TY  - BOOK
AU  - Pierson,Lillian
AU  - Porway,Jake
TI  - Data science
T2  - --For dummies
SN  - 9781119327639
AV  - T 58.5 P6158 2017
PY  - 2017///]
CY  - Hoboken, NJ
PB  - John Wiley and Sons, Incorporated
KW  - Information retrieval
KW  - Data mining
KW  - Information technology
KW  - Databases
KW  - COMPUTERS
KW  - Data Processing
KW  - bisacsh
KW  - fast
N1  - Includes index; Getting Started With Data Science -- Wrapping Your Head around Data Science -- Exploring Data Engineering Pipelines and Infrastructure -- Applying Data-Driven Insights to Business and Industry -- Using Data Science to Extract Meaning from Your Data -- Machine Learning: Learning from Data with your Machine -- Math, Probability, and Statistical Modeling -- Using Clustering to Subdivide Data -- Modeling with Instances -- Building models that Operate Internet-of-Things Devices -- Creating Data Visualizations that Clearly Communicate Meaning -- Following the Principles of Data Visualization Design -- Using D3.js for Data Visualization -- Web-Based Applications for Visualization Design -- Exploring Best Practices in Dashboard Design -- Making Maps from Spatial Data -- Computing for Data Science -- Using Python for Data Science -- Using Open Source R for Data Science -- Using SQL in Data Science -- Doing Data Science with Excel and Knime -- Applying Domain Expertise to Solve Real-World Problems Using Data Science -- Data Science in Journalism: Nailing Down the Five Ws (and an H) -- Delving into Environmental Data Science -- Data Science for Driving Growth in E-Commerce -- Using Data Science to Describe and Predict Criminal Activity -- The Part of Tens --Ten Phenomenal Resources for Open Data -- Ten Free Data Science Tools and Applications
N2  - 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
ER  -