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* Targests readers with a background in programming, interested in
an introduction/refresher in statistical hypothesis testing * Uses
Python throughout * Provides the reader with the opportunity of
using the book whenever needed rather than following a sequential
path.
* Targests readers with a background in programming, interested in
an introduction/refresher in statistical hypothesis testing * Uses
Python throughout * Provides the reader with the opportunity of
using the book whenever needed rather than following a sequential
path.
Advanced Data Science and Analytics with Python enables data
scientists to continue developing their skills and apply them in
business as well as academic settings. The subjects discussed in
this book are complementary and a follow-up to the topics discussed
in Data Science and Analytics with Python. The aim is to cover
important advanced areas in data science using tools developed in
Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK,
NetworkX and others. The model development is supported by the use
of frameworks such as Keras, TensorFlow and Core ML, as well as
Swift for the development of iOS and MacOS applications. Features:
Targets readers with a background in programming, who are
interested in the tools used in data analytics and data science
Uses Python throughout Presents tools, alongside solved examples,
with steps that the reader can easily reproduce and adapt to their
needs Focuses on the practical use of the tools rather than on
lengthy explanations Provides the reader with the opportunity to
use the book whenever needed rather than following a sequential
path The book can be read independently from the previous volume
and each of the chapters in this volume is sufficiently independent
from the others, providing flexibility for the reader. Each of the
topics addressed in the book tackles the data science workflow from
a practical perspective, concentrating on the process and results
obtained. The implementation and deployment of trained models are
central to the book. Time series analysis, natural language
processing, topic modelling, social network analysis, neural
networks and deep learning are comprehensively covered. The book
discusses the need to develop data products and addresses the
subject of bringing models to their intended audiences - in this
case, literally to the users' fingertips in the form of an iPhone
app. About the Author Dr. Jesus Rogel-Salazar is a lead data
scientist in the field, working for companies such as Tympa Health
Technologies, Barclays, AKQA, IBM Data Science Studio and Dow
Jones. He is a visiting researcher at the Department of Physics at
Imperial College London, UK and a member of the School of Physics,
Astronomy and Mathematics at the University of Hertfordshire, UK.
Data Science and Analytics with Python is designed for
practitioners in data science and data analytics in both academic
and business environments. The aim is to present the reader with
the main concepts used in data science using tools developed in
Python, such as SciKit-learn, Pandas, Numpy, and others. The use of
Python is of particular interest, given its recent popularity in
the data science community. The book can be used by seasoned
programmers and newcomers alike. The book is organized in a way
that individual chapters are sufficiently independent from each
other so that the reader is comfortable using the contents as a
reference. The book discusses what data science and analytics are,
from the point of view of the process and results obtained.
Important features of Python are also covered, including a Python
primer. The basic elements of machine learning, pattern
recognition, and artificial intelligence that underpin the
algorithms and implementations used in the rest of the book also
appear in the first part of the book. Regression analysis using
Python, clustering techniques, and classification algorithms are
covered in the second part of the book. Hierarchical clustering,
decision trees, and ensemble techniques are also explored, along
with dimensionality reduction techniques and recommendation
systems. The support vector machine algorithm and the Kernel trick
are discussed in the last part of the book. About the Author Dr.
Jesus Rogel-Salazar is a Lead Data scientist with experience in the
field working for companies such as AKQA, IBM Data Science Studio,
Dow Jones and others. He is a visiting researcher at the Department
of Physics at Imperial College London, UK and a member of the
School of Physics, Astronomy and Mathematics at the University of
Hertfordshire, UK, He obtained his doctorate in physics at Imperial
College London for work on quantum atom optics and ultra-cold
matter. He has held a position as senior lecturer in mathematics as
well as a consultant in the financial industry since 2006. He is
the author of the book Essential Matlab and Octave, also published
by CRC Press. His interests include mathematical modelling, data
science, and optimization in a wide range of applications including
optics, quantum mechanics, data journalism, and finance.
Advanced Data Science and Analytics with Python enables data
scientists to continue developing their skills and apply them in
business as well as academic settings. The subjects discussed in
this book are complementary and a follow-up to the topics discussed
in Data Science and Analytics with Python. The aim is to cover
important advanced areas in data science using tools developed in
Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK,
NetworkX and others. The model development is supported by the use
of frameworks such as Keras, TensorFlow and Core ML, as well as
Swift for the development of iOS and MacOS applications. Features:
Targets readers with a background in programming, who are
interested in the tools used in data analytics and data science
Uses Python throughout Presents tools, alongside solved examples,
with steps that the reader can easily reproduce and adapt to their
needs Focuses on the practical use of the tools rather than on
lengthy explanations Provides the reader with the opportunity to
use the book whenever needed rather than following a sequential
path The book can be read independently from the previous volume
and each of the chapters in this volume is sufficiently independent
from the others, providing flexibility for the reader. Each of the
topics addressed in the book tackles the data science workflow from
a practical perspective, concentrating on the process and results
obtained. The implementation and deployment of trained models are
central to the book. Time series analysis, natural language
processing, topic modelling, social network analysis, neural
networks and deep learning are comprehensively covered. The book
discusses the need to develop data products and addresses the
subject of bringing models to their intended audiences - in this
case, literally to the users' fingertips in the form of an iPhone
app. About the Author Dr. Jesus Rogel-Salazar is a lead data
scientist in the field, working for companies such as Tympa Health
Technologies, Barclays, AKQA, IBM Data Science Studio and Dow
Jones. He is a visiting researcher at the Department of Physics at
Imperial College London, UK and a member of the School of Physics,
Astronomy and Mathematics at the University of Hertfordshire, UK.
Learn Two Popular Programming Languages in a Single Volume Widely
used by scientists and engineers, well-established MATLAB and
open-source Octave are similar software programs providing
excellent capabilities for data analysis, visualization, and more.
By means of straightforward explanations and examples from
different areas in mathematics, engineering, finance, and physics,
Essential MATLAB and Octave explains how MATLAB and Octave are
powerful tools applicable to a variety of problems. This text
provides an introduction that reveals basic structures and syntax,
demonstrates the use of functions and procedures, outlines
availability in various platforms, and highlights the most
important elements for both programs. Effectively Implement Models
and Prototypes Using Computational Models This text requires no
prior knowledge. Self-contained, it allows the reader to use the
material whenever needed rather than follow a particular order.
Compatible with both languages, the book material incorporates
commands and structures that allow the reader to gain a greater
awareness of MATLAB and Octave, write their own code, and implement
their scripts and programs within a variety of applicable fields.
It is always made clear when particular examples apply only to
MATLAB or only to Octave, allowing the book to be used flexibly
depending on readers requirements. Includes brief, simple code that
works in both MATLAB and Octave Provides exercise sections at the
end of each chapter Introduces framed examples and discussions with
a scientific twist Exercises are provided at the end of each
chapter Essential MATLAB and Octave offers an introductory course
in MATLAB and Octave programming and is an authoritative resource
for students in phy
Data Science and Analytics with Python is designed for
practitioners in data science and data analytics in both academic
and business environments. The aim is to present the reader with
the main concepts used in data science using tools developed in
Python, such as SciKit-learn, Pandas, Numpy, and others. The use of
Python is of particular interest, given its recent popularity in
the data science community. The book can be used by seasoned
programmers and newcomers alike. The book is organized in a way
that individual chapters are sufficiently independent from each
other so that the reader is comfortable using the contents as a
reference. The book discusses what data science and analytics are,
from the point of view of the process and results obtained.
Important features of Python are also covered, including a Python
primer. The basic elements of machine learning, pattern
recognition, and artificial intelligence that underpin the
algorithms and implementations used in the rest of the book also
appear in the first part of the book. Regression analysis using
Python, clustering techniques, and classification algorithms are
covered in the second part of the book. Hierarchical clustering,
decision trees, and ensemble techniques are also explored, along
with dimensionality reduction techniques and recommendation
systems. The support vector machine algorithm and the Kernel trick
are discussed in the last part of the book. About the Author Dr.
Jesus Rogel-Salazar is a Lead Data scientist with experience in the
field working for companies such as AKQA, IBM Data Science Studio,
Dow Jones and others. He is a visiting researcher at the Department
of Physics at Imperial College London, UK and a member of the
School of Physics, Astronomy and Mathematics at the University of
Hertfordshire, UK, He obtained his doctorate in physics at Imperial
College London for work on quantum atom optics and ultra-cold
matter. He has held a position as senior lecturer in mathematics as
well as a consultant in the financial industry since 2006. He is
the author of the book Essential Matlab and Octave, also published
by CRC Press. His interests include mathematical modelling, data
science, and optimization in a wide range of applications including
optics, quantum mechanics, data journalism, and finance.
Learn Two Popular Programming Languages in a Single Volume Widely
used by scientists and engineers, well-established MATLAB (R) and
open-source Octave are similar software programs providing
excellent capabilities for data analysis, visualization, and more.
By means of straightforward explanations and examples from
different areas in mathematics, engineering, finance, and physics,
Essential MATLAB and Octave explains how MATLAB and Octave are
powerful tools applicable to a variety of problems. This text
provides an introduction that reveals basic structures and syntax,
demonstrates the use of functions and procedures, outlines
availability in various platforms, and highlights the most
important elements for both programs. Effectively Implement Models
and Prototypes Using Computational Models This text requires no
prior knowledge. Self-contained, it allows the reader to use the
material whenever needed rather than follow a particular order.
Compatible with both languages, the book material incorporates
commands and structures that allow the reader to gain a greater
awareness of MATLAB and Octave, write their own code, and implement
their scripts and programs within a variety of applicable fields.
It is always made clear when particular examples apply only to
MATLAB or only to Octave, allowing the book to be used flexibly
depending on readers' requirements. Includes brief, simple code
that works in both MATLAB and Octave Provides exercise sections at
the end of each chapter Introduces framed examples and discussions
with a scientific twist Exercises are provided at the end of each
chapter Essential MATLAB and Octave offers an introductory course
in MATLAB and Octave programming and is an authoritative resource
for students in physics, mathematics, statistics, engineering, and
any other subjects that require the use of computers to solve
numerical problems.
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