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: Paperback / softback
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 approach. It recognizes that many aspects of day-to-day data science work are often overlooked in conventional statistics, machine learning, and data science curricula, despite their significant importance to data professionals in the industry.

Based on extensive industry experience, the book outlines real-world scenarios and discusses common 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 that are crucial for success in this field.

The authors use R as the primary tool throughout the book, providing code for both R and Python to facilitate hands-on learning. This makes the book accessible to readers with varying levels of mathematical backgrounds, as the reproducibility of the code allows them to repeat the analysis and apply the concepts to their own data.

The book is designed for readers who are interested in exploring possible career paths in data science and eventually becoming skilled data scientists. It provides a comprehensive introduction to various data science fields, including machine learning, natural language processing, and data visualization. It also covers essential soft and programming skills required for data science projects, such as data cleaning, data manipulation, and programming in R and Python.

Furthermore, the book offers insights into potential career paths in data science, including roles in data analytics, business intelligence, and machine learning engineering. It provides advice on how to build a strong professional network, develop marketable skills, and secure job opportunities in the data science industry.

Whether you are a traditional data-related practitioner looking to expand your skills for future data science careers, an undergraduate or graduate student in analytics-related areas, or a non-mathematical reader interested in learning real-world data science applications, this book is an invaluable resource. It provides a comprehensive and hands-on approach to data science, equipping you with the knowledge and tools you need to succeed in this rapidly growing field.

Weight: 740g
Dimension: 234 x 156 (mm)
ISBN-13: 9780815354390