Practitioner's Guide to Data Science

This book aims to increase the visibility of data science in real-world applications, covering topics such as big data cloud platforms, soft skills, and career paths. It uses R as the primary tool and provides code for both R and Python, making it accessible to readers with various backgrounds. The hands-on approach and reproducible code in notebooks allow readers to practice data science and apply it to their own data and problems.

Format: Hardback
Length: 378 pages
Publication date: 24 May 2023
Publisher: Taylor & Francis Inc

This book is a comprehensive guide to increasing the visibility of data science in real-world applications, which differs from the traditional textbook-based knowledge. It recognizes that many aspects of day-to-day data science work are often overlooked in conventional statistics, machine learning, and data science curricula. However, these activities constitute a significant portion of the time and effort spent by data professionals in the industry.

Based on extensive industry experience, the book outlines real-world scenarios and highlights pitfalls that data science practitioners should avoid. It also delves into the big data cloud platform and the art of data science, including soft skills, which are essential for success in this field.

The authors use R as the primary tool and provide code for both R and Python, making it accessible to readers with varying programming backgrounds. This book is designed for readers who are interested in exploring possible career paths in data science and eventually becoming skilled data scientists.

The book comprehensively introduces various data science fields, including machine learning, natural language processing, and data visualization. It also covers essential soft skills such as communication, teamwork, and problem-solving, which are crucial for data science projects.

Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will benefit from learning real-world data science applications, which can enhance their job prospects.

Non-mathematical readers will appreciate the reproducibility of the companion R and Python codes, which allow readers to repeat the analysis in the book using the provided data and code. The authors also suggest modifications and extensions to the code, allowing readers to customize it to their specific needs.

In conclusion, this book is a valuable resource for anyone who wants to gain a deeper understanding of data science and its applications in the real world. It provides a comprehensive introduction to various data science fields, soft skills, and potential career paths. With its hands-on approach and reproducible code, it is suitable for readers of all backgrounds and levels of experience. Whether you are a professional looking to advance your career or a student seeking to learn more about data science, this book will help you achieve your goals.

Weight: 900g
Dimension: 234 x 156 (mm)
ISBN-13: 9780815354475