Explore the basics of Python Environment in Business Analytics with Python | This introductory course will help you to create and manage separate virtual environments for your Python projects | Earn recognized Certification, on completion of the course
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This course will help you with the basics of Python Environment. Learn about Anaconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects, & more.
Know the complete offerings of our Basics of Python Environment
This module will help the candidate to gain knowledge about the Python Environment. Learn about Anaconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects
Know the complete offerings of our Business Analytics with Python
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On completion of the Basics of Python Envronment in Business Analytics with Python Course, earn Henry Harvin® Certification. The Certification is a proof that you have taken a big leap in mastering the domain.
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A virtual environment is a Python tool for dependency management and project isolation. They allow Python site packages (third party libraries) to be installed locally in an isolated directory for a particular project, as opposed to being installed globally (i.e. as part of a system-wide Python).
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The videos are created by industry expert mentors keeping all types of learners in mind. The course is well structured for the beginners.
This is a self-paced course and you can do it at your own convenience.
A conda environment is a directory that contains a specific collection of conda packages that you have installed. For example, you may have one environment with NumPy 1.7 and its dependencies, and another environment with NumPy 1.6 for legacy testing.
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