Data Science for Dummies, 2nd Edition

  • 5h 27m
  • Lillian Pierson
  • John Wiley & Sons (US)
  • 2017

Discover how data science can help you gain in-depth insight into your business - the easy way!

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect:

  • Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value
  • Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL
  • Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
  • Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate

It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

About the Author

Lillian Pierson, P.E. is a data scientist, professional environmental engineer, and leading data science consultant to global leaders in IT, major governmental and non-governmental entities, prestigious media corporations, and not-for-profit technology groups.

In this Book

  • Foreword
  • Introduction
  • Wrapping Your Head around Data Science
  • Exploring Data Engineering Pipelines and Infrastructure
  • Applying Data-Driven Insights to Business and Industry
  • 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
  • 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
  • Using Python for Data Science
  • Using Open Source R for Data Science
  • Using SQL in Data Science
  • Doing Data Science with Excel and Knime
  • 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
  • Ten Phenomenal Resources for Open Data
  • Ten Free Data Science Tools and Applications


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