Data Science Fundamentals Pocket Primer

by Oswald Campesato

As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available.


The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com.

428 pages, Paperback

Published June 8, 2021

Reviews

[Rick Sam · Rating 3 out of 5]

Jason Brownlee's Blog is easy way to understand Machine Learning & Deep Learning.

I don't think, People can read directly from Bishop, Trevor Hastie directly, without having sufficient pre-requisite knowledge.

Everyone has preferences.

I believe, reading from wide array of sources, would help a person to understand, with depth.

Working With Data
Probability and Statistics
Linear Algebra

I came across, Gini Impurity.

Gini impurity -- "measurement of likelihood of incorrect data"

I'd recommend anyone to go through the following, from this work.

Time: Few Hours

Deus Vult,
Gottfried