Practitioner’s Guide to Data Science (Chapman & Hall/CRC Data Science Series) 1st Edition
by Hui Lin(Author), Ming Li(Author)
Publisher finelybook 出版社: Chapman and Hall/CRC; 1st edition (May 12, 2023)
Language 语言: English
Print Length 页数: 376 pages
ISBN-10: 0815354398
ISBN-13: 9780815354390
Book Description
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python.
This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. 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 find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes.
Key Features:
• It covers both technical and soft skills.
• It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment.
• It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
