Jimmy Andersson - Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms

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First and foremost, I would like to thank my partner Lisette for keeping me on track while writing and making sure I did not overwork myself in all the excitement. Your support and encouragement mean the world to me.

I would also like to thank the team at Apress. You have made this a fantastic journey. Thank you, Aaron, for reaching out and suggesting that we write this book; I am very grateful for the opportunity. Thank you, Jessica, for ensuring a smooth writing process and making it an enjoyable experience from start to finish. Thank you, Vishwesh, for keeping me on my toes and ensuring that the topics were relevant and correct. All help you have provided has been invaluable.

Last but not least, I want to thank my friend Bastian, who has spent the past year tossing ideas about mathematics and programming back and forth with me. The knowledge and realizations from our talks have helped me a great deal when writing this.

Swiftis a general-purpose programming language built using a modern approach to safety, performance, and software design patterns.

Swift.org

Apple introduced the Swift programming language at Worldwide Developers Conference 2014, with the vision to replace other C-based languages such as C, C++, and Objective-C. Since then, Swift has grown a passionate community of developers by striving to strike the perfect balance between performance, safety, and ease of use.

Before we dive into statistical analysis, we need to ask ourselves a few questions about whether Swift is an appropriate technology choice. Other languages support these types of calculations, and many of them have excellent third-party libraries that further extend the support of their standard libraries. To convince ourselves that Swift is a well-suited tool for these tasks, let us look at some advantageous features of the language and its ecosystem.

One bottleneck in modern computing is that it often includes working with significant amounts of data. For example, Microsofts Malware Classification data set is almost half a terabyte in size, while Googles Landmark Recognition data consists of more than 1.2 million data points. Processing these amounts of data requires access to powerful hardware and demands that the programming language we use is efficient.

Performance has been an important buzzword in the marketing of Swift ever since 2014. The vision to compete with C and C++ sets a high bar for speed and efficiency, and many benchmarks show that it is doing a reasonably good job of keeping up. Compared to Python, which has pretty much become the gold standard for data scientists today, benchmarks suggest that a corresponding Swift program may yield considerable speedups. Since we know that there is much data out there to process, a high-performant language is always welcome. However, large amounts of complex data put new requirements on how well our tools allow us to manipulate it safely and correctly.

Safety is a guiding star in the development of Swift. In this context, safety means that the language actively tries to protect developers from writing code that results in undefined behavior. These safety features include such simple things as encouraging the use of immutable variables. However, they also contain far more sophisticated schemes and requirements.