Statistical analysis with Swift data sets, statistical models, and predictions on Apple platforms
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 mor...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress
[2022]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009635712306719 |
Tabla de Contenidos:
- Intro
- 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.
