If you type learn python into Google, you’ll come back with 427 million search results. Even on our small but growing site, we list more than 500 courses in which you can work on python skills.
Since there does not yet exist a filtering mechanism that tells you the perfect course to take, you’ll likely do a bit of experimenting, especially considering the number of free learning options available today.
But just like watching a new series on Netflix, you should balance giving enough time to evaluate a course’s potential value and giving too much time to something that ultimately won’t help.
Here are three warning signs that can help you make a stop decision more quickly, saving time and effort.
This is our biggest pet peeve. Although we completely understand how challenging it is for instructors to maintain consistency between lecture slides, notes, and code, running into code errors or inconsitencies can be completely debilitating. For example, the lecture slides show variable_name = ‘Tony’, but the prefilled code example starts with variableName = ‘"Tony". You pass in variable_name later in the script after reviewing the slides and your console spits out errors. If this happens only occasionally, it can be a constructive troubleshooting experience. If it happens all the time, it is time to bail.
Making your first histogram with the amount of Barium (Ba) present among a glass sample's oxide content isn’t terribly relatable. Annual stock market returns for the S&P 500 casts a slightly larger net. A better learning example might be average rainfall among the world’s 500 largest cities. If you do come across a dataset with variables that aren’t intuitive, start by looking around for a data dictionary. If you can’t find any additional context — and most of the data examples aren’t that engaging — you might want to try a different course.
Does it seem like the instructor focuses too much on theory? For instance, you’ve learned how to calculate the correlation coefficient between a series of x values and y values from scratch, but still don’t understand how it might apply to your data. Or, more importantly still, you have no clue what you'd do next even if you did identify a negative or positive between your variables?
The best data courses make even the most technical concepts come to life by providing intuition behind a technique and potential use cases for it. If too many concepts are thrown around for which you can’t see how they relate to each other or how they potentially extend to the real-world, you should search for another learning opportunity that will paint a fuller picture.
All three of the warning signs above are especially dangerous if you are just starting out with a new skill or technique because they have the potential to stop you in your tracks.
Education is increasingly experimentation. Although we can’t tell you the exact course that take based on your personal background and aspirations, we can help narrow down available options based on criteria such as learning length, content level, platform, and institution. Find one that seems to meet your needs, give it a trial run, and then pivot to another opportunity if it doesn’t serve your learning objectives.
“There is no end to education. It is not that you read a book, pass an examination, and finish with education. The whole of life, from the moment you are born to the moment you die, is a process of learning.” Jiddu Krishnamurti
Do you have examples of courses that exemplify these warning signs or do a great job avoiding them? If so, drop a link in the comments.