Hands-On Mathematics for Data Scientists

More Information
  • Get an essential understanding of Set algebra, discrete math, and numbers
  • Learn how to use Python packages like SciPy and PuLP to solve simple optimization problems
  • Understand descriptive statistics and probability for data analysis, Inferential statistics, Bayesian statistics
  • Learn how to integrate linear algebra techniques and objects into machine learning algorithms
  • Understand important concepts of p-values, statistical power, and experimental/research design
  • Learn computational complexity for developing algorithms for solving Big Data problems
  • Solve linear equations using matrix inverse and Gauss-Jordan elimination techniques

This hands-on guide will help you sharpen the skillsets by understanding the required math for implementing machine learning models.

The book will start with giving you an overview of fundamental mathematical concepts such as set algebra and discrete math, various algebraic functions, plotting and visualization techniques, and more. You will cover essential topics such as calculus and key optimization techniques as applicable to machine learning. It will help you learn various statistical methods such as descriptive statistics and probability for data analysis, Inferential statistics, Bayesian statistics and more using examples. Further, the book focuses on the basic properties of vectors and matrices. It also touches on the advanced topic of principal component analysis, as an important component of machine learning pipeline. Lastly, you will be able to apply these learned topics to various popular machine learning algorithms such as linear and logistic regression, decision trees, support vector machine, and even cover advanced topics such as deep neural networks.

By the end of the book, you will build a strong foundation of mathematical skills, statistical knowledge, and data computation abilities to pursue a successful career as a highly efficient and impactful data scientist.

  • Implement complex mathematical and statistical concepts for solving data science problems using Python libraries
  • Explore essential mathematics behind the algorithmic methods to power machine learning and data science pipeline.
  • Learn and apply mathematics and statistics to build popular Machine learning algorithms
Page Count 378
Course Length 11 hours 20 minutes
ISBN 9781838554965
Date Of Publication 24 Jan 2020


Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.