We live in an incredible time for learning new skills. If you have a device and an internet connection, you can access a huge array of online courses, textbooks, and tutorials on virtually any topic. Data science is no exception.

In today’s environment, the hard part is narrowing down which platform and format to use. This challenge is why we built DataKwery in the first place. 

There are now more than 1,700 learning resources available in our catalog. In 2022, we will continue to add new search features for you to further narrow down the best path across a variety of decision factors, including price, which is clearly a leading driver in course selection. Thankfully, 47 percent of the offerings in the current catalog are entirely free. But is free better?

Like many things, it depends.

4 reasons to choose a free online data course

1. Price: Economists say there is no such thing as a free lunch. That’s true, but aside from time, these courses won’t cost you anything.
2. Experimentation: Because these courses are free, switching costs are nearly non-existent. This gives you the ability to experiment and “fail fast” by dropping courses that aren’t helping.
3. Brands: Despite the price tag, you still have access to incredible content made available from the world’s leading universities, business schools, and companies.
4. Peers: If you like learning with friends and family, it is a lot easy to convince them to sign up with you for free courses when compared with paid alternatives.

6 reasons to pay for an online data course

1. Content consistency: Paid courses, especially within subscription plans such as those offered by DataCamp and others, tend to have a similar look and feel across courses. This consistency facilitates learning, enabling you to more easily make connections as your progress through a learning path.
2. Interactive tools: One potential headache with learning data online is configuring tools such as Python or R to align with the course content. This is especially true as packages and software versions tend to change quickly. Many paid courses have these tools integrated directly into the learning platform, allowing you to focus purely on the concepts by reducing troubleshooting time. (On the other hand, troubleshooting can be a good practical experience!)
3. Support: Because you are a paying customer, there is a stronger incentive to keep you happy as a user. This means that there are more support services for when you run into problems that can't be answered by community-managed message boards.
4. Recency: Paid services are also more likely to update content and code as technology changes. This is great because no one wants to start a statistical programming course in R, for example, that was made in 2014 before the tidyverse even existed! 
5. Validation: This may or may not matter depending on your goals. However, if you need or want to signal your accomplishment to colleagues and potential employers, most platforms now offer certificates of completion exclusively for paying learners.
6. Careers: Speaking of employers, paid online learning platforms are now working harder to connect learners with job opportunities. DataCamp, for example, now offers career services for those who get certified as Data Scientists or Data Analysts.

The online learning spectrum in 2022

Although there are exceptions within each platform, the major online learning services broadly fall into the following categories:

The face of free online learning is changing

Just because there are more reasons listed above to pay for an online course doesn't mean that is always the best decision. In fact, as the free vs. paid learning spectrum becomes less clear, most paid providers either offer some free courses or allow learners to start a course for free. 

In addition, many of the historically free platforms are finding ways to monetize. This includes several of the original Massive Open Online Learning (MOOC) companies, who are now balancing their education missions with growing commercial pressures. As a result, the look and feel of free courses will undoubtedly change in the coming years with a greater emphasis on converting free learners into paying customers who (hopefully) will benefit from better content, interaction, and assessment. 

This is not necessarily a bad thing. These paid courses, certificates, and micro degrees will remain a fraction of the cost of higher education and — at least in a field such as data science — boast learning outcomes with the potential to rival traditional approaches.

So, should you pay for an online data course? 

We think the answer to that depends on your background, your timeline, and the specific tools and techniques that you are trying to learn. 

Want to learn the basics of how to code in python? Start with something free from FreeCodeCamp or Coursera. If following along becomes too challenging due to maintaining your own code environment and data connections, give DataCamp a trial run to see if their integrated coding tools help out. 

Are you convinced you want to be a data scientist and don’t want to waste time experimenting with random free courses? Then maybe it is worth the investment to start a dedicated career track with DataCamp or Codecademy by picking up a subscription and progressing down a proven learning sequence.

Free or bust?

Still not sold on paying money to learn data science? No worries, our data scientists have curated career-focused learning paths by aggregating the best free courses from the most trusted providers. Have a look below!

Best wishes on your data journey in 2022 and beyond.