| Description |
1 online resource (XVII, 576 pages) : 104 illustrations |
| Contents |
1. Introduction : how to use this book? -- 2. Essential knowledge : hardware -- 3. Essential knowledge : operating systems -- 4. Learning C++ -- 5. Learning Java -- 6 Learning Python and a few more things -- 7. Learning R -- 8. Visualizing data in Python and R -- 9. Processing data in Python and R -- 10. Essential knowledge : parallel programming -- 11. Essential knowledge : testing -- 12. A few more things about programming -- 13. Essential knowledge : data stores -- 14. Thoughts on system design for big data -- 15. Thoughts on software craftsmanship |
| Summary |
This book introduces basic computing skills designed for industry professionals without a strong computer science background. Written in an easily accessible manner, it serves as a self-study guide to survey data science and data engineering for those who aspire to start a computing career, or expand on their current roles, in areas such as applied statistics, big data, machine learning, data mining, and informatics. The authors draw from their combined experience working at software and social network companies, on big data products at several major online retailers, as well as their experience building big data systems for an AI startup. Spanning from the basic inner workings of a computer to advanced data manipulation techniques, this book opens doors for readers to quickly explore and enhance their computing knowledge. Computing with Data comprises a wide range of computational topics essential for data scientists, analysts, and engineers, providing them with the necessary tools to be successful in any role that involves computing with data. The introduction is self-contained, and chapters progress from basic hardware concepts to operating systems, programming languages, graphing and processing data, testing and programming tools, big data frameworks, and cloud computing. The book is fashioned with several audiences in mind. Readers without a strong educational background in CS--or those who need a refresher--will find the chapters on hardware, operating systems, and programming languages particularly useful. Readers with a strong educational background in CS, but without significant industry background, will find the following chapters especially beneficial: learning R, testing, programming, visualizing and processing data in Python and R, system design for big data, data stores, and software craftsmanship |
| Bibliography |
Includes bibliographical references |
| In |
Springer eBooks |
| Subject |
Artificial intelligence.
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Data mining.
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Computer science.
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Statistics.
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Engineering economy.
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Artificial Intelligence
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Data Mining
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Electronic Data Processing
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artificial intelligence.
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statistics.
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COMPUTERS -- Computer Literacy.
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COMPUTERS -- Computer Science.
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COMPUTERS -- Data Processing.
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COMPUTERS -- Hardware -- General.
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COMPUTERS -- Information Technology.
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COMPUTERS -- Machine Theory.
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COMPUTERS -- Reference.
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Artificial intelligence
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Computer science
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Data mining
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Engineering economy
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Statistics
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| Form |
Electronic book
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| Author |
El-Geish, Mohamed, author
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| ISBN |
9783319981499 |
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3319981498 |
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