Exploring Math for Programmers and Data Scientists
With chapters selected by Paul Orland
Strong math skills are a prerequisite if you’re interested in a career in data science, artificial intelligence, cryptography, or virtually any tech field. This free mini ebook is the perfect primer to essential math applications you need to break into these exciting and lucrative technology careers.
About the book
Exploring Math for Programmers and Data Scientists showcases chapters from three Manning books, chosen by author and master-of-math Paul Orland. You’ll start with a look at the nearest neighbor search problem, common with multidimensional data, and walk through a real-world solution for tackling it. Next, you’ll delve into a set of methods and techniques integral to Principal Component Analysis (PCA), an underlying technique in Latent Semantic Analysis (LSA) for document retrieval. In the last chapter, you’ll work with digital audio data, using mathematical functions in different and interesting ways. Begin sharpening your competitive edge with the fun and fascinating math in this (free!) practical guide!
“Nearest Neighbors Search” – Chapter 8 from Algorithms and Data Structures in Action by Marcello La Rocca
“Linear Algebraic Tools in Machine Learning and Data Science” – Chapter 4 from Math and Architectures of Deep Learning by Krishnendu Chaudhury
“Analyzing sound waves with Fourier series” - Chapter 13 from Math for Programmers by Paul Orland
Paul Orland is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. As founding CTO, he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations. He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.
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