Java Data Science Cookbook

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Java Data Science Cookbook

Author : Rushdi Shams
Publisher : Packt Publishing Ltd
ISBN 13 : 1787127656
Total Pages : 366 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Java Data Science Cookbook by : Rushdi Shams

Download or read book Java Data Science Cookbook written by Rushdi Shams and published by Packt Publishing Ltd. This book was released on 2017-03-28 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recipes to help you overcome your data science hurdles using Java About This Book This book provides modern recipes in small steps to help an apprentice cook become a master chef in data science Use these recipes to obtain, clean, analyze, and learn from your data Learn how to get your data science applications to production and enterprise environments effortlessly Who This Book Is For This book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro. What You Will Learn Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge. retrieve information from large amount of data in text format. Familiarize yourself with cutting-edge techniques to store and search large volumes of data and retrieve information from large amounts of data in text format Develop basic skills to apply big data and deep learning technologies on large volumes of data Evolve your data visualization skills and gain valuable insights from your data Get to know a step-by-step formula to develop an industry-standard, large-scale, real-life data product Gain the skills to visualize data and interact with users through data insights In Detail If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more—things that will come in handy at work. Style and approach This book contains short yet very effective recipes to solve most common problems. Some recipes cater to very specific, rare pain points. The recipes cover different data sets and work very closely to real production environments

Java for Data Science

Author : Richard M. Reese
Publisher : Packt Publishing Ltd
ISBN 13 : 1785281240
Total Pages : 376 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Java for Data Science by : Richard M. Reese

Download or read book Java for Data Science written by Richard M. Reese and published by Packt Publishing Ltd. This book was released on 2017-01-10 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the techniques and Java tools supporting the growing field of data science 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 Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis 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 book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book 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. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major 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.

Data Science with Java

Author : Michael R. Brzustowicz, PhD
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491934069
Total Pages : 236 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Data Science with Java by : Michael R. Brzustowicz, PhD

Download or read book Data Science with Java written by Michael R. Brzustowicz, PhD and published by "O'Reilly Media, Inc.". This book was released on 2017-06-06 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms

Practical Data Science Cookbook

Author : Prabhanjan Tattar
Publisher : Packt Publishing Ltd
ISBN 13 : 178712326X
Total Pages : 428 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Practical Data Science Cookbook by : Prabhanjan Tattar

Download or read book Practical Data Science Cookbook written by Prabhanjan Tattar and published by Packt Publishing Ltd. This book was released on 2017-06-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Mastering Java for Data Science

Author : Alexey Grigorev
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887394
Total Pages : 355 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Mastering Java for Data Science by : Alexey Grigorev

Download or read book Mastering Java for Data Science written by Alexey Grigorev and published by Packt Publishing Ltd. This book was released on 2017-04-27 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.

Java Cookbook

Author : Ian F. Darwin
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492072532
Total Pages : 620 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Java Cookbook by : Ian F. Darwin

Download or read book Java Cookbook written by Ian F. Darwin and published by "O'Reilly Media, Inc.". This book was released on 2020-03-17 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Java continues to grow and evolve, and this cookbook continues to evolve in tandem. With this guide, you’ll get up to speed right away with hundreds of hands-on recipes across a broad range of Java topics. You’ll learn useful techniques for everything from string handling and functional programming to network communication. Each recipe includes self-contained code solutions that you can freely use, along with a discussion of how and why they work. If you’re familiar with Java basics, this cookbook will bolster your knowledge of the language and its many recent changes, including how to apply them in your day-to-day development. This updated edition covers changes through Java 12 and parts of 13 and 14. Recipes include: Methods for compiling, running, and debugging Packaging Java classes and building applications Manipulating, comparing, and rearranging text Regular expressions for string and pattern matching Handling numbers, dates, and times Structuring data with collections, arrays, and other types Object-oriented and functional programming techniques Input/output, directory, and filesystem operations Network programming on both client and server Processing JSON for data interchange Multithreading and concurrency Using Java in big data applications Interfacing Java with other languages

Java Deep Learning Cookbook

Author : Rahul Raj
Publisher : Packt Publishing Ltd
ISBN 13 : 1788999479
Total Pages : 294 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Java Deep Learning Cookbook by : Rahul Raj

Download or read book Java Deep Learning Cookbook written by Rahul Raj and published by Packt Publishing Ltd. This book was released on 2019-11-08 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch Key FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook Description Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java. What you will learnPerform data normalization and wrangling using DL4JBuild deep neural networks using DL4JImplement CNNs to solve image classification problemsTrain autoencoders to solve anomaly detection problems using DL4JPerform benchmarking and optimization to improve your model's performanceImplement reinforcement learning for real-world use cases using RL4JLeverage the capabilities of DL4J in distributed systemsWho this book is for If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.

