Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your p...

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for ...

Big data has more disruptive potential than any information technology developed in the past 40 years. As author Jeffrey Needham points out in this revealing book, big data can provide unprecedented visibility into the operational efficiency of enterprises and agencies.Disruptive Possibilities provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conve...

R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics b...

This book sheds light on the principles behind the relational model, which is fundamental to all database-backed applications--and, consequently, most of the work that goes on in the computing world today. Database in Depth: The Relational Model for Practitioners goes beyond the hype and gets to the heart of how relational databases actually work.Ideal for experienced database developers and designers, this concise guide gives you a clear view of the technology--a view tha...

Harnessing Hibernate is an ideal introduction to the popular framework that lets Java developers work with information from a relational database easily and efficiently. Databases are a very different world than Java objects, and they often involve people with different skills and specializations. With Hibernate, bridging these two worlds is significantly easier, and with this book, you can get up to speed with Hibernate quickly.Rather than present you with another referen...

As one of the most widely used desktop applications ever created, Excel is familiar to just about everyone with a computer and a keyboard. Yet most of us don't know the full extent of what Excel can do, mostly because of its recent growth in power, versatility, and complexity. The truth is that there are many ways Excel can help make your job easier-beyond calculating sums and averages in a standard spreadsheet.Analyzing Business Data with Excel shows you how to solve real...

In recent years, companies and government agencies have come to realize that the data they use represent a significant corporate resource, whose cost calls for management every bit as rigorous as the management of human resources, money, and capital equipment. With this realization has come recognition of the importance to integrate the data that has traditionally only been available from disparate sources.An important component of this integration is the management of the “metadat...

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visuali...

Leverage benefits of machine learning techniques using PythonAbout This BookImprove and optimise machine learning systems using effective strategies.Develop a strategy to deal with a large amount of data.Use of Python code for implementing a range of machine learning algorithms and techniques.Who This Book Is ForThis title is for data scientist and researchers who are already into the field of data scienc...

Data collection, processing, analysis, and moreAbout This BookYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesA 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 ForThis course is meant for...

Learn how to integrate full-stack open source big data architecture and to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer.Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses.Big Data SMACK...