Statistical Analysis with Swift (eBook)

Jimmy M Andersson is a software engineer in the automotive industry, specializing in acquiring and visualizing real-time data collected from cars. He is also a graduate student at Chalmers University of Technology, currently working towards a master's degree in data science and artificial intelligence. Outside of work and studies, Jimmy writes software development articles focusing on the Swift programming language. He also develops the StatKit library - a collection of statistical analysis tools for Swift developers. StatKit is open-source and available for anyone who wants to incorporate statistical methods into their programs.

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Work with large data sets, create statistical models, and make predictions with statistical methods using the Swift programming language. The variety of problems that can be solved using statistical methods range in fields from financial management to machine learning to quality control and much more.  Those who possess knowledge of statistical analysis become highly sought after candidates for companies worldwide.    Starting with an introduction to statistics and probability theory, you will learn core concepts to analyze your data's distribution. You'll get an introduction to random variables, how to work with them, and how to leverage their properties in computations. On top of the mathematics, you'll learn several essential features of the Swift language that significantly reduce friction when working with large data sets. These functionalities will prove especially useful when working with multivariate data, which applies to most information in today's complex world.    Once you know how to describe a data set, you will learn how to create models to make predictions about future events. All provided data is generated from real-world contexts so that you can develop an intuition for how to apply statistical methods with Swift to projects you're working on now.  You will:* Work with real-world data using the Swift programming language  * Compute essential properties of data distributions to understand your customers, products, and processes  * Make predictions about future events and compute how robust those predictions are