Data Science For Dummies
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 covering 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 aspects of the topic, including the sometimes intimidating field of big data and data science, it is not an instructional manual for hands-on implementation. Here’s what to expect in Data Science for Dummies:
- 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 and applications 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 get started harnessing its power so you can gain a competitive edge for your organization.
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
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.