Data collection, processing, analysis, and more
About This Book
- Your entry ticket to the world of data science with the stability and power of Java
- Explore, analyse, and visualize your data effectively using easy-to-follow examples
- A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks.
Who This Book Is For
This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you!
What You Will Learn
- Understand the key concepts of data science
- Explore the data science ecosystem available in Java
- Work with the Java APIs and techniques used to perform efficient data analysis
- Find out how to approach different machine learning problems with Java
- Process unstructured information such as natural language text or images, and create your own search
- Learn how to build deep neural networks with DeepLearning4j
- Build data science applications that scale and process large amounts of data
- Deploy data science models to production and evaluate their performance
In Detail
Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics - from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings.
By the end of this course, you will be up and running with various facets of data science using Java, in no time at all.
This course contains premium content from two of our recently published popular titles:
- Java for Data Science
- Mastering Java for Data Science
Style and approach
This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.
Richard M. Reese has worked in both industry and academics. For 17 years, he worked in the telephone and aerospace industries, serving in several capacities, including research and development, software development, supervision, and training. He currently teaches at Tarleton State University, where he has the opportunity to apply his years of industry experience to enhance his teaching. Richard has written several Java books and a C Pointer book. He uses a concise and easy-to-follow approach to topics at hand. His Java books have addressed EJB 3.1, updates to Java 7 and 8, certification, jMonkeyEngine, natural language processing, functional programming, and networks.
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 research 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 previously worked as a software engineer developing software for county- and district-level government offices throughout Texas. 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.
Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 7 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 Searchmetrics, 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, but he also enjoys working on a broad set of problems, which is why he often participates in data science competitions on platforms such as kaggle.
