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Bibliographische Detailangaben
Beteilige Person: Pierson, Lillian (VerfasserIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Hoboken, NJ for dummies, a Wiley brand [2021]
Ausgabe:3rd edition
Schlagwörter:
Links:https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6707824
https://swbplus.bsz-bw.de/bsz1767997523cov.jpg
https://www.gbv.de/dms/bowker/toc/9781119811558.pdf
Abstract:Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Data Science -- Chapter 1 Wrapping Your Head Around Data Science -- Seeing Who Can Make Use of Data Science -- Inspecting the Pieces of the Data Science Puzzle -- Collecting, querying, and consuming data -- Applying mathematical modeling to data science tasks -- Deriving insights from statistical methods -- Coding, coding, coding - it's just part of the game -- Applying data science to a subject area -- Communicating data insights -- Exploring Career Alternatives That Involve Data Science -- The data implementer -- The data leader -- The data entrepreneur -- Chapter 2 Tapping into Critical Aspects of Data Engineering -- Defining Big Data and the Three Vs -- Grappling with data volume -- Handling data velocity -- Dealing with data variety -- Identifying Important Data Sources -- Grasping the Differences among Data Approaches -- Defining data science -- Defining machine learning engineering -- Defining data engineering -- Comparing machine learning engineers, data scientists, and data engineers -- Storing and Processing Data for Data Science -- Storing data and doing data science directly in the cloud -- Storing big data on-premise -- Processing big data in real-time -- Part 2 Using Data Science to Extract Meaning from Your Data -- Chapter 3 Machine Learning Means . . . Using a Machine to Learn from Data -- Defining Machine Learning and Its Processes -- Walking through the steps of the machine learning process -- Becoming familiar with machine learning terms -- Considering Learning Styles -- Learning with supervised algorithms -- Learning with unsupervised algorithms -- Learning with reinforcement -- Seeing What You Can Do
Beschreibung:Description based on publisher supplied metadata and other sources
Umfang:1 Online-Ressource (xii, 416 Seiten) Illustrationen, Diagramme
ISBN:9781119811664
9781119811619