Beginning Mathematica and Wolfram for Data Science
Apress (Verlag)
978-1-4842-6593-2 (ISBN)
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You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages.
You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out.
What You Will Learn
Use Mathematica to explore data and describe the concepts using Wolfram language commands
Create datasets, work with data frames, and create tables
Import, export, analyze, and visualize data
Work with the Wolfram data repository
Build reports on the analysis
Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering
Who This Book Is For
Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.
Jalil Villalobos Alva is a Wolfram language programmer and Mathematica user. He graduated with a degree in engineering physics from the Universidad Iberoamericana in Mexico City. His research background comprises quantum physics, bionformatics, proteomics, and protein design. His academic interests cover the topics of quantum technology, bioinformatics, machine learning, stochastic processes, and space engineering. During his idle hours he likes to play soccer, swim, and listen to music.
1. Introduction to Mathematica.- 2. Data Manipulation.- 3. Working with Data and Datasets.- 4. Import and Export.- 5. Data Visualization.- 6. Statistical Data Analysis.- 7. Data Exploration.- 8. Machine Learning with the Wolfram Language.- 9. Neural Networks with the Wolfram Language.- 10. Neural Network Framework.
| Erscheinungsdatum | 25.02.2021 |
|---|---|
| Zusatzinfo | 54 Illustrations, color; 290 Illustrations, black and white; XXIII, 416 p. 344 illus., 54 illus. in color. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-4842-6593-9 / 1484265939 |
| ISBN-13 | 978-1-4842-6593-2 / 9781484265932 |
| Zustand | Neuware |
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