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Hands-on data science with Anaconda / [written by] Dr. Yuxing Yan, James Yan.

Academic Video Online: Premium - United States Available online

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Format:
Video
Contributor:
Packt Publishing, production company.
Series:
Academic Video Online
Language:
English
Subjects (All):
ANACONDA (Electronic resource).
Information visualization.
Electronic data processing.
Genre:
Instructional films.
Physical Description:
1 online resource (153 minutes)
Place of Publication:
Birmingham, England : PACKT Publishing, 2018.
Language Note:
In English.
System Details:
video file
Summary:
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform: Anaconda. About This Video: Use Anaconda to find solutions for clustering, classification, and linear regression. Analyze your data efficiently with the most powerful data science stack. Use the Anaconda cloud to store, share, and discover projects and libraries. In Detail: Anaconda is an open-source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting the Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world with ease. Throughout this course, you will learn how to use different packages, with Anaconda to get the best results. You will learn how to efficiently use Conda - the package, dependency, and environment manager for Anaconda. You will also be introduced to several powerful features of Anaconda. You will learn how to build scalable and functionally efficient packages, and how to perform heterogeneous data exploration, distributed computing, and more. You will learn to discover and share packages, notebooks, and environments to increase productivity. You will also learn about Anaconda Accelerate, a feature that can help you to achieve SLAs easily and optimize computational power.
Notes:
Title from resource description page (viewed April 24, 2019).
OCLC:
1056626580

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