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):

  1. remote server setup tutorial (How to Install Python, SQL, R and Bash to work in the "cloud")
  2. Python libraries and packages for Data Scientists (list + installation guide)
  3. Anaconda installation video (to get Python and Jupyter Notebooks to your local computer)
  4. Python for Data Science from scratch (tutorial series about Python, pandas, etc.)
  5. Command Line (Bash) from scratch (tutorial series about the basics)
  6. [OPTIONAL] Crontab Tutorial (for automations)
  7. [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.)