Learn what it takes to succeed in the 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. Introduction Contents 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 Paths Summary 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 Intervals Random 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 Thinking From 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
