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I have used the same post that for First edition but the link is updated to 2022-12-06.

Look at the contents: https://www.routledge.com/Mathematica-Beyond-Mathematics-The-Wolfram-Language-in-the-Real-World/Sanchez-Leon/p/book/9781032004839

Preface IX

1. Getting Started 1 1.1 Mathematica, an Integrated Technical Computing System 1 1.2 First Steps 3 1.3 Editing Notebooks 12 1.4 Basic Ideas 19 1.5 From Graphics to Machine Learning 29 1.6 Additional Resources and Supplementary Materials 40

2. Programming: The Beauty and Power of the Wolfram Language 41 2.1 Mathematica’s Programming Language: The Wolfram Language 41 2.2 Lists Operations 46 2.3 Association and Dataset 50 2.4 Matrix Operations 52 2.5 Set, SetDelayed, and Dynamic Variables 55 2.6 Functional vs. Procedural Programming 57 2.7 Apply, Map, and Other Related Functions 60 2.8 Iterative Functions 63 2.9 Pure Functions 64 2.10 Global and Local Variables 70 2.11 Conditional Expressions and Conditions 72 2.12 Accuracy and Precision 79 2.13 Choosing the Method of Computation 82 2.14 Optimizing the Computation Time 84 2.15 Cloud Deployment 86 2.16 Package Development 87 2.17 Additional Resources 90

3. Interactive Applications, Image Processing, and More 91 3.1 The Manipulate Function 91 3.2 Creating Demonstrations 97 3.3 Image Processing 100 3.4 Image Manipulation 105 3.5 Graphs and Networks 110 3.6 Application: Finding the Period of a Pendulum 113 3.7 Additional Resources 116

4. Accessing Scientific and Technical Information 117 4.1 The Wolfram Data Framework: Introducing Entities 117 4.2 Computable Data Functions 123 4.3 The Wolfram Data Repository 127 4.4 Weather Data in Real Time 129 4.5 Chemical and Physical Properties of Elements and Compounds 132 4.6 Life Sciences and Medicine 136 4.7 Earth Sciences and Geographic Data 142 4.8 Additional Resources 151

5. Data Analysis and Manipulation 153 5.1 Importing/Exporting 153 5.2 Statistical Analysis 162 5.3 Probability Distributions 169 5.4 Exploratory Data Analysis 181 5.5 Bootstrapping and Confidence Estimates 189 5.6 Curve Fitting 194 5.7 Time Series Analysis 203 5.8 Spatial Statistics 204 5.9 Additional Resources 208

6. Machine Learning and Neural Networks 207 6.1 What is Machine Learning 207 6.2 Classification 212 6.3 Prediction 221 6.4 Working with Neural Networks 225 6.5 Additional Resources 230

7. Calculating π and Other Mathematical Tales 231 7.1 The Origins of π 231 7.2 Archimedes’ Approximation 232 7.3 π with More Than One Billion Decimals 235 7.4 Buffon’s Method 240 7.5 Application: Are the Decimal Digits of π Random? 242 7.6 The Strange Connection 246 7.7 The Riemann Hypothesis 248 7.8 Looking for the Magic Prime Formula 252 7.9 Additional Resources 254

8. Looking at the Sky 255 8.1 A Short Astronomical Walk 255 8.2 Solar Analemma 259 8.3 Stargazing 260 8.4 Application: Determining the Color of the Stars 279 8.5 The Measurement of Distances Across the Universe 283 8.6 Application: Binary Systems and the Search for Exoplanets 287 8.7 Light Curves 291 8.8 Additional Resources 300

9. Nuclei and Radiations 301 9.1 Nuclear and Particle Physics 301 9.2 What are Isotopes? 302 9.3 Decay Constants, Decay Periods and Half-Lives 304 9.4 Decay Chains 308 9.5 Application: Modeling the Evolution of a Chain of Isotopes Over Time 313 9.6 Application: Dating the History of Humankind 316 9.7 Application: Calculating Binding Energies 321 9.8 Radiation Attenuation 328 9.9 Additional Resources 330

10. Modeling: Applications in Biokinetics, Epidemiology and More 331 10.1 Compartmental Modeling 331 10.2 Epidemiological Models 342 10.3 Physiological Modeling 346 10.4 Fitting a Model 351 10.5 Optimal Experimental Designs (OED) 355 10.6 BIOKMOD: The New Iodine OIR Model (ICRP 137) 360 10.7 Additional Modeling Examples 364 10.8 Modeling Using PDEs 366 10.9 System Modeler 369 10.10 Additional Resources 370

11. Economic, Financial and Optimization Applications 371 11.1 Accessing Economic Information 371 11.2 Financial Information 374 11.3 Financial Functions 381 11.4 Optimization 392 11.5 The Shortest Path Problem 403 11.6 Optimum Flows 407 11.7 Blockchains 410 11.8 Additional Resources 412

12. Faster, Further 415 12.1 Parallel Computing 413 12.2 Parallel Programming 414 12.3 The Mandelbrot Set 422 12.4 Comparing Organisms Genetically 427 12.5 Software Development with Wolfram Workbench 431 12.6 Compute Unified Device Architecture (CUDA) 434 12.7 Connecting with Other Programs and Devices 434 12.8 Additional Resources 435

Mathematics is the science of measurement and thus none of the topics mentioned in the Abstract are "beyond mathematics".