LEADER 00696nam0-2200253 --450 001 9910628100403321 005 20221122153850.0 100 $a20221122d1950----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $aNotarelle critiche$fSiro Solazzi 210 $aMilano$cGiuffrè$d1950 215 $a24 p.$d24 cm 300 $aEstratto da: Studi in onore di Emilio Albertario 676 $a340.54$v23$zita 700 1$aSolazzi,$bSiro$0236351 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910628100403321 952 $aBibl. Solazzi Busta S 330$b64375$fFGBC 959 $aFGBC 996 $aNotarelle critiche$92973261 997 $aUNINA LEADER 04698nam 22006735 450 001 9910369900003321 005 20201110140307.0 010 $a9781523150502 010 $a1523150505 010 $a9781484254943 010 $a1484254945 024 7 $a10.1007/978-1-4842-5494-3 035 $a(CKB)4100000009940204 035 $a(MiAaPQ)EBC5986808 035 $a(DE-He213)978-1-4842-5494-3 035 $a(CaSebORM)9781484254943 035 $a(PPN)252511581 035 $a(OCoLC)1140553193 035 $a(OCoLC)on1140553193 035 $a(EXLCZ)994100000009940204 100 $a20191126d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Decision Maker's Handbook to Data Science $eA Guide for Non-Technical Executives, Managers, and Founders /$fby Stylianos Kampakis 205 $a2nd ed. 2020. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2020. 215 $a1 online resource (154 pages) 300 $aIncludes index. 311 08$a9781484254936 311 08$a1484254937 320 $aIncludes bibliographical references. 327 $aChapter 1: Demystifying Data Science and All the Other Buzzwords -- Chapter 2: Data Management -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data Scientist (Without Being One) -- Chapter 6: A Short Introduction to Statistics -- Chapter 7: A Short Introduction to Machine Learning -- Chapter 8: Problem Solving -- Chapter 9: Pitfalls -- Chapter 10: Hiring and Managing Data Scientists -- Chapter 11: Building a Data-Science Culture -- Chapter 12: Epilogue: Data Science Rules the World -- Appendix A: Tools for Data Science -- . 330 $aData science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don?t realize is that data science is in fact quite multidisciplinary?useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker?s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker?s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. 606 $aData structures (Computer science) 606 $aData mining 606 $aBig data 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aData structures (Computer science) 615 0$aData mining. 615 0$aBig data. 615 14$aData Structures and Information Theory. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data/Analytics. 615 24$aBig Data. 676 $a005.7 700 $aKampakis$b Stylianos$4aut$4http://id.loc.gov/vocabulary/relators/aut$0973114 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910369900003321 996 $aThe Decision Maker's Handbook to Data Science$92214004 997 $aUNINA