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Learning Python Data Visualization
Learning Python Data Visualization

The best applications use data and present it in a meaningful, easy-to-understand way. Packed with sample code and tutorials, this book will walk you through installing common charts, graphics, and utility libraries for the Python programming language. Firstly you will discover how to install and reference libraries in Visual Studio or Eclipse. We will then go on to build simple graphics and charts that allow you to generate HTML5-ready SVG charts and graphs, along with testing and validating your data sources. We will also cover parsing data from the Web and offline sources, and building a Python charting application using dynamic data. Lastly, we will review other popular tools and frameworks used to create charts and import/export chart data. By the end of this book, you will be able to represent complex sets of data using Python. ...
Splunk Essentials
Splunk Essentials

Splunk is a powerful tool that is used extensively to search, monitor, and analyze any machine data. This book is designed to introduce you quickly to the benefits of using the Splunk Enterprise system. Understanding this technology will allow you to engage with your important data and ensure that it is collected, stored, managed, reported on, and utilized well to enable you to make better business decisions. By equipping you with this knowledge, you will be better prepared to tackle data issues in the fast-paced business world of today. You will learn about various vital topics such as data collection, managing apps, creating reports, and analyzing data using Splunk. You will also be equipped with skills to help you obtain a Twitter API key for use with the Twitter app for Splunk. ...
Data Science For Dummies
Data Science For Dummies

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. It's a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization. ...
Code-First Development with Entity Framework
Code-First Development with Entity Framework

Entity Framework Code-First enables developers to read and write data in a relational database system using C# or VB.NET. It is Microsoft's answer to demand for an ORM from .NET developers. This book will help you acquire the necessary skills to program your applications using Entity Framework. You will start with database configuration and learn how to write classes that define the database structure. You will see how LINQ can be used with Entity Framework to give you access to stored data. You will then learn how to use Entity Framework to persist information in a Relational Database Management System. You will also see how you can benefit from writing ORM-based .NET code. Finally, you will learn how Entity Framework can help you to solve database deployment problems using migrations. ...
Learning Apache Mahout
Learning Apache Mahout

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark. Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naïve Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end us ...
Big Data For Dummies
Big Data For Dummies

Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. ...
Data Mining For Dummies
Data Mining For Dummies

Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. ...
PostgreSQL for Data Architects
PostgreSQL for Data Architects

PostgreSQL is an incredibly flexible and dependable open source relational database. Harnessing its power will make your applications more reliable and extensible without increasing costs. Using PostgreSQL's advanced features will save you work and increase performance, once you've discovered how to set it up. PostgreSQL for Data Architects will teach you everything you need to learn in order to get a scalable and optimized PostgreSQL server up and running. The book starts with basic concepts like installing PostgreSQL from source and covers theoretical aspects such as concurrency and transaction management. After this, you'll learn how to set up replication, use load balancing to scale horizontally, and troubleshoot errors. Finally, you will get acquainted with useful tools available in the PostgreSQL ecosystem used for analyzing PostgreSQL logs, setting up load balancing, and recovery. ...
Data Science from Scratch
Data Science from Scratch

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. 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. ...
Advanced Analytics with Spark
Advanced Analytics with Spark

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques - classification, collaborative filtering, and anomaly detection among others - to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications. ...
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