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Learning Data Science Data Wrangling Exploration Visualization And Modeling With Python 1st Edition Sam Lau

  • SKU: BELL-52600430
Learning Data Science Data Wrangling Exploration Visualization And Modeling With Python 1st Edition Sam Lau
$ 31.00 $ 45.00 (-31%)

4.4

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Learning Data Science Data Wrangling Exploration Visualization And Modeling With Python 1st Edition Sam Lau instant download after payment.

Publisher: O'Reilly Media
File Extension: PDF
File size: 20.44 MB
Pages: 597
Author: Sam Lau, Joseph Gonzalez, Deborah Nolan
ISBN: 9781098113001, 1098113004
Language: English
Year: 2023
Edition: 1

Product desciption

Learning Data Science Data Wrangling Exploration Visualization And Modeling With Python 1st Edition Sam Lau by Sam Lau, Joseph Gonzalez, Deborah Nolan 9781098113001, 1098113004 instant download after payment.

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions—whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.

Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.

• Refine a question of interest to one that can be studied with data
• Pursue data collection that may involve text processing, web scraping, etc.
• Glean valuable insights about data through data cleaning, exploration, and visualization
• Learn how to use modeling to describe the data
• Generalize findings beyond the data

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