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Computing with Data

An Introduction to the Data Industry

  • Guy Lebanon
  • Mohamed El-Geish

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Guy Lebanon, Mohamed El-Geish
    Pages 1-5
  3. Guy Lebanon, Mohamed El-Geish
    Pages 7-36
  4. Guy Lebanon, Mohamed El-Geish
    Pages 37-98
  5. Guy Lebanon, Mohamed El-Geish
    Pages 99-168
  6. Guy Lebanon, Mohamed El-Geish
    Pages 169-190
  7. Guy Lebanon, Mohamed El-Geish
    Pages 191-253
  8. Guy Lebanon, Mohamed El-Geish
    Pages 255-276
  9. Guy Lebanon, Mohamed El-Geish
    Pages 277-324
  10. Guy Lebanon, Mohamed El-Geish
    Pages 325-361
  11. Guy Lebanon, Mohamed El-Geish
    Pages 363-413
  12. Guy Lebanon, Mohamed El-Geish
    Pages 415-439
  13. Guy Lebanon, Mohamed El-Geish
    Pages 441-470
  14. Guy Lebanon, Mohamed El-Geish
    Pages 471-493
  15. Guy Lebanon, Mohamed El-Geish
    Pages 495-541
  16. Guy Lebanon, Mohamed El-Geish
    Pages 543-576

About this book

Introduction

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.

Keywords

Data science Learn R Data mining Big data Python for data scientists Java for data scientists

Authors and affiliations

  • Guy Lebanon
    • 1
  • Mohamed El-Geish
    • 2
  1. 1.AmazonMenlo ParkUSA
  2. 2.VoiceraSanta ClaraUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-98149-9
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-98148-2
  • Online ISBN 978-3-319-98149-9
  • Buy this book on publisher's site