Java: Data Science Made Easy

Author : Richard M. Reese
Publisher : Packt Publishing Ltd
ISBN 13 : 1788479181
Total Pages : 715 pages
Book Rating : 4.7/5 (884 download)

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Book Synopsis Java: Data Science Made Easy by : Richard M. Reese

Download or read book Java: Data Science Made Easy written by Richard M. Reese and published by Packt Publishing Ltd. This book was released on 2017-07-07 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning in Java

Author : Bostjan Kaluza
Publisher :
ISBN 13 : 9781784396589
Total Pages : 258 pages
Book Rating : 4.3/5 (965 download)

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Book Synopsis Machine Learning in Java by : Bostjan Kaluza

Download or read book Machine Learning in Java written by Bostjan Kaluza and published by . This book was released on 2016-04-29 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design, build, and deploy your own machine learning applications by leveraging key Java machine learning librariesAbout This Book- Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries- Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications- Packed with practical advice and tips to help you get to grips with applied machine learningWho This Book Is ForIf you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary.What You Will Learn- Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches- Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve- Learn how to implement classification, regression, and clustering- Develop a sustainable strategy for customer retention by predicting likely churn candidates- Build a scalable recommendation engine with Apache Mahout- Apply machine learning to fraud, anomaly, and outlier detection- Experiment with deep learning concepts, algorithms, and the toolbox for deep learning- Write your own activity recognition model for eHealth applications using mobile sensorsIn DetailAs the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.Style and approachThis is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with Java libraries using clear and practical examples. You will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

Data Science with Java

Author : Michael R. Brzustowicz, PhD
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491934085
Total Pages : 235 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Data Science with Java by : Michael R. Brzustowicz, PhD

Download or read book Data Science with Java written by Michael R. Brzustowicz, PhD and published by "O'Reilly Media, Inc.". This book was released on 2017-06-06 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms

Jakarta EE Recipes

Author : Josh Juneau
Publisher : Apress
ISBN 13 : 1484255879
Total Pages : 871 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Jakarta EE Recipes by : Josh Juneau

Download or read book Jakarta EE Recipes written by Josh Juneau and published by Apress. This book was released on 2020-03-27 with total page 871 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a problem-solution approach to programming enterprise Java applications and microservices for cloud-based solutions, enterprise database applications, and even small business web applications. This book provides effective and proven code snippets that you can immediately use to accomplish just about any task that you may encounter. You can feel confident using the reliable solutions that are demonstrated in this book in your personal or corporate environment. Java EE was made open source under the Eclipse Foundation, and Jakarta EE is the new name for what used to be termed the Java Enterprise Edition Platform. This book helps you rejuvenate your Java expertise and put the platform’s latest capabilities to use in quickly developing robust applications. If you are new to Jakarta EE, this book will help you learn features of the platform, and benefit from one of the most widely used and powerful technologies available for application development today. Examples in Jakarta EE Recipes highlight Jakarta EE’s capabilities, helping you to build streamlined and reliable applications using the latest in Java technologies. The book takes a problem-solution approach in which each section introduces a common programming problem, showing you how to best solve that problem using the latest features in Jakarta EE. Solutions are presented in the form of working code examples that you can download and use immediately in your own projects. Clear descriptions are given so you can understand and learn to build further on the solutions that are provided. This is the ideal book for the code-focused programmer interested in keeping up with the future of enterprise development on the Java Platform. What You Will LearnDevelop enterprise Java applications using the now open source Jakarta EE platform Create great-looking user interfaces using Jakarta Server Faces and the Eclipse Krazo framework Build database applications using Jakarta Enterprise Beans and Jakarta RESTFul web services Automate testing through cohesive test suites built on Arquillian for Jakarta EE applications Deploy microservices applications in cloud environments using Docker Secure applications utilizing the Jakarta EE Security API and JSON Web Tokens Who This Book Is For Java developers interested in quickly finding effective and proven solutions without reading through a lengthy manual and scrubbing for techniques

Python Data Analysis Cookbook

Author : Ivan Idris
Publisher : Packt Publishing Ltd
ISBN 13 : 1785283855
Total Pages : 462 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Python Data Analysis Cookbook by : Ivan Idris

Download or read book Python Data Analysis Cookbook written by Ivan Idris and published by Packt Publishing Ltd. This book was released on 2016-07-22 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.

R Data Analysis Cookbook

Author : Kuntal Ganguly
Publisher : Packt Publishing Ltd
ISBN 13 : 1787125319
Total Pages : 549 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis R Data Analysis Cookbook by : Kuntal Ganguly

Download or read book R Data Analysis Cookbook written by Kuntal Ganguly and published by Packt Publishing Ltd. This book was released on 2017-09-20 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 80 recipes to help you breeze through your data analysis projects using R About This Book Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Who This Book Is For This book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed. What You Will Learn Acquire, format and visualize your data using R Using R to perform an Exploratory data analysis Introduction to machine learning algorithms such as classification and regression Get started with social network analysis Generate dynamic reporting with Shiny Get started with geospatial analysis Handling large data with R using Spark and MongoDB Build Recommendation system- Collaborative Filtering, Content based and Hybrid Learn real world dataset examples- Fraud Detection and Image Recognition In Detail Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios. Style and Approach Hands-on recipes to walk through data science challenges using R Your one-stop solution for common and not-so-common pain points while performing real-world problems to execute a series of tasks. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf

Python Data Science Handbook

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 743 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python Data Science Handbook by : Jake VanderPlas

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Data Crunching

Author : Greg Wilson
Publisher :
ISBN 13 :
Total Pages : 212 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Data Crunching by : Greg Wilson

Download or read book Data Crunching written by Greg Wilson and published by . This book was released on 2005 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every day, all around the world, programmers have to recycle legacy data, translate from one vendor's proprietary format into another's, check that configuration files are internally consistent, and search through web logs to see how many people have downloaded the latest release of their product. This kind of "data crunching," may not be glamorous, but knowing how to do it efficiently is essential to being a good programmer. This book describes the most useful data crunching techniques, explains when you should use them, and shows how they will make your life easier. Along the way, it will introduce you to some handy, but under-used, features of Java, Python, and other languages. It will also show you how to test data crunching programs, and how data crunching fits into the larger software development picture.

Apache Spark for Data Science Cookbook

Author : Padma Priya Chitturi
Publisher : Packt Publishing Ltd
ISBN 13 : 1785288806
Total Pages : 388 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Apache Spark for Data Science Cookbook by : Padma Priya Chitturi

Download or read book Apache Spark for Data Science Cookbook written by Padma Priya Chitturi and published by Packt Publishing Ltd. This book was released on 2016-12-22 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.

Java Cookbook

Author : Ian F. Darwin
Publisher : O'Reilly Media
ISBN 13 : 1492072559
Total Pages : 639 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Java Cookbook by : Ian F. Darwin

Download or read book Java Cookbook written by Ian F. Darwin and published by O'Reilly Media. This book was released on 2020-03-17 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Java continues to grow and evolve, and this cookbook continues to evolve in tandem. With this guide, you’ll get up to speed right away with hundreds of hands-on recipes across a broad range of Java topics. You’ll learn useful techniques for everything from string handling and functional programming to network communication. Each recipe includes self-contained code solutions that you can freely use, along with a discussion of how and why they work. If you’re familiar with Java basics, this cookbook will bolster your knowledge of the language and its many recent changes, including how to apply them in your day-to-day development. This updated edition covers changes through Java 12 and parts of 13 and 14. Recipes include: Methods for compiling, running, and debugging Packaging Java classes and building applications Manipulating, comparing, and rearranging text Regular expressions for string and pattern matching Handling numbers, dates, and times Structuring data with collections, arrays, and other types Object-oriented and functional programming techniques Input/output, directory, and filesystem operations Network programming on both client and server Processing JSON for data interchange Multithreading and concurrency Using Java in big data applications Interfacing Java with other languages