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A01=Genevieve Love
A01=Kennedy Behrman
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Author_Kennedy Behrman
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Foundational Python for Data Science

English

By (author): Genevieve Love Kennedy Behrman

Data science and machine learningtwo of the world's hottest fieldsare attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. 

Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.

Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and moreall created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
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A01=Genevieve LoveA01=Kennedy BehrmanAge Group_UncategorizedAuthor_Genevieve LoveAuthor_Kennedy Behrmanautomatic-updateCategory1=Non-FictionCategory=UMXCategory=UNDCategory=UNFCOP=United StatesDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=In stockPrice_€20 to €50PS=Activesoftlaunch
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Product Details
  • Weight: 420g
  • Dimensions: 178 x 230mm
  • Publication Date: 24 Jan 2022
  • Publisher: Pearson Education (US)
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9781350160361

About Genevieve LoveKennedy Behrman

Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world using Python at startups using machine learning to characterize videos and predict the social media power of athletes.

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