A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.
You will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
Yuli Vasiliev is a programmer, freelance writer, and consultant, who has been working with databases for more than two decades. He specializes in open-source development, and is experienced in building data structures and models, as well as designing and implementing database backends for various applications using Oracle technologies, MySQL, and natural language processing. Vasiliev is the author of Natural Language Processing with spaCy (No Starch Press.
"A great introduction to Python for data science in a compact
package. I'm impressed with what Yuli Vasiliev included: the basics
of Python, multiple angles at preparation, and a number of chapters
on common types of data. The book is surprisingly visual for a
field known for rows and columns—the location data was especially
interesting to me. By the end of [Python for Data Science], I was
ready to apply the principles to my own datasets."
—Adam DuVander, @adamd, Founder of EveryDeveloper
"Python to help you evaluate information is a great skill to have.
Python for Data Science, written by Yuli Vasiliev, is an excellent
resource for someone interested in getting started with Data
Science. Although not intended for beginners in data analytics or
python programming, this book explains how to implement some
essential python concepts, gives excellent code snippets for
practice, and guides you on using a few important libraries useful
for processing and analyzing data with code."
—Kelly Schuster-Paredes, Co-Host of Teaching Python Podcast,
Educator
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