Prerequisites (recommended environments and languages)
If you’re taking this course, you are probably not an absolute beginner in data science. I'll assume that:
- you are confident with the basics of Python (for loops, if statements, functions, imports, etc.)
- you have some very basic command line (bash) skills and
- you have a data science environment to work with
If so, feel free to use your favorite environments and tools.
But, just in case, here’s a list of my recommendations, favorite tools and the environment I usually use in real data science projects (and in this course):
- remote server setup tutorial (How to Install Python, SQL, R and Bash to work in the "cloud")
- Python libraries and packages for Data Scientists (list + installation guide)
- Anaconda installation video (to get Python and Jupyter Notebooks to your local computer)
- Python for Data Science from scratch (tutorial series about Python, pandas, etc.)
- Command Line (Bash) from scratch (tutorial series about the basics)
- [OPTIONAL] Crontab Tutorial (for automations)
- [OPTIONAL] How to Upload your Dataset to a Server (Using the Command Line or Jupyter)
And finally, let me recommend another online course of mine (if you haven't taken that already): The Junior Data Scientist's First Month course. It's not a "prerequisite" for this course. But it's a simulation of a junior data scientist's first month at a true-to-life online company -- so it's a perfect fastlane to make you very confident with all the tools that I mentioned above (Python, bash, remote servers, automations, etc.)