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!
