After using Datacamp for a while, I am here with the DataCamp Review for you! DataCamp differs greatly from all the other learning platforms we’ve reviewed here. How, you ask? Read the full review to find out.
A good learning platform plays a key role in learning data and AI skills. Tech learning platforms is a topic that I’ve talked about a lot in this blog. So, why not some more? 😀
In this review you’ll see:
- What Datacamp does well
- What Datacamp doesn’t do well
- My opinion of Datacamp Courses, Workspace, Certification, and other features
- Whether Datacamp is worth the subscription fee
- A whole lot more
Let’s get started.
Career & Skill Tracks
Datacamp has organized the learning into two tracks.
- Career Tracks
- Skill tracks
You can access these from the learn option as show below.
Career tracks are designed for people who want to pursue a career in Data Science and related profiles.
There are 15+ career tracks and 8+ career tracks come with certification. Later in the review, we will look at certification in detail.
Here are some key career tracks available in Datacamp.
- Data Analyst
- Data Scientist
- Data Engineer
- Machine Learning Scientist
Each career track has several courses. For example, I enrolled in the Python Programmer career track and there are 18 courses associated with it.
The track is well-structured with courses starting from very basics to advanced concepts. Also, there are courses that cover shell, and git basics that are necessary for beginners.
In my case, I already know Python basics so I can skip a course by taking the assessment to verify my skill level.
If you are opting for career tracks, If you know the technology you can skip a course by taking an assessment.
Skill tracks help you learn specific technology skills. There are total of 11 skill tacks.
For example, I was interested in MLOps and I chose the MLOps Fundamentals skill track to learn about MLOps.
Each skill track contains multiple courses. For example, MLOps skill track has four courses (almost 14 hours of learning content).
Datacamp has 430+ courses in different topics and technologies. Once you login to Datacamp, you can access these courses as shown below.
The courses are created by over 260 experts from the data science and analytics community who are knowledgeable in data, AI/ML field.
If you want to pick and choose technology stacks, courses are the right option.
Courses are well organized into chapters and subtopics. Each course has video lectures and exercises (quizzes) to deepen our understanding about each topic as shown below.
In my learning experience, doing quizzes on what I learned helped me a lot in retaining the information.
And Datacamp exercises offer quiz approach. It helped me to rethink the concepts I learned through video lectures.
Here is an example of an exercise.
Also, If you want to know about the upcoming courses, you can check the Datacamp course roadmap.
Next up is Datacamp Workspace.
Overall I think Datacamp Workspaces is SUPER good.
That is because, hands-on practice is one of the important aspects of learning data, ML/AI skills.
And it is really hard to switch between the browser and the local lab environment during the leraning process.
Here is where workspaces comes in handy. You can have the lab environment in the datacamp app itself. You can create workspaces for your specific needs.
And while I prefer my own lab setup, Workspaces is still really good during learning.
Also, what I love about the workspace is its integration with generative AI.
You can use GPT 3 and 4 to generate code, get explanations for the code in the learning process.
Here is a demo of the workspace.
(Click the GIF to view in full screen)
The workspace experience is so smooth. You can also also upload your own files to the workspace.
Another great feature of the workspace is Databases. The workspace has 15 sample databases.
Also you can connect to variety of external databases to the workspace. The following image shows the supported databases.
All the data in the workspace gets save and you dont have to create the workspace everytime you login to the datacamp app. It is like having a full fledged Data and AI lab on the browser.
All the content on Datacamp is well organized and you will never get confused over the options.
I primarily used my workstation for learning and course navigation, videos, exercises were so smooth and never faced any glitches.
The user experience of workspaces is also great. The platform is solid and you can perform all the tasks as if you are working in a local system.
You can also access Datacamp through mobile apps. It is available for apple and android devices.
Datacamp pricing starts as low as $12 per month.
Before you gets started with the paid subscription, you can get started with a free account.
With the free account, you will get access to the first chapter of every course for free. You will also get free access to job boards and professional profile.
With free account you will get acess to three workspaces for free. (4GB RAM, 2vCPUs)
If you want unlimited workapaces and Unlimited AI Assistant prompts and more CPU and memory resources (16GB RAM, 8vCPUs), you need to get the premium workspace access for $4.92 per month.
If you want to save money on Datacamp subscription, you can make use of the occassional Datacamp Promotions. You can get up to 50% discount on its subscription plans.
Support & Community
Datacamp has a dedicated support and resource page.
You can submit a report request from this support page as shown below.
Datacamp premium users get access to dedicated slack community. There you can discuss topics or get help from other community members.
You can get access to the community from your profile options.
Datacamp: Things I Like
First, let me cover some of the key things that I found during this Datacamp review.
A Huge Catalog of Courses: Whatever course you expect related to Data, AI/ML, you will find on Datacamp. You will also find courses on DevOps, MLOPS etc
Hands on Projects: Learning with real world projects through workspaces is a great feature of Datacamp.
Also, he user interface is very intuitive.
And there is case studies, Tutorials, code alongs and competitions.
The interview prep feature is great for interview preparation.
So yeah, there are a TON of features provided by Datacamp. Most of them are very good. Some of them need work. But there’s no question that Datacamp is making learning easier and adding new courses and features to its platform.
Datacamp: Things I Dont Like
Course Focus: Datacamp courses are focussed on Data, AI and ML. So if you want to learn other technologies, you need to subscribe to other platforms.
Limited Free Content: With free account you can access only limited free content from courses.
Certificates Not Universally Recognized: The course completion certifications may not help as it is not verified with partnered universities or organizations
The Bottom Line: My Datacamp Review
Datacamp has courses on industry leading Data technologies. That is why it is one of the best online learning platforms for Data and AI related technologies.
Personally I have been going through courses related to MLOPS.
I primarily work on DevOps related technologies and MLOPS is the new path I am involved in and DataCamp courses help me in learning all the related technologies in a well structured way.
Also, the workspace feature lets you learn from anywhere as long as you have a valid Datacamp subscription.
Even though I am using only 10-15% of Datacamp features, I still get enough value out of it to justify my subscription.
You might not get the expected value from Datacamp if you are not in to Data and AI/ML related technologies.
But if Data and AI/ML are big part of your career, I would definitely recommend Datacamp.
If you commit to a skill or career track, you can definitely upskill yourself with hands-on projects.
There you have it: my detailed review of Datacamp.
Now I’d like to turn things over to you:
Have you used Datacamp or any other data learning platform before?
If so, what are your overall impressions? Good? Bad? Somewhere in between?
Let me know your thoughts in the comments section below.