Datacamp Data Science with Python Course Review

I’ve recently completed the Datacamp Data Scientist with Python Career Track and wanted to provide a review of my learning experience and how prepared I feel about working in Data Science going forward.

Previous Coding Experience

I started learning Python a couple of years ago for about a year. Since then I forgot most of the syntax but knew how to get set up pretty quickly so I wasn’t a complete beginner. I had written some code in the past but I wouldn’t consider myself anywhere near “Intermediate” as I don’t have any knowledge of advanced topics such as Object Oriented Programming, Decorators, Data Structures or experience writing complete applications.

I felt I needed this course to really give Data Science a good shot before deciding if this career path was still a viable option. So I purchased the annual subscription and committed to doing an hour a weekday of studying within Datacamp. I was able to finish within 6 months.

Course Learning Style

I was expecting a similar interactive experience to the likes of Codecademy where you are given interactive exercises with gamified feedback in the form of scores and point systems. I did not expect how much it would be like a course from Edx or Coursera where you would need to watch lecture videos in between tasks. Fortunately these videos were only 3–5 minutes long. And I would say the basic structure of each module were 5 minute videos in between 10–15 minute long interactive exercises.

Did the course meet my expectations?

Given I have zero experience with Calculus or Statistics, I was expecting this to be challenging and I definitely struggled with the more technical modules. But even when I could not grasp the intuition of concepts I was able to complete modules. Overall the content was challenging but the criteria to complete the exercises was not. There were too many get-out-of-jail-free passes within exercises that allowed you to complete modules without much effort.

By the end of Career track, I felt I was simply going through the motions of filling in coding fields without ever improving my intuition for Python programming or Machine Learning concepts.

How confident do I feel about pursuing Data Science going forward?

I would not apply for any Python or Data Science roles off the back of this course. I need practical experience building projects myself which I would put into an online portfolio of some kind. This is probably the case for 99% of online courses.

It isn’t until you apply your knowledge do you really internalise what you’ve learned. But I do feel this Career track course hasn’t helped me develop the level of intuition to even start building Machine Learning projects on my own.

Would I pay the same amount for what I learned?

I bought the Datacamp Annual Subscription in January at half price and have managed to complete the Data Scientist with Python Career Track in 6 months after 100+ hours. Even though I am not completely satisfied with the Data Science track, there’s a lot of useful projects to borrow code from, cheat sheets to download and courses on niche subjects that you won’t find elsewhere. With 6 months to go, I believe I will get my money’s worth by using the projects as templates to build my portfolio on.

Compared to Dataquest($33) or Codecademy($22), the annual subscription offers more features, a bigger library of projects and data related courses at a better price. Now with it’s Workspace feature, it could be the best place to build projects outside of Kaggle.

Still, if Dataquest has the exact courses you need. I would choose Dataquest. I personally chose Datacamp because it gave me access to both SQL and Python courses whereas Dataquest doesn’t offer this and many other features which Datacamp has.

Why Datacamp is good for beginners

No set-up needed

More online courses are providing the interactive shells to begin coding immediately but it is always something to highlight for beginners. Nothing makes a beginner stop learning than cumbersome debugging when they haven’t even got their feet wet. Datacamp has definitely put a lot of effort into making the learning experience smooth and pleasant.

Building momentum through gamification

I have completed courses with Codecademy and Khan Academy so I know a good gamification learning experience when I see one. It isn’t too over the top like Khan Academy but still provides that consistent nudge to maintain your learning streak. These small prompts are vital in building momentum in your coding journey.

Structured courses and tracks to keep you focused

Learning from building projects yourself is the ultimate learning experience in order to develop practical expertise. Still, an underrated part of online courses is the structure.They did a really good job of laying out course syllabuses on multiple levels so you can see what was involved and how much progress you were making. A good course structure allows you to spend minimal time getting distracted and makes your learning efficient and frictionless. Every morning I knew how much progress I could get done and how. This allowed me to stay focused on building my daily ritual learning through Datacamp.

Why Datacamp isn’t so good for intermediate learners

Problem and the solution is mapped out for you.

The longer I stayed on the Data Science with Python Career track, the more it became evident that everything was too mapped out for me. Datacamp’s method of pre-writing 80% of the code only for you to fill in the last 20% is nice at first until you realise it is taking away from the learning experience. By removing most of the effort of the exercise you do not internalise the concepts and you forget easily. As an intermediate coder, I felt my hand was being held too strongly.

Too much focus on memorising Syntax in passing exercises

There were many courses that focused too much on syntax memorisation instead of how to solve data science problems. The exercises rarely tested my intuition and problem solving skills but rather how much I remembered from the 5 minute presentation I had just watched.

Scoring is too lenient

The points system is good at providing additional encouragement but it’s too lenient. I believe Khan Academy has a better points system where you have to earn the right to advance onto the next module. Whereas Datacamp lets you accumulate points by simply participating in exercises, revealing answers to yourself and clicking through videos.

Who should invest in a Datacamp subscription?

Beginners will get a lot of value from the smooth, seamless experience of Datacamp and abundance of courses across SQL, Python and R. Also the mobile app, projects, practice exercises and assessments provide multiple ways to learn everyday. Being able to code or learn even a little bit everyday is vital for building your competence as a data professional.

This is where I believe the true value of Datacamp is. It is for data professionals looking for a platform to aid them in bolstering their Data Portfolio. There’s a lot of well structured code here to build projects from. You can download notebooks of completed projects, cheat sheets on subject areas and presentation slides. The value isn’t really in the working through the courses but the platform of templates and foundational code to build projects off of.