Developing analytic talent : becoming a data scientist / Vincent Granville.
- Indianapolis, Indiana : Wiley, 2014. , ©2014
1 online resource (338 p.)
- Data mining.
Data structures (Computer science)
- Electronic books.
- Learn the skills needed for the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. This guide discusses the essential skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common j
- Contents; Introduction; Chapter 1: What Is Data Science?; Real Versus Fake Data Science; The Data Scientist; Data Science Applications in 13 Real-World Scenarios; Data Science History, Pioneers, and Modern Trends; Summary; Chapter 2: Big Data Is Different; Two Big Data Issues; Examples of Big Data Techniques; What MapReduce Can't Do; Communication Issues; Data Science: The End of Statistics?; The Big Data Ecosystem; Summary; Chapter 3: Becoming a Data Scientist; Key Features of Data Scientists; Types of Data Scientists; Data Scientist Demographics; Training for Data Science
Data Scientist Career PathsSummary; Chapter 4: Data Science Craftsmanship, Part I; New Types of Metrics; Choosing Proper Analytics Tools; Visualization; Statistical Modeling Without Models; Three Classes of Metrics: Centrality, Volatility, Bumpiness; Statistical Clustering for Big Data; Correlation and R-Squared for Big Data; Computational Complexity; Structured Coefficient; Identifying the Number of Clusters; Internet Topology Mapping; Securing Communications: Data Encoding; Summary; Chapter 5: Data Science Craftsmanship, Part II; Data Dictionary; Hidden Decision Trees
Model-Free Confidence IntervalsRandom Numbers; Four Ways to Solve a Problem; Causation Versus Correlation; How Do You Detect Causes?; Life Cycle of Data Science Projects; Predictive Modeling Mistakes; Logistic-Related Regressions; Experimental Design; Analytics as a Service and APIs; Miscellaneous Topics; New Synthetic Variance for Hadoop and Big Data; Summary; Chapter 6: Data Science Application Case Studies; Stock Market; Encryption; Fraud Detection; Digital Analytics; Miscellaneous; Summary; Chapter 7: Launching Your New Data Science Career; Job Interview Questions
Testing Your Own Visual and Analytic ThinkingFrom Statistician to Data Scientist; Taxonomy of a Data Scientist; 400 Data Scientist Job Titles; Salary Surveys; Summary; Chapter 8: Data Science Resources; Professional Resources; Career-Building Resources; Summary; Index
- Includes index.
Description based on print version record.
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