Developing Analytic Talent: Becoming a Data Scientist
Om denne e-boken
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.
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Om forfatteren
Vincent Granville, Ph.D. is a data scientist with 15 years of big data, predictive modeling, and business analytics experience. He is the co-founder of Data Science Central, which includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology tools and trends, and industry job opportunities.
