Learn to effectively manage data and execute data science projects
from start to finish using Python Key Features Understand and
utilize data science tools in Python, such as specialized machine
learning algorithms and statistical modeling Build a strong data
science foundation with the best data science tools available in
Python Add value to yourself, your organization, and society by
extracting actionable insights from raw data Book
DescriptionPractical Data Science with Python teaches you core data
science concepts, with real-world and realistic examples, and
strengthens your grip on the basic as well as advanced principles
of data preparation and storage, statistics, probability theory,
machine learning, and Python programming, helping you build a solid
foundation to gain proficiency in data science. The book starts
with an overview of basic Python skills and then introduces
foundational data science techniques, followed by a thorough
explanation of the Python code needed to execute the techniques.
You'll understand the code by working through the examples. The
code has been broken down into small chunks (a few lines or a
function at a time) to enable thorough discussion. As you progress,
you will learn how to perform data analysis while exploring the
functionalities of key data science Python packages, including
pandas, SciPy, and scikit-learn. Finally, the book covers ethics
and privacy concerns in data science and suggests resources for
improving data science skills, as well as ways to stay up to date
on new data science developments. By the end of the book, you
should be able to comfortably use Python for basic data science
projects and should have the skills to execute the data science
process on any data source. What you will learn Use Python data
science packages effectively Clean and prepare data for data
science work, including feature engineering and feature selection
Data modeling, including classic statistical models (such as
t-tests), and essential machine learning algorithms, such as random
forests and boosted models Evaluate model performance Compare and
understand different machine learning methods Interact with Excel
spreadsheets through Python Create automated data science reports
through Python Get to grips with text analytics techniques Who this
book is forThe book is intended for beginners, including students
starting or about to start a data science, analytics, or related
program (e.g. Bachelor's, Master's, bootcamp, online courses),
recent college graduates who want to learn new skills to set them
apart in the job market, professionals who want to learn hands-on
data science techniques in Python, and those who want to shift
their career to data science. The book requires basic familiarity
with Python. A "getting started with Python" section has been
included to get complete novices up to speed.
General
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