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Practitioner's Guide to Data Science (Paperback)
Loot Price: R1,645
Discovery Miles 16 450
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Practitioner's Guide to Data Science (Paperback)
Series: Chapman & Hall/CRC Data Science Series
Expected to ship within 12 - 17 working days
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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!
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