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Data science /

Pierson, Lillian,

Data science /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data science /by Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind.
其他題名:	Data science for dummies
作者:	Pierson, Lillian,
其他作者:	Porway, Jake,
出版者:	Hoboken, NJ :John Wiley & Sons, Inc.,2017
面頁冊數:	xvi, 364 p. :ill., charts ;24 cm.
附註:	Includes index.
標題:	Information retrieval.
ISBN:	9781119327639

Data science /
Pierson, Lillian,

Data science /Data science for dummiesby Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind. - 2nd edition. - Hoboken, NJ :John Wiley & Sons, Inc.,2017 - xvi, 364 p. :ill., charts ;24 cm. - --For dummies.

Includes index.

Getting Started With Data Science -- Wrapping Your Head around Data Science -- Exploring Data Engineering Pipelines and Infrastructure -- Applying Data-Driven Insights to Business and Industry -- Using Data Science to Extract Meaning from Your Data -- Machine Learning: Learning from Data with your Machine -- Math, Probability, and Statistical Modeling -- Using Clustering to Subdivide Data -- Modeling with Instances -- Building models that Operate Internet-of-Things Devices -- Creating Data Visualizations that Clearly Communicate Meaning -- Following the Principles of Data Visualization Design -- Using D3.js for Data Visualization -- Web-Based Applications for Visualization Design -- Exploring Best Practices in Dashboard Design -- Making Maps from Spatial Data -- Computing for Data Science -- Using Python for Data Science -- Using Open Source R for Data Science -- Using SQL in Data Science -- Doing Data Science with Excel and Knime -- Applying Domain Expertise to Solve Real-World Problems Using Data Science -- Data Science in Journalism: Nailing Down the Five Ws (and an H) -- Delving into Environmental Data Science -- Data Science for Driving Growth in E-Commerce -- Using Data Science to Describe and Predict Criminal Activity -- The Part of Tens --Ten Phenomenal Resources for Open Data -- Ten Free Data Science Tools and Applications.

Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.

ISBN: 9781119327639 NT$774

John Wiley & Sons Inc, Eastern Dist Center One Wiley Dr, Somerset, NJ, USA, 08873SAN 200-2272

LCCN: 2017932294Subjects--Topical Terms:

215224
Information retrieval.

LC Class. No.: T58.5 / .P624 2017

Dewey Class. No.: 004

Data science /
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100 1 $a Pierson, Lillian, $e author. $3 796138
245 1 0 $a Data science / $c by Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind.
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250 $a 2nd edition.
260 $a Hoboken, NJ : $b John Wiley & Sons, Inc., $c 2017
300 $a xvi, 364 p. : $b ill., charts ; $c 24 cm.
490 0 $a --For dummies
500 $a Includes index.
505 0 $a Getting Started With Data Science -- Wrapping Your Head around Data Science -- Exploring Data Engineering Pipelines and Infrastructure -- Applying Data-Driven Insights to Business and Industry -- Using Data Science to Extract Meaning from Your Data -- Machine Learning: Learning from Data with your Machine -- Math, Probability, and Statistical Modeling -- Using Clustering to Subdivide Data -- Modeling with Instances -- Building models that Operate Internet-of-Things Devices -- Creating Data Visualizations that Clearly Communicate Meaning -- Following the Principles of Data Visualization Design -- Using D3.js for Data Visualization -- Web-Based Applications for Visualization Design -- Exploring Best Practices in Dashboard Design -- Making Maps from Spatial Data -- Computing for Data Science -- Using Python for Data Science -- Using Open Source R for Data Science -- Using SQL in Data Science -- Doing Data Science with Excel and Knime -- Applying Domain Expertise to Solve Real-World Problems Using Data Science -- Data Science in Journalism: Nailing Down the Five Ws (and an H) -- Delving into Environmental Data Science -- Data Science for Driving Growth in E-Commerce -- Using Data Science to Describe and Predict Criminal Activity -- The Part of Tens --Ten Phenomenal Resources for Open Data -- Ten Free Data Science Tools and Applications.
520 $a Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.
650 0 $a Information retrieval. $3 215224
650 0 $a Data mining. $3 184440
650 0 $a Information technology. $3 184390
650 0 $a Databases. $3 218042
650 7 $a COMPUTERS $x Data Processing. $2 bisacsh $3 538067
700 1 $a Porway, Jake, $e writer of introductory text. $3 796139