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Statistical analysis with swift : data sets, statistical models, and predictions on apple platforms / Jimmy Andersson

By: Material type: TextTextPublication details: New York Springer 2022Description: 241pISBN:
  • 9781484284971
Subject(s): DDC classification:
  • 005.268 AND-J
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Item type Current library Collection Shelving location Call number Copy number Status Date due Barcode Item holds
Books Books BITS Pilani Hyderabad 003-007 General Stack (For lending) 005.268 AND-J (Browse shelf(Opens below)) INR 529.00 Available 47606
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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 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.
What You'll Learn
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

Who This Book Is For
Aspiring data scientists and machine learning engineers who want to learn about the statistical methods that support today's smart applications. Previous experience with Swift is required, and knowledge about linear algebra is helpful.

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