How Data Scientists can easily develop themselves?

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How Data Scientists can easily develop themselves?

By ridhigrg |Email | Jul 29, 2019 | 19917 Views

Data Science (MIT Press Essential Knowledge series) Paperback - 18 May 2018

by John D. Kelleher

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning.

Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Data Science in Practice (Studies in Big Data) 1st ed. 2019 Edition

by Alan Said

This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open-source software. The book offers an essential guide to modern data science for all students, practitioners, developers, and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

The Art of Data Science Paperback - 8 Jun 2016

by Roger Peng

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

An Introduction to Data Science First Edition

by Jeffrey S. Saltz

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.

Numsense! Data Science for the Layman: No Math Added Paperback - Large Print, 24 Mar 2017

by Kenneth Soo

This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of Features:

Intuitive explanations and visuals
Real-world applications to illustrate each algorithm
Point summaries at the end of each chapter
Reference sheets comparing the pros and cons of algorithms
Glossary list of commonly-used terms
With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

Source: HOB

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