I believe that the book describes itself quite well when it says: Mathematically correct yet highly intuitive…This book would be great for a class that one takes before one takes my statistical learning class. I often run into beginning graduate Data Science students whose background is not math (e.g., CS or Business) and they are not ready…The book fills an important niche, in that it provides a self-contained introduction to material that is useful for a higher-level statistical learning course. I think that it compares well with competing books, particularly in that it takes a more "Data Science" and "example driven" approach than more classical books." "This text by Matloff (Univ. of California, Davis) affords an excellent introduction to statistics for the data science student…Its examples are often drawn from data science applications such as hidden Markov models and remote sensing, to name a few… All the models and concepts are explained well in precise mathematical terms (not presented as formal proofs), to help students gain an intuitive understanding."

Advanced, graduate-level book for data scientists who wish to move into deep learning.

As the field of data science explodes, data professionals are increasingly using programming language Python to get work done, over other tools such as R and SQL, according to Harnham's US Data & Analytics Salary Guide 2019, released this week. With more professionals entering the field, the data science industry is moving from traditional "core" data scientists toward those with more specialized skillsets, the report noted. And in an active market, candidates are often able to be selective about the jobs they take. Data scientists move between roles more quickly than professionals in any other part of the tech industry, averaging less than two years in each position, the report found. SEE: Python is eating the world: How one developer's side project became the hottest programming language on the planet (cover story PDF) (TechRepublic) When it comes to tools, a common debate in the data science realm is whether or not Python or R is a better programming language for data work.

Python vs. R is a common debate among data scientists, as both languages are useful for data work and among the most frequently mentioned skills in job postings for data science positions. Each language offers different advantages and disadvantages for data science work, and should be chosen depending on the work you are doing. To help data scientists select the right language, Norm Matloff, a professor of computer science at the University of California Davis wrote a Github post aiming to shed some light on the debate. While this is subjective, Python greatly reduces the use of parentheses and braces when coding, making it more sleek, Matloff wrote in the post. While data scientists working with Python must learn a lot of material to get started, including NumPy, Pandas and matplotlib, matrix types and basic graphics are already built into base R, Matloff wrote.