Mathematica Data Analysis. Learn and explore the fundamentals of data analysis with power of Mathematica - Helion
Tytuł oryginału: Mathematica Data Analysis. Learn and explore the fundamentals of data analysis with power of Mathematica
ISBN: 9781785884450
stron: 164, Format: ebook
Data wydania: 2015-12-24
Księgarnia: Helion
Cena książki: 98,10 zł (poprzednio: 109,00 zł)
Oszczędzasz: 10% (-10,90 zł)
There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis.
If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure.
With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems.
With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
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Spis treści
Mathematica Data Analysis. Learn and explore the fundamentals of data analysis with power of Mathematica eBook -- spis treści
- 1. First Steps in Data Analysis
- 2. Broad Capabilities for Data Import
- 3. Create an Interface for an External Program
- 4. Analyzing Data with the Help of Mathematica
- 5. Discovering the Advanced Capabilities of the Time Series
- 6. Statistical Hypothesis Testing in Two Clicks
- 7. Predicting the Dataset Behavior
- 8. Rock-Paper-Scissors: Intelligent Processing of the Datasets