Data Science For Dummies
- 5h 44m
- Lillian Pierson
- John Wiley & Sons (US)
- 2015
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.
About the Author
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.
In this Book
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Foreword
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Introduction
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Wrapping Your Head around Data Science
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Exploring Data Engineering Pipelines and Infrastructure
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Applying Data Science to Business and Industry
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Introducing Probability and Statistics
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Clustering and Classification
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Clustering and Classification with Nearest Neighbor Algorithms
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Mathematical Modeling in Data Science
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Modeling Spatial Data with Statistics
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Following the Principles of Data Visualization Design
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Using D3.js for Data Visualization
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Web-Based Applications for Visualization Design
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Exploring Best Practices in Dashboard Design
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Making Maps from Spatial Data
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Using Python for Data Science
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Using Open Source R for Data Science
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Using SQL in Data Science
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Software Applications for Data Science
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Using Data Science in Journalism
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Delving into Environmental Data Science
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Data Science for Driving Growth in E-Commerce
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Using Data Science to Describe and Predict Criminal Activity
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Ten Phenomenal Resources for Open Data
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Ten (or So) Free Data Science Tools and Applications
