Erschienen: 31.10.2021 Abbildung von Andersson | Statistical Analysis with Swift | 1. Auflage | 2021 | beck-shop.de

Andersson

Statistical Analysis with Swift

Data Sets, Statistical Models, and Predictions on Apple Platforms

lieferbar ca. 10 Tage als Sonderdruck ohne Rückgaberecht

ca. 40,65 €

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auch verfügbar als eBook (PDF) für 39.99 €

Fachbuch

Buch. Softcover

1st ed.. 2021

xiii, 214 S. 28 s/w-Abbildungen, Bibliographien.

In englischer Sprache

Apress. ISBN 978-1-4842-7764-5

Format (B x L): 15,5 x 23,5 cm

Gewicht: 355 g

Produktbeschreibung

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 random number generators in calculations. 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 data provided is authentic and taken 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

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