Data science /
Pierson, Lillian
Data science / Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind - Second edition - xvi, 364 pages : illustrations ; 23 cm - For dummies .
Includes index.
Introduction -- Part 1: Getting Started with Data Science -- CHAPTER 1: Wrapping You 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 Method -- 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 Parts of Tens -- CHAPTER 22: Ten Phenomenal Resources for Open Data -- CHAPTER 23: Ten Free Data Science Tools and Applications
Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.
Pierson, L. (2017). Data science (2nd ed.). John Wiley and Sons, Inc..
9781119327639
Information retrieval
Data mining
Information technology
Databases
004 / P624 2017
Data science / Lillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind - Second edition - xvi, 364 pages : illustrations ; 23 cm - For dummies .
Includes index.
Introduction -- Part 1: Getting Started with Data Science -- CHAPTER 1: Wrapping You 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 Method -- 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 Parts of Tens -- CHAPTER 22: Ten Phenomenal Resources for Open Data -- CHAPTER 23: Ten Free Data Science Tools and Applications
Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.
Pierson, L. (2017). Data science (2nd ed.). John Wiley and Sons, Inc..
9781119327639
Information retrieval
Data mining
Information technology
Databases
004 / P624 2017