Essential Math for Data Science 1st Edition by Thomas Nield

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Essential Math for Data Science 1st Edition by Thomas Nield PDF

Author: Thomas Nield

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