Statistical analysis with Swift : data sets, statistical models, and predictions on Apple platforms
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Bibliographic Information
Statistical analysis with Swift : data sets, statistical models, and predictions on Apple platforms
Apress, c2022
- : pbk
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Includes index
Description and Table of Contents
Description
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
Chapter 1: Swift Primer
* Introduction to Swift and its pros when working with large data sets
* Provided data sets and how to load them using the Decodable protocol
* Higher-Order Functions (map, filter, reduce, apply)
Chapter 2: Introduction to Probability and Random Variables
* What is a random variable?
* Sample spaces
* Laws and axioms of probability
* Variable Independence
* Conditional probability
Chapter 3: Distributions and Random Numbers
* Mass and density functions
* Discrete distributions
* Discrete uniform distribution
* Bernoulli trials
* Binomial distribution
* Poisson distribution
* Continuous distributions
* Continuous uniform distribution
* Exponential distribution
* Normal distribution
* Implement a random number generator that samples from a given distribution
Chapter 4: Predicting House Sale Prices with Linear Regression
* Central tendency measures
* Variance measures
* Association measures
* Stratification of data
* Linear regression
Chapter 5: Hypothesis Testing
* T Testing
* Null and Alternative Hypotheses
* P-value
* Determining sample sizes
Chapter 6: Data Compression Using Statistical Methods
* Measurement scales
* Calculate the distribution of example data
* Compute a Huffman Tree
* Encode the original data in a smaller package
* Decode the compressed data
Chapter 7: Movie Recommendations Using Clustering
* Data transformation
* Similarity measurements
* Simple movie recommendation system
Chapter 8: Bringing It All Together
* Applying to new, real-world projects
* Building your data intuition
by "Nielsen BookData"
