Data Science
Springer Berlin (Verlag)
978-3-662-67881-7 (ISBN)
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
lt;b>Matthias Plaue works as a data scientist and uses mathematical methods in daily practice to implement algorithms in the field of data analysis and artificial intelligence.In addition to research in his areas of interest, he has spent many years helping students to learn mathematics and how to apply it to solve problems in science, technology, and engineering.
Preface.- Part I Basics.- 1 Elements of data organization.- 2 Descriptive statistics.- Part II Stochastics.- 3 Probability theory.- 4 Inferential statistics.- 5 Multivariate statistics.- Part III Machine learning.- 6 Supervised machine learning.- 7 Unsupervised machine learning.- 8 Applications of machine learning.- Appendix.- A Exercises with answers.- B Mathematical preliminaries.- Supplementary literature.- Index.
"The book covers a wide range of topics, from basic statistical concepts to advanced machine learning algorithms. It is both deep and broad, making it a valuable resource for both beginners and experienced practitioners. Each concept is well explained and often accompanied by practical examples, which enhances understanding. The inclusion of real-world examples and applications of machine learning techniques is a major strength." (Wael Badawy, Computing Reviews, March 1, 2024)
| Erscheinungsdatum | 02.09.2023 |
|---|---|
| Zusatzinfo | XXIV, 361 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 593 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Mathematik | |
| Schlagworte | Artificial Intelligence • Big Data • Deep learning • machine learning • Statistics |
| ISBN-10 | 3-662-67881-0 / 3662678810 |
| ISBN-13 | 978-3-662-67881-7 / 9783662678817 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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