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Books > Computing & IT > General theory of computing
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Practical Data Science Cookbook - (Paperback, 2nd Revised edition)
Loot Price: R1,373
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Practical Data Science Cookbook - (Paperback, 2nd Revised edition)
Expected to ship within 10 - 15 working days
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Over 85 recipes to help you complete real-world data science
projects in R and Python About This Book * Tackle every step in the
data science pipeline and use it to acquire, clean, analyze, and
visualize your data * Get beyond the theory and implement
real-world projects in data science using R and Python *
Easy-to-follow recipes will help you understand and implement the
numerical computing concepts Who This Book Is For If you are an
aspiring data scientist who wants to learn data science and
numerical programming concepts through hands-on, real-world project
examples, this is the book for you. Whether you are brand new to
data science or you are a seasoned expert, you will benefit from
learning about the structure of real-world data science projects
and the programming examples in R and Python. What You Will Learn *
Learn and understand the installation procedure and environment
required for R and Python on various platforms * Prepare data for
analysis by implement various data science concepts such as
acquisition, cleaning and munging through R and Python * Build a
predictive model and an exploratory model * Analyze the results of
your model and create reports on the acquired data * Build various
tree-based methods and Build random forest In Detail As increasing
amounts of data are generated each year, the need to analyze and
create value out of it is more important than ever. Companies that
know what to do with their data and how to do it well will have a
competitive advantage over companies that don't. Because of this,
there will be an increasing demand for people that possess both the
analytical and technical abilities to extract valuable insights
from data and create valuable solutions that put those insights to
use. Starting with the basics, this book covers how to set up your
numerical programming environment, introduces you to the data
science pipeline, and guides you through several data projects in a
step-by-step format. By sequentially working through the steps in
each chapter, you will quickly familiarize yourself with the
process and learn how to apply it to a variety of situations with
examples using the two most popular programming languages for data
analysis-R and Python. Style and approach This step-by-step guide
to data science is full of hands-on examples of real-world data
science tasks. Each recipe focuses on a particular task involved in
the data science pipeline, ranging from readying the dataset to
analytics and visualization
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