Conda Essentials for Data Scientists
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Chapter 1: Introduction

Course overview
Course outline
Student prerequisites and setup
Meet your instructor
Chapter 2: Getting started with conda

What are conda, Miniconda, and Anaconda?
Choosing between Miniconda and Anaconda
How to install conda
How to access conda
Chapter 3: Managing packages with conda

Listing packages: conda list
Installing packages: conda install
Updating packages: conda update
Removing packages: conda remove
Chapter summary
Chapter 4: Managing environments with conda

Why use conda environments?
Creating environments: conda create
Activating environments: conda activate, conda deactivate
Listing environments: conda env list
Removing environments: conda env remove
More example environments
Chapter summary
Intermission

Can I ask you a quick favor?
Chapter 5: Using conda channels

What are conda channels?
Installing a package from conda-forge
Configuring your channel list
How I use channels
Chapter summary
Chapter 6: Using conda and pip together

Comparing conda, pip, and pipenv
Best practices for using pip with conda
Chapter 7: Advanced conda features

Duplicating environments
Reverting changes to your environment
Renaming your environment
Changing your conda settings
Speeding up package installation
Clearing your conda cache
Conclusion

Can I ask you a quick favor?
What topics did I miss?
Request your certificate of completion
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What are conda, Miniconda, and Anaconda?

Anaconda download page
Anaconda package list
What are conda, Anaconda, and Miniconda? (Data School blog)
Miniconda documentation
Continuum Analytics Officially Becomes Anaconda (Anaconda blog)