Essential Math for Data Science
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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.
Practical 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:
Recognize the nuances and pitfalls of probability math
Master statistics and hypothesis testing (and avoid common pitfalls)
Discover practical applications of probability, statistics, calculus, and machine learning
Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added
Perform calculus derivatives and integrals completely from scratch in Python
Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks
Thomas Nield is the founder of Nield Consulting Group as well as an instructor at O'Reilly Media and University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. He's authored two books, including Getting Started with SQL (O'Reilly) and Learning RxJava (Packt).