Clojure Data Analysis Cookbook

Learn to perform efficient data analysis using Haskell

Pandas Data Analysis with Python Fundamentals LiveLessons provides analysts and aspiring data scientists with a practical introduction to Python and pandas, the analytics stack that enables you to move from spreadsheet programs such as Excel into automation of your data analysis workflows.

You’re a software developer with a basic understanding of Java and the Java Virtual Machine (JVM), but you want to write more productive code that is fast and less verbose. This learning path covers the fundamentals of Clojure, a dynamic, general­purpose programming language for the JVM. You’ll dive into such topics as Java interoperability, concurrency, interacting with data, and working with collections, along with best practices so you can be on your way to writing Clojure code that is simple, maintainable, and fast. Once you’ve added the skills in this learning path to your programming tool belt, you’ll be ready to move on to more advanced Clojure development challenges.

Production debugging is hard, and it’s getting harder. With architectures becoming more distributed and code more asynchronous, pinpointing and resolving errors in production is no child's game. This session will cover advanced techniques that Java, Scala and Clojure developers can use to debug live servers and resolve critical errors in production quickly.We'll explore five crucial techniques for distributed and reactive logging, and some of the pitfalls that make resolution much more difficult (and even lead to downtime). We'll then dive into more advanced techniques and powerful tools for capturing live state from a production JVM without deploying new code, restarting the application or attaching a debugger.

A comprehensive guide to working on statistical data with the R language.

Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms.

Put your data to work on the modern web

Understanding Big Data Using Hadoop and Spark