Get your statistics basics right before diving into the world of
data science About This Book * No need to take a degree in
statistics, read this book and get a strong statistics base for
data science and real-world programs; * Implement statistics in
data science tasks such as data cleaning, mining, and analysis *
Learn all about probability, statistics, numerical computations,
and more with the help of R programs Who This Book Is For This book
is intended for those developers who are willing to enter the field
of data science and are looking for concise information of
statistics with the help of insightful programs and simple
explanation. Some basic hands on R will be useful. What You Will
Learn * Analyze the transition from a data developer to a data
scientist mindset * Get acquainted with the R programs and the
logic used for statistical computations * Understand mathematical
concepts such as variance, standard deviation, probability, matrix
calculations, and more * Learn to implement statistics in data
science tasks such as data cleaning, mining, and analysis * Learn
the statistical techniques required to perform tasks such as linear
regression, regularization, model assessment, boosting, SVMs, and
working with neural networks * Get comfortable with performing
various statistical computations for data science programmatically
In Detail Data science is an ever-evolving field, which is growing
in popularity at an exponential rate. Data science includes
techniques and theories extracted from the fields of statistics;
computer science, and, most importantly, machine learning,
databases, data visualization, and so on. This book takes you
through an entire journey of statistics, from knowing very little
to becoming comfortable in using various statistical methods for
data science tasks. It starts off with simple statistics and then
move on to statistical methods that are used in data science
algorithms. The R programs for statistical computation are clearly
explained along with logic. You will come across various
mathematical concepts, such as variance, standard deviation,
probability, matrix calculations, and more. You will learn only
what is required to implement statistics in data science tasks such
as data cleaning, mining, and analysis. You will learn the
statistical techniques required to perform tasks such as linear
regression, regularization, model assessment, boosting, SVMs, and
working with neural networks. By the end of the book, you will be
comfortable with performing various statistical computations for
data science programmatically. Style and approach Step by step
comprehensive guide with real world examples
General
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