.Net Data Science

Key FeaturesThis easy-to-follow guide will help you enter the field of Data Science with C#Learn to use various .NET APIs and perform exploratory data analysis that starts with a few data journalism questions and ends with intuitive visualizationsExplore the power of C#'s cool Data Science libraries and learn to perform complex computations using this hands-on guide!Book DescriptionData Science is a new field that is still being defined. It's existed largely in academia and "start-ups," yet stands at the core of the world's most important, fun, and useful software. Because it was traditionally done in an elite academic setting, proprietary software was not included. With Microsoft taking a much more different approach with licensing, Data Science can easily be done with Microsoft tools and is now readily available to C# developers.This book will re-introduce you to the important parts of statistics and probabilities. From there, we move on to how to apply these principles using C# and C# based libraries. This includes using purely Microsoft technologies such as SSRS, SSIS, PowerShell, SSAS, as well as integrating various .NET libraries available for Data Science.Once you have realized the potential of various libraries, we'll dive deeper in to implementing concepts of regression, inference, correlation, and causation to devise empirical evidence from your data sets and visualize it. Towards the end of the book, you will learn to implement supervised, unsupervised, and deep learning algorithms and techniques that can be applied to a wide range of real-world problems.What you will learnBe reintroduced to the world of .NET statistics and calculus libraries and frameworksOvercome the pitfalls of modeling your data for simple to complex analysisUse the tools to utilize the statistical models for designing custom Machine Learning applicationsKnow how to deal with high volumes of data and scale your ASP.NET application to match the performance requirements of your Data Science applicationIntegrate various tools such as ATOM (ASP.NET MVC), Data Science libraries such as Accord.NET, Python's Natural Language Toolkit, and SWIRL to achieve precise data manipulations and intuitive visualizationsImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and deep learning

Author: William Ryan 'Bill'

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