000			03531nam a22003017a 4500
005			20241104144032.0
008			220922b ph ||||| |||| 00| 0 eng d
020			_a9781119327639 _qpaperback
040			_aPH-MaPCU
082	0	0	_a004 _bP624 2017
100	1		_aPierson, Lillian _eauthor
245			_aData science / _cLillian Pierson ; foreword by Jake Porway, founder and executive director of DataKind
250			_aSecond edition
264			_aHoboken, New Jersey : _bJohn Wiley and Sons, Inc., _c2017.
300			_axvi, 364 pages : _billustrations ; _c23 cm
490	1		_aFor dummies
504			_aIncludes index.
505	0		_aIntroduction -- 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
520			_aBegins 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.
524			_aPierson, L. (2017). Data science (2nd ed.). John Wiley and Sons, Inc..
650		0	_aInformation retrieval
650		0	_aData mining
650		0	_aInformation technology
650		0	_aDatabases
999			_c4720 _d4720