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Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms 1st ed. [Minkštas viršelis]

  • Formatas: Paperback / softback, 214 pages, aukštis x plotis: 235x155 mm, weight: 355 g, 28 Illustrations, black and white; XIII, 214 p. 28 illus., 1 Paperback / softback
  • Išleidimo metai: 31-Oct-2021
  • Leidėjas: APress
  • ISBN-10: 1484277643
  • ISBN-13: 9781484277645
Kitos knygos pagal šią temą:
Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms 1st ed.Didesnis paveikslėlis
  • Formatas: Paperback / softback, 214 pages, aukštis x plotis: 235x155 mm, weight: 355 g, 28 Illustrations, black and white; XIII, 214 p. 28 illus., 1 Paperback / softback
  • Išleidimo metai: 31-Oct-2021
  • Leidėjas: APress
  • ISBN-10: 1484277643
  • ISBN-13: 9781484277645
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781484277645.html
Raktažodžiai:
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 
About the Author ix
About the Technical Reviewer xi
Acknowledgments xiii
Chapter 1 Swift Primer
1(18)
A Swift Overview
1(4)
Performance
2(1)
Safety
2(1)
Correctness
3(1)
Hardware Acceleration
4(1)
Swift Package Manager
4(1)
Conclusion
5(1)
Working with Swift
5(12)
Data Formats
5(2)
The Code Project
7(1)
The Decodable Protocol
7(3)
The KeyPath Type
10(4)
Higher-Order Functions
14(3)
Chapter Summary
17(2)
Chapter 2 Introduction to Probability and Random Variables
19(20)
Probability
19(15)
Sample Spaces
21(1)
Events
22(1)
The General Addition Rule
23(4)
Conditional Probabilities
27(4)
Independence
31(1)
Bayes' Theorem
31(3)
Random Variables
34(3)
Discrete vs. Continuous Random Variables
35(2)
Chapter Summary
37(2)
Chapter 3 Distributions
39(32)
What Is a Distribution?
39(2)
Discrete Distributions
41(14)
Bernoulli Distribution and Trials
41(2)
Geometric Distribution
43(6)
Binomial Distribution
49(4)
Distributions Application
53(2)
Continuous Distributions
55(14)
Differences from Discrete Distributions
55(4)
Exponential Distribution
59(3)
Normal Distribution
62(5)
Expected Value
67(1)
Variance and Standard Deviation
68(1)
Chapter Summary
69(2)
Chapter 4 Predicting House Sale Prices with Linear Regression
71(38)
Linear Regression
71(36)
Splines
73(2)
Regression Techniques
75(1)
Loss Function
76(3)
Finding an Optimal Solution
79(2)
Implementing Simple Linear Regression
81(5)
Multiple Linear Regression
86(1)
Deriving Linear Regression with Vectors
87(4)
Implementing Multiple Linear Regression
91(16)
Predicting House Sale Prices
107(1)
Chapter Summary
107(2)
Chapter 5 Hypothesis Testing
109(26)
What Is Hypothesis Testing?
109(1)
Formulating Hypotheses
110(4)
The Null Hypothesis
111(1)
The Alternative Hypothesis
111(1)
Tails
112(2)
Distribution of Sample Means
114(9)
The Central Limit Theorem
117(6)
Testing the Hypothesis
123(6)
Determining Confidence Levels
123(1)
Determining Alpha Values
124(1)
Performing the Test
124(2)
Determining the P-value
126(3)
Standardization
129(5)
Computing a Standard Score
130(2)
Computing Confidence Intervals
132(2)
A Word on Chi-Squared Tests
134(1)
Chapter Summary
134(1)
Chapter 6 Statistical Methods for Data Compression
135(32)
An Introduction to Compression
135(6)
Function Behaviors
136(3)
Lossless vs. Lossy Compression
139(2)
Huffman Coding
141(4)
Storing the Huffman Tree
144(1)
Implementing a Compression Algorithm
145(20)
The Compression Stage
145(13)
The Decompression Stage
158(7)
Chapter Summary
165(2)
Chapter 7 Statistical Methods in Recommender Systems
167(32)
Recommender Systems
167(2)
The Functions of Recommender Systems
168(1)
Approaching the Problem
169(4)
First Approach
169(1)
Second Approach
170(1)
Final Approach
171(2)
Similarity Measures
173(5)
Cosine Similarity
173(3)
Euclidean Squared Distance
176(2)
Expected Ratings
178(7)
Laplace Smoothing
179(4)
Rating Probabilities
183(2)
Implementing the Algorithm
185(13)
The Main Program
186(12)
Chapter Summary
198(1)
Chapter 8 Reflections
199(12)
The Swift Programming Language
199(2)
Probability Theory
201(1)
Distributions
202(1)
Regression Techniques
203(1)
Hypothesis Testing
204(1)
Statistical Methods for Data Compression
205(2)
Statistical Methods in Recommender Systems
207(1)
Professional Areas of Application
208(1)
Data Scientist
208(1)
Machine Learning Engineer
208(1)
Data Engineer
208(1)
Data Analyst
209(1)
Topics for Further Studies
209(2)
Numerical Linear Algebra
209(1)
Multivariate Statistics
210(1)
Supervised Machine Learning
210(1)
Index 211
Jimmy M Andersson is a software engineer in the automotive industry, specializing in acquiring and visualizing real-time data collected from cars. He is also a graduate student at Chalmers University of Technology, currently working towards a master's degree in data science and artificial intelligence. Outside of work and studies, Jimmy writes software development articles focusing on the Swift programming language. He also develops the StatKit library - a collection of statistical analysis tools for Swift developers. StatKit is open-source and available for anyone who wants to incorporate statistical methods into their programs.