Essential Math for Data Science

Name: Essential Math for Data Science
Author: Thomas Nield

by Thomas Nield

Computers

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

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Bridges

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Delving into the foundational mathematical concepts that power the modern data landscape, "Essential Math for Data Science" by Thomas Nield acts as a crucial nexus, bridging the gap between abstract theory and practical application, particularly for those who have already begun to explore computational thinking through titles like "Grokking Algorithms, Second Edition" by Aditya Y Bhargava. This connection is not merely superficial; rather, it signifies a shared commitment to cultivating a profound understanding of logical precision and structured problem-solving. For readers who have navigated the elegant flow of algorithms, understanding how data science relies on a rigorously defined mathematical language will feel like a natural progression. The clarity and sequential thinking inherent in understanding algorithms provide a powerful "vibe" for appreciating the beauty of mathematical underpinnings in areas like calculus, linear algebra, and probability, which are meticulously detailed in Nield's work. This synergy empowers learners to approach complex data science challenges with a consistent and analytical lens, much like one would break down an algorithmic problem. The journey from understanding computational steps to grasping the mathematical principles that drive them is a seamless bridge, reinforcing the idea that a strong analytical foundation is paramount, whether you're optimizing a search function or predicting future trends.