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A hands-on guide for professionals to perform various data science
tasks in R Key Features Explore the popular R packages for data
science Use R for efficient data mining, text analytics and feature
engineering Become a thorough data science professional with the
help of hands-on examples and use-cases in R Book DescriptionR is
the most widely used programming language, and when used in
association with data science, this powerful combination will solve
the complexities involved with unstructured datasets in the real
world. This book covers the entire data science ecosystem for
aspiring data scientists, right from zero to a level where you are
confident enough to get hands-on with real-world data science
problems. The book starts with an introduction to data science and
introduces readers to popular R libraries for executing data
science routine tasks. This book covers all the important processes
in data science such as data gathering, cleaning data, and then
uncovering patterns from it. You will explore algorithms such as
machine learning algorithms, predictive analytical models, and
finally deep learning algorithms. You will learn to run the most
powerful visualization packages available in R so as to ensure that
you can easily derive insights from your data. Towards the end, you
will also learn how to integrate R with Spark and Hadoop and
perform large-scale data analytics without much complexity. What
you will learn Understand the R programming language and its
ecosystem of packages for data science Obtain and clean your data
before processing Master essential exploratory techniques for
summarizing data Examine various machine learning prediction,
models Explore the H2O analytics platform in R for deep learning
Apply data mining techniques to available datasets Work with
interactive visualization packages in R Integrate R with Spark and
Hadoop for large-scale data analytics Who this book is forIf you
are a budding data scientist keen to learn about the popular pandas
library, or a Python developer looking to step into the world of
data analysis, this book is the ideal resource you need to get
started. Some programming experience in Python will be helpful to
get the most out of this course
Get command of your organizational Big Data using the power of data
science and analytics Key Features A perfect companion to boost
your Big Data storing, processing, analyzing skills to help you
take informed business decisions Work with the best tools such as
Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform
massive online analyses Get expert tips on statistical inference,
machine learning, mathematical modeling, and data visualization for
Big Data Book DescriptionBig Data analytics relates to the
strategies used by organizations to collect, organize and analyze
large amounts of data to uncover valuable business insights that
otherwise cannot be analyzed through traditional systems. Crafting
an enterprise-scale cost-efficient Big Data and machine learning
solution to uncover insights and value from your organization's
data is a challenge. Today, with hundreds of new Big Data systems,
machine learning packages and BI Tools, selecting the right
combination of technologies is an even greater challenge. This book
will help you do that. With the help of this guide, you will be
able to bridge the gap between the theoretical world of technology
with the practical ground reality of building corporate Big Data
and data science platforms. You will get hands-on exposure to
Hadoop and Spark, build machine learning dashboards using R and R
Shiny, create web-based apps using NoSQL databases such as MongoDB
and even learn how to write R code for neural networks. By the end
of the book, you will have a very clear and concrete understanding
of what Big Data analytics means, how it drives revenues for
organizations, and how you can develop your own Big Data analytics
solution using different tools and methods articulated in this
book. What you will learn - Get a 360-degree view into the world of
Big Data, data science and machine learning - Broad range of
technical and business Big Data analytics topics that caters to the
interests of the technical experts as well as corporate IT
executives - Get hands-on experience with industry-standard Big
Data and machine learning tools such as Hadoop, Spark, MongoDB,
KDB+ and R - Create production-grade machine learning BI Dashboards
using R and R Shiny with step-by-step instructions - Learn how to
combine open-source Big Data, machine learning and BI Tools to
create low-cost business analytics applications - Understand
corporate strategies for successful Big Data and data science
projects - Go beyond general-purpose analytics to develop
cutting-edge Big Data applications using emerging technologies Who
this book is forThe book is intended for existing and aspiring Big
Data professionals who wish to become the go-to person in their
organization when it comes to Big Data architecture, analytics, and
governance. While no prior knowledge of Big Data or related
technologies is assumed, it will be helpful to have some
programming experience.
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