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Home Java: Data Science Made Easy - Paperback
Java: Data Science Made Easy
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Java: Data Science Made Easy - Paperback

Original price $134.28 - Original price $134.28
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Availability: In Stock
SKU 9781788475655
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by Richard M. Reese (Author), Jennifer L. Reese (Author), Alexey Grigorev (Author)

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Author Biography

Reese Jennifer L.

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling--especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.Reese Richard M.

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in JavaGrigorev Alexey

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.

Number of Pages: 734
Dimensions: 1.47 x 9.25 x 7.5 IN
Publication Date: July 12, 2017

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