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  <title>Learning Data Science</title>
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  <namePart>Sam Lau</namePart>
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  <namePart>Joseph Gonzalez</namePart>
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  <namePart>Deborah Nolan</namePart>
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   <placeTerm type="text">Sebastopol, CA</placeTerm>
   <publisher>O’Reilly Media</publisher>
   <dateIssued>2023</dateIssued>
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  <extent>xx, 573 p. ; 18 cm x 23 cm</extent>
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 <note>As an aspiring data scientist, you understand why organizations rely on data for critical decisions—whether it's a company designing a website, a city deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills needed to sift through messy data into actionable insights. We call this the data science lifecycle: the process of collecting, processing, analyzing, and drawing conclusions from data.&#13;
&#13;
Learning Data Science is the first book to cover fundamental programming and statistics skills that cover this entire lifecycle. It's aimed at those who want to become data scientists or already work with data scientists, and for data analysts who want to cross the &quot;technical/non-technical&quot; divide. If you have a basic knowledge of Python programming, you'll learn how to process data using industry-standard tools like pandas.</note>
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  <topic>Komputer</topic>
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 <classification>006. 312 Lau l</classification>
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