Essential Math for Data Science:
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Essential Math for Data Science:
Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
$32.90
$56.90
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Detailed Overview:
Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by Thomas Nield is a practical and beginner-friendly guide designed for aspiring data scientists, analysts, and machine learning enthusiasts who want to build a strong mathematical foundation without getting overwhelmed by complex theory. The book explains essential concepts such as algebra, calculus, probability, statistics, linear algebra, regression, and neural networks using clear language and real-world examples. Instead of focusing heavily on abstract equations, the author demonstrates how mathematical principles directly apply to data science and machine learning workflows.
Readers are introduced to key topics like descriptive statistics, probability distributions, matrices, gradient descent, logistic regression, and neural networks in an accessible and application-oriented manner. The book also incorporates Python examples using libraries such as NumPy, SymPy, and scikit-learn, making it ideal for practical learners who want hands-on understanding alongside theory.
One of the strongest aspects of this book is its ability to bridge the gap between mathematics and real-world data science applications. Thomas Nield’s engaging writing style makes complex concepts easier to understand for beginners while still offering valuable insights for intermediate learners. It is an excellent resource for students, self-taught programmers, data analysts, and professionals transitioning into artificial intelligence and machine learning careers
Essential Math for Data Science provides a complete introduction to the mathematical concepts required for modern data science and machine learning. The book covers core areas including algebra, calculus, probability, statistics, linear algebra, regression models, and neural networks. It emphasizes practical understanding rather than advanced theoretical derivations, making it highly suitable for beginners.
The author explains difficult mathematical ideas using simple language, visual intuition, and real-world coding examples in Python. Readers learn how concepts such as vectors, matrices, probability distributions, hypothesis testing, and gradient descent are applied in predictive modeling and machine learning algorithms. The book also explores practical topics like overfitting, model evaluation, and career guidance for aspiring data professionals.
This book is an excellent learning resource for students, developers, analysts, and professionals seeking to strengthen their mathematical understanding for data science and AI applications
Thomas Nield is a data science educator, software developer, and instructor known for simplifying complex technical concepts for beginners. He has taught courses on data analytics, machine learning, artificial intelligence, and mathematical optimization. Thomas Nield is also the founder of Nield Consulting Group and has worked extensively in analytics and software development. His teaching style focuses on practical applications, helping readers and students understand technical subjects through real-world examples and accessible explanations
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