In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Many exercises are presented to help reinforce the lessons learned, and answers to the exercises are given in a new appendix.Most of the remaining chapters are dedicated to the reference material, which describes in detail the standard features of Scheme included in the Revised$^5$ Report on Scheme and the ANSI/IEEE standard for Scheme.Numerous examples are presented throughout the introductory and reference portions of the text, and a unique set of extended example programs and applications, with additional exercises, are presented in the final chapter. Reinforcing the book's utility as a reference text are appendices that present the formal syntax of Scheme, a summary of standard forms and procedures, and a bibliography of Scheme resources.The Scheme Programming Language stands alone as an introduction to and essential reference for Scheme programmers. it is also useful as a supplementary text for any course that uses Scheme.The Scheme Programming Language is illustrated by artist Jean-Pierre Hébert, who writes Scheme programs to extend his ability to create sophisticated works of digital art.R. Kent Dybvig is Professor of Computer Science at Indiana University and principal developer of Chez Scheme.

    How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables

    It teaches Clojure from the basics to advanced topics using practical, real-world application examples. Blow through the theory and dive into practical matters like unit-testing and environment set-up, all the way through building a scalable web-application using domain-specific languages, Hadoop, HBase, and RabbitMQ. About the TechnologyClojure is a modern Lisp for the JVM, and it has the strengths you'd expect: first-class functions, macros, support for functional programming, and a Lisp-like, clean programming style. About this BookClojure in Action is a practical guide focused on applying Clojure to practical programming challenges. You'll start with a language tutorial written for readers who already know OOP. Then, you'll dive into the use cases where Clojure really shines: state management, safe concurrency and multicore programming, first-class code generation, and Java interop. In each chapter, you'll first explore the unique characteristics of a problem area and then discover how to tackle them using Clojure. Along the way, you'll explore practical matters like architecture, unit testing, and set-up as you build a scalable web application that includes custom DSLs, Hadoop, HBase, and RabbitMQ. What's InsideA fast-paced Clojure tutorial Creating web services with Clojure Scaling through messaging Creating DSLs with Clojure's macro system Test-driven development with Clojure Distributed programming with Clojure, and moreThis book assumes you're familiar with an OO language like Java, C#, or C++ but requires no background in Lisp or Clojure itself.================================== Table of ContentsPART 1 GETTING STARTED Introduction to Clojure A whirlwind tour Building blocks of Clojure Polymorphism with multimethods Clojure and Java interop State and the concurrent world Evolving Clojure through macros PART 2 GETTING REAL Test-driven development and more Data storage with Clojure Clojure and the web Scaling through messaging Data processing with Clojure More on functional programming Protocols, records, and type More macros and DSLs

    Topics covered include:Basic version control commands and conceptsIntroduction to Distributed Version Control Systems (DVCS)Advanced branching workflowsStrengths and weaknesses of DVCS vs. centralized toolsBest practicesHow distributed version control works under the hoodFeaturing these open source version control tools:Apache SubversionMercurialGitVeracity