Math for Data Science
Springer International Publishing (Verlag)
978-3-031-89706-1 (ISBN)
Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability.
Clear examples are supported with detailed figures and Python code; Jupyter notebooks and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level.
Omar Hijab obtained his doctorate from the University of California at Berkeley, and is faculty at Temple University in Philadelphia, Pennsylvania. He is currently affiliated with the University of New Haven in West Haven, Connecticut.
Preface.- List of Figures.- Datasets.- Linear Geometry.- Principal Components.- Calculus.- Probability.- Statistics.- Machine Learning.- A. Auxiliary Material.- B. Auxiliary Files.- References.- Python Index.- Index.
| Erscheinungsdatum | 28.05.2025 |
|---|---|
| Zusatzinfo | XV, 575 p. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Schlagworte | book math data analytics • book math data science • Calculus • entropy convexity • geometry of matrices • Gradient descent • Keras training • Linear Geometry • machine learning • math data science book • Network training • Neural networks • Probability • Python • SQL • textbook math for data science • text math data analysis • text math data science |
| ISBN-10 | 3-031-89706-4 / 3031897064 |
| ISBN-13 | 978-3-031-89706-1 / 9783031897061 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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