To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.\n\nPractical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:\n\nRecognize the nuances and pitfalls of probability math\nMaster statistics and hypothesis testing (and avoid common pitfalls)\nDiscover practical applications of probability, statistics, calculus, and machine learning\nIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added\nPerform calculus derivatives and integrals completely from scratch in Python\nApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks
