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Practical Data Science with Python 3 - Synthesizing Actionable Insights from Data (Paperback, 1st ed.)
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Practical Data Science with Python 3 - Synthesizing Actionable Insights from Data (Paperback, 1st ed.)
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Gain insight into essential data science skills in a holistic
manner using data engineering and associated scalable computational
methods. This book covers the most popular Python 3 frameworks for
both local and distributed (in premise and cloud based) processing.
Along the way, you will be introduced to many popular open-source
frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The
book is structured around examples, so you will grasp core concepts
via case studies and Python 3 code. As data science projects gets
continuously larger and more complex, software engineering
knowledge and experience is crucial to produce evolvable solutions.
You'll see how to create maintainable software for data science and
how to document data engineering practices. This book is a good
starting point for people who want to gain practical skills to
perform data science. All the code will be available in the form of
IPython notebooks and Python 3 programs, which allow you to
reproduce all analyses from the book and customize them for your
own purpose. You'll also benefit from advanced topics like Machine
Learning, Recommender Systems, and Security in Data Science.
Practical Data Science with Python will empower you analyze data,
formulate proper questions, and produce actionable insights, three
core stages in most data science endeavors. What You'll Learn Play
the role of a data scientist when completing increasingly
challenging exercises using Python 3 Work work with proven data
science techniques/technologies Review scalable software
engineering practices to ramp up data analysis abilities in the
realm of Big Data Apply theory of probability, statistical
inference, and algebra to understand the data science practices Who
This Book Is For Anyone who would like to embark into the realm of
data science using Python 3.
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