Print this page Share

Data Science For Dummies

ISBN: 978-1-118-84155-6
408 pages
March 2015
Data Science For Dummies (1118841557) cover image


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 in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization.

  • Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis
  • Details different data visualization techniques that can be used to showcase and summarize your data
  • Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques
  • Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark

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.

See More

Table of Contents

Foreword xv

Introduction 1

Part I: Getting Started With Data Science 5

Chapter 1: Wrapping Your Head around Data Science 7

Chapter 2: Exploring Data Engineering Pipelines and Infrastructure 17

Chapter 3: Applying Data Science to Business and Industry 33

Part II: Using Data Science to Extract Meaning from Your Data 47

Chapter 4: Introducing Probability and Statistics 49

Chapter 5: Clustering and Classification 73

Chapter 6: Clustering and Classification with Nearest Neighbor Algorithms 87

Chapter 7: Mathematical Modeling in Data Science 99

Chapter 8: Modeling Spatial Data with Statistics 113

Part III: Creating Data Visualizations that Clearly Communicate Meaning 129

Chapter 9: Following the Principles of Data Visualization Design 131

Chapter 10: Using D3.js for Data Visualization 157

Chapter 11: Web-Based Applications for Visualization Design 171

Chapter 12: Exploring Best Practices in Dashboard Design 189

Chapter 13: Making Maps from Spatial Data 195

Part IV: Computing for Data Science 215

Chapter 14: Using Python for Data Science 217

Chapter 15: Using Open Source R for Data Science 239

Chapter 16: Using SQL in Data Science 255

Chapter 17: Software Applications for Data Science 267

Part V: Applying Domain Expertise to Solve Real-World Problems Using Data Science 279

Chapter 18: Using Data Science in Journalism 281

Chapter 19: Delving into Environmental Data Science 299

Chapter 20: Data Science for Driving Growth in E-Commerce 311

Chapter 21: Using Data Science to Describe and Predict Criminal Activity 327

Part VI: The Part of Tens 337

Chapter 22: Ten Phenomenal Resources for Open Data 339

Chapter 23: Ten (or So) Free Data Science Tools and Applications 351

Index 365

See More

Author Information

Lillian Pierson, P.E. is an entrepreneurial data scientist and professional environmental engineer. She's the founder of Data-Mania, a start-up that focuses mainly on web analytics, data-driven growth services, data journalism, and data science training services. She also covers the topics of data science, analytics, and statistics for prominent organizations like IBM and UBM.

See More

Related Titles

Back to Top