Learn how to apply powerful data analysis techniques with popular
open source Python modules About This Book * Find, manipulate, and
analyze your data using the Python 3.5 libraries * Perform
advanced, high-performance linear algebra and mathematical
calculations with clean and efficient Python code * An
easy-to-follow guide with realistic examples that are frequently
used in real-world data analysis projects. Who This Book Is For
This book is for programmers, scientists, and engineers who have
the knowledge of Python and know the basics of data science. It is
for those who wish to learn different data analysis methods using
Python 3.5 and its libraries. This book contains all the basic
ingredients you need to become an expert data analyst. What You
Will Learn * Install open source Python modules such NumPy, SciPy,
Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on
various platforms * Prepare and clean your data, and use it for
exploratory analysis * Manipulate your data with Pandas * Retrieve
and store your data from RDBMS, NoSQL, and distributed filesystems
such as HDFS and HDF5 * Visualize your data with open source
libraries such as matplotlib, bokeh, and plotly * Learn about
various machine learning methods such as supervised, unsupervised,
probabilistic, and Bayesian * Understand signal processing and time
series data analysis * Get to grips with graph processing and
social network analysis In Detail Data analysis techniques generate
useful insights from small and large volumes of data. Python, with
its strong set of libraries, has become a popular platform to
conduct various data analysis and predictive modeling tasks. With
this book, you will learn how to process and manipulate data with
Python for complex analysis and modeling. We learn data
manipulations such as aggregating, concatenating, appending,
cleaning, and handling missing values, with NumPy and Pandas. The
book covers how to store and retrieve data from various data
sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to
visualize data using visualization libraries, along with advanced
topics such as signal processing, time series, textual data
analysis, machine learning, and social media analysis. The book
covers a plethora of Python modules, such as matplotlib,
statsmodels, scikit-learn, and NLTK. It also covers using Python
with external environments such as R, Fortran, C/C++, and Boost
libraries. Style and approach The book takes a very comprehensive
approach to enhance your understanding of data analysis. Sufficient
real-world examples and use cases are included in the book to help
you grasp the concepts quickly and apply them easily in your
day-to-day work. Packed with clear, easy to follow examples, this
book will turn you into an ace data analyst in no time.
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