The use of data in solving real-world problems has become all the rage in contemporary times. The field of data science has significantly evolved, necessitating the need for cutting-edge materials to keep data functionaries abreast in an ever dynamic industry. An invaluable resource in this regard is the Practical Statistics for Data Scientists: 50 Essential Concepts, an authoritative book by Peter Bruce and Andrew Bruce that seeks to demystify key statistical concepts that every data scientist needs to know.
This textual masterpiece, available on Amazon, is an upgrade of the previous edition and comes with five new modern data science techniques. It covers 50 essential concepts that data scientists need to thoroughly comprehend to function effectively in their various roles. Statistics remain pivotal to the field of data science, and this book beautifully presents these statistical concepts in a simplified but comprehensive manner.
This second edition is witness to five new methods, including gradient descent, regularization, and graphical models. Furthermore, this invaluable guide offers practical, real-world insights into how these core concepts of statistics translate into effective data science. The references to open source software, most notably R and Python, speak to the practicality of the work.
Practical Statistics for Data Scientists: 50 Essential Concepts is an indispensable guide for professional data scientists and those aspiring to join this fascinating field. By exploring it, you get to stay updated regarding the changing techniques in the world of data science.
