What Sometimes Goes Wrong When Learning Data Science

For you if you want to learn data science sooner rather than later

Data science is an ever-growing field that requires dedication and hard work to master. Sometimes learning data science can feel like an impossible race. With the right mindset and plans you can win that race!

With the right mindset and strategy, anyone can learn data science — but it’s easy to make mistakes if you don’t know what you’re doing. This blog will discuss common missteps on the data science learning journey and provide tips on how to avoid them. We’ll explore big-picture topics such as how learning data science cannot be a linear process and how not to use GitHub as your only portfolio platform.

Before concluding we’ll also discuss a few more pragmatic topics such as setting realistic goals, staying organized, and asking for help when needed. So whether you’re just starting out or already have some experience in data science, this blog can help guide your journey towards becoming a successful practitioner!

Learning Data Science Is Not Linear

The first major common misstep in learning data science is: Trying to learn like you are still in school. You cannot learn data science like you are in school. Most schools teach most subjects in a linear fashion. Try this analogy for example, learning to speak Spanish could you say:

I’m on a mission to discover the language, one letter at a time. From A-words today to B-words tomorrow and eventually Z words in just a few weeks — I’m confident that soon enough I’ll be fluent!

Wrong! Anyone who has learned a new language knows this is not how it works. To become fluent in a language, you need to practice speaking it and make plenty of mistakes, but especially in our modern world you don’t need to worry so much about those mistakes-you can always ask for assistance from Google Translate or more experienced speakers.

You often learn data science by starting with statistics (maybe correlation or regression analysis). Then you find a problem and apply the statistical knowledge to that problem. You make mistakes, ask Google, and others for help, and you learn from your mistakes.