większość podręczników do statystyki w ogóle nie zajmuje się narzę̜dziami wywodzącymi się̜ z informatyki. To drugie wydanie popularnego podrę̜cznika statystyki przeznaczonego dla analityków danych. Uzupełniono je o obszerne przykłady w Pythonie oraz wyjaśnienie, jak stosować poszczególne metody statystyczne w problemach data science, a także jak ich nie używać. Skoncentrowano się też na tych zagadnieniach statystyki, które odgrywają istotną rolę w data science. Wyjaśniono, które koncepcje są ważne i przydatne z tej perspektywy, a które mniej istotne i dlaczego. Co ważne, poszczególne koncepcje i zagadnienia praktyczne przedstawiono w sposób przyswajalny i zrozumiały również dla osób nienawykłych do posługiwania się statystyką na co dzień.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C128728499</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C128728499</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[pol]]></category><dc:creator><![CDATA[Bruce, Peter C.]]></dc:creator><pubDate>Sun, 31 Jan 2021 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/128728499049</comments><format>EBOOK</format><subtitle>50 kluczowych zagadnień w językach R i Python</subtitle><language>pol</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9788328374287/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Shu ju ke xue zhong de shi yong tong ji xue]]></title><description><![CDATA[Detailed summary in vernacular field.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C128444965</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C128444965</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[chi]]></category><dc:creator><![CDATA[Bruce, Peter C.]]></dc:creator><pubDate>Sun, 31 Jan 2021 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/128444965049</comments><format>EBOOK</format><subtitle>Practical statistics for data scientists</subtitle><language>chi</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9787115569028/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Praktische Statistik für Data Scientists, 2nd Edition]]></title><description><![CDATA[Statistische Methoden sind ein zentraler Bestandteil der Arbeit mit Daten, doch nur wenige Data Scientists haben eine formale statistische Ausbildung. In Kursen und Büchern über die Grundlagen der Statistik wird das Thema aber selten aus der Sicht von Data Scientists behandelt. Viele stellen daher fest, dass ihnen eine tiefere statistische Perspektive auf ihre Daten fehlt. Dieses praxisorientierte Handbuch mit Beispielen in Python und R erklärt Ihnen, wie Sie verschiedene statistische Methoden speziell in den Datenwissenschaften anwenden. Es zeigt Ihnen auch, wie Sie den falschen Gebrauch von statistischen Methoden vermeiden können, und gibt Ratschläge, welche statistischen Konzepte für die Datenwissenschaften besonders relevant sind. Wenn Sie mit R oder Python vertraut sind, ermöglicht diese zugängliche, gut lesbare Referenz es Ihnen, Ihr statistisches Wissen für die Praxis deutlich auszubauen.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C129618897</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129618897</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[ger]]></category><dc:creator><![CDATA[Bruce, Peter]]></dc:creator><pubDate>Sun, 31 Jan 2021 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129618897049</comments><format>EBOOK</format><subtitle/><language>ger</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9781098129224/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Dēta saiensu no tame no tōkeigaku nyūmon]]></title><description><![CDATA["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 practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning." --]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C128445100</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C128445100</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[jpn]]></category><dc:creator><![CDATA[Bruce, Peter C.]]></dc:creator><pubDate>Fri, 31 Jan 2020 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/128445100049</comments><format>EBOOK</format><subtitle>yosoku, bunrui, tōkei moderingu, tōkeiteki kikai gakushū to R/Python  puroguramingu</subtitle><language>jpn</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9784873119267/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Practical Statistics for Data Scientists, 2nd Edition]]></title><description><![CDATA[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.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C129618002</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129618002</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[eng]]></category><dc:creator><![CDATA[Bruce, Peter]]></dc:creator><pubDate>Fri, 31 Jan 2020 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129618002049</comments><format>EBOOK</format><subtitle></subtitle><language>eng</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9781492072935/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Practical Statistics for Data Scientists]]></title><description><![CDATA[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 practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis, data and sampling distributions, statistical experiments and significance testing, regression and prediction, classification, statistical machine learning, and unsupervised learning.]]></description><link>https://sitka.bibliocommons.com/v2/record/S49C129294776</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129294776</guid><category><![CDATA[BK]]></category><category><![CDATA[eng]]></category><dc:creator><![CDATA[Bruce, Peter C.]]></dc:creator><pubDate>Fri, 31 Jan 2020 00:00:00 GMT</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129294776049</comments><format>BK</format><subtitle>50+ Essential Concepts Using R and Python</subtitle><language>eng</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9781492072942/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item><item><title><![CDATA[Data Mining for Business Analytics]]></title><link>https://sitka.bibliocommons.com/v2/record/S49C129616315</link><guid isPermaLink="true">https://sitka.bibliocommons.com/v2/record/S49C129616315</guid><category><![CDATA[EBOOK]]></category><category><![CDATA[eng]]></category><dc:creator><![CDATA[Shmueli, Gali]]></dc:creator><pubDate>Invalid Date</pubDate><comments>https://sitka.bibliocommons.com/item/comment/129616315049</comments><format>EBOOK</format><subtitle></subtitle><language>eng</language><image_url>https://secure.syndetics.com/index.aspx?isbn=9781119549864/MC.GIF&amp;client=bclibcoop&amp;type=xw12&amp;oclc=</image_url></item></channel></rss>