Have you ever heard the phrase “You Are What You Read”? Simply put, the information that you consume informs your reasoning pattern and determines your productivity. So choosing the right study buddy or platform is essential and will keep you studying for longer.
As a bonafide bookworm, I’ve had the pleasure of studying on many online platforms. Some have been a total waste of time and subscription fees, while others have been career-changing. However, DataCamp is absolutely among my top 5 interactive learning platforms!
I’ve always been a fan of ‘doing’ while ‘learning,’ so DataCamