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- Title: Data Science For Dummies, 2nd Edition
- Author: Lillian Pierson
- Length: 384 pages
- Edition: 2
- Language: English
- Publisher: For Dummies
- Publication Date: 2017-03-13
- ISBN-10: 1119327636
- ISBN-13: 9781119327639
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Your ticket to breaking into the field of data science!
Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an 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 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
Table of Contents
Part 1: Getting Started with Data Science
Chapter 1: Wrapping Your Head around Data Science
Chapter 2: Exploring Data Engineering Pipelines and Infrastructure
Chapter 3: Applying Data-Driven Insights to Business and Industry
Part 2: Using Data Science to Extract Meaning from Your Data
Chapter 4: Machine Learning: Learning from Data with Your Machine
Chapter 5: Math, Probability, and Statistical Modeling
Chapter 6: Using Clustering to Subdivide Data
Chapter 7: Modeling with Instances
Chapter 8: Building Models That Operate Internet-of-Things Devices
Part 3: Creating Data Visualizations That Clearly Communicate Meaning
Chapter 9: Following the Principles of Data Visualization Design
Chapter 10: Using D3.js for Data Visualization
Chapter 11: Web-Based Applications for Visualization Design
Chapter 12: Exploring Best Practices in Dashboard Design
Chapter 13: Making Maps from Spatial Data
Part 4: Computing for Data Science
Chapter 14: Using Python for Data Science
Chapter 15: Using Open Source R for Data Science
Chapter 16: Using SQL in Data Science
Chapter 17: Doing Data Science with Excel and Knime
Part 5: Applying Domain Expertise to Solve Real-World Problems Using Data Science
Chapter 18: Data Science in Journalism: Nailing Down the Five Ws (and an H)
Chapter 19: Delving into Environmental Data Science
Chapter 20: Data Science for Driving Growth in E-Commerce
Chapter 21: Using Data Science to Describe and Predict Criminal Activity
Part 6: The Part of Tens
Chapter 22: Ten Phenomenal Resources for Open Data
Chapter 23: Ten Free Data Science Tools and Applications
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