Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks
Autor Jalil Villalobos Alvaen Limba Engleză Paperback – 2 feb 2021
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
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.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (2) | 266.66 lei 3-5 săpt. | |
Apress – 2 feb 2021 | 266.66 lei 3-5 săpt. | |
Apress – 5 iul 2024 | 311.89 lei 3-5 săpt. |
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Specificații
ISBN-13: 9781484265932
ISBN-10: 1484265939
Pagini: 416
Ilustrații: XXIII, 416 p. 344 illus., 54 illus. in color.
Dimensiuni: 178 x 254 mm
Greutate: 0.76 kg
Ediția:First Edition
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
ISBN-10: 1484265939
Pagini: 416
Ilustrații: XXIII, 416 p. 344 illus., 54 illus. in color.
Dimensiuni: 178 x 254 mm
Greutate: 0.76 kg
Ediția:First Edition
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States
Cuprins
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.
Notă biografică
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.
Textul de pe ultima copertă
Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages.
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.
You will:
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
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.
You will:
Caracteristici
The first introduction to data science using Mathematica and Wolfram Covers very popular in-demand topics such as machine learning and neural networks Includes freely available source code