Data Scientist Pocket Guide
Download - Data Scientist Pocket Guide: Over 600 Concepts, Terminologies, and Processes of Machine Learning and Deep Learning Assembled Together by Mohamed Sabri - ePUB
At the beginning of my career as a data scientist, I use to go on search engines and use various sources to find explanations about a concept in data science. This was time consuming and the answers to my questions where not always reliable. It is hard for any data scientist to find quickly all the answers to his questions and sometimes answers vary from a source to another. Also, some concepts are hard to understand so you have to find a source that explains clearly what a concept means. This book is a first of a kind dictionary or glossary that regroups the most popular terms in data science. It helps data scientist from beginners to senior to look for definitions very quickly and have reliable answers to their questions. Usually books in data science focuses on coding and on practical use cases, whereas this book goal is to explain concepts and give a better idea to data scientist about what the words means. It’s good to be able to code in data science and build machine learning models but if the data scientist doesn’t understand the logic and the mechanism behind each concept it is hard for him to provide good results and explain its work. I hope you will keep this book as your Bible for data science and use it each time you have doubt about a concept’s meaning. Have fun!
This book is separated into two sections. The first section is composed of 26 chapters, each chapter correspond to a letter in the alphabet and a set of definitions in each chapter. The second section is an FAQ or frequently asked questions and it contains all the questions that a data scientist might have when it comes to data science, the questions covers some theorical parts and others are more practical such as “should I learn R or Python?”.
This book objective is not be read all at once but to become your data science Bible, so each time you might have a question about a concept and wondering how it works or what does it mean you might look at the book for answers. Also, this book is a good support for beginners that are always confused around all the concepts that they might find in data science. So, the lecture of this book is not linear you might start to read wherever you want and jump to any chapter based on the answers you are looking for. This book is a first of a kind in data science as no other book regroup as much terms in the field as this book does.
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