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The Data Science Handbook
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A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline
Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems.
The book also features:
✓ Extensive sample code and tutorials using Python™ along with its technical libraries
✓ Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems
✓ Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity
✓ A wide variety of case studies from industry
✓ Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed
The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software.
Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems.
The book also features:
✓ Extensive sample code and tutorials using Python™ along with its technical libraries
✓ Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems
✓ Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity
✓ A wide variety of case studies from industry
✓ Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed
The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software.
年:
2017
出版社:
John Wiley & Sons
语言:
english
页:
417
ISBN 10:
1119092930
ISBN 13:
9781119092933
文件:
PDF, 3.98 MB
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IPFS:
CID , CID Blake2b
english, 2017
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