Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

Essential Math for Data Science by Thomas Nield is exactly what the title suggests. It covers the most important math concepts that are needed to work in data and analytics related jobs. The topics range from basic math, to probability, stats, linear algebra, and calculus.
By focusing on the most important aspects and by providing very manageable examples in Python, one can grasp the intuition behind these topics very fast. Even if you are already a seasoned vet, you might learn new things or at least see them from a different perspective:

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