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Developing analytic talent : becoming a data scientist /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Indianapolis, IN :
Wiley,
2014.
©2014 |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Granville, Vincent |c (Ph. D.) | |
| 245 | 1 | 0 | |a Developing analytic talent : |b becoming a data scientist / |c Vincent Granville. |
| 260 | |a Indianapolis, IN : |b Wiley, |c 2014. | ||
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xxv, 310 pages) : |b illustrations (chiefly color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 520 | |a Learn what it takes to succeed in 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. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. | ||
| 588 | 0 | |a Description based on print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Data structures (Computer science) | |
| 650 | 0 | |a Database management |x Vocational guidance. | |
| 650 | 0 | |a Electronic data processing consultants |x Vocational guidance. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Bases de données |x Gestion |x Orientation professionnelle. | |
| 650 | 6 | |a Informaticiens-conseils |x Orientation professionnelle. | |
| 650 | 6 | |a Structures de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Electronic data processing consultants |x Vocational guidance |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Data structures (Computer science) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Granville, Vincent (Ph. D.). |t Developing analytic talent. |d Indianapolis, IN : John Wiley & Sons, Inc., [2014] |z 9781118810088 |w (DLC) 2013958300 |w (OCoLC)859179846 |
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