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 Table of Contents About the Author About the Technical Reviewer Acknowledgments Chapter 1: Swift Primer A Swift Overview Performance Safety Correctness Hardware Acceleration Swift Package Manager Conclusion Working with Swift Data Formats The Code Project The Decodable Protocol The KeyPath Type Higher-Order Functions Chapter Summary Chapter 2: Introduction to Probability and Random Variables Probability Sample Spaces Events The General Addition Rule Conditional Probabilities Independence Bayes’ Theorem Random Variables Discrete vs. Continuous Random Variables Chapter Summary Chapter 3: Distributions What Is a Distribution? Discrete Distributions Bernoulli Distribution and Trials Geometric Distribution Binomial Distribution Distributions Application Continuous Distributions Differences from Discrete Distributions Exponential Distribution Normal Distribution Expected Value Variance and Standard Deviation Chapter Summary Chapter 4: Predicting House Sale Prices with Linear Regression Linear Regression Splines Regression Techniques Loss Function Finding an Optimal Solution Implementing Simple Linear Regression Multiple Linear Regression Deriving Linear Regression with Vectors Implementing Multiple Linear Regression Predicting House Sale Prices Chapter Summary Chapter 5: Hypothesis Testing What Is Hypothesis Testing? Formulating Hypotheses The Null Hypothesis The Alternative Hypothesis Tails Distribution of Sample Means The Central Limit Theorem Testing the Hypothesis Determining Confidence Levels Determining Alpha Values Performing the Test Determining the P-value Standardization Computing a Standard Score Computing Confidence Intervals A Word on Chi-Squared Tests Chapter Summary Chapter 6: Statistical Methods for Data Compression An Introduction to Compression Function Behaviors Lossless vs. Lossy Compression Huffman Coding Storing the Huffman Tree Implementing a Compression Algorithm The Compression Stage The Decompression Stage Chapter Summary Chapter 7: Statistical Methods in Recommender Systems Recommender Systems The Functions of Recommender Systems Approaching the Problem First Approach Second Approach Final Approach Similarity Measures Cosine Similarity Euclidean Squared Distance Expected Ratings Laplace Smoothing Rating Probabilities Implementing the Algorithm The Main Program Chapter Summary Chapter 8: Reflections The Swift Programming Language Probability Theory Distributions Regression Techniques Hypothesis Testing Statistical Methods for Data Compression Statistical Methods in Recommender Systems Professional Areas of Application Data Scientist Machine Learning Engineer Data Engineer Data Analyst Topics for Further Studies Numerical Linear Algebra Multivariate Statistics Supervised Machine Learning Index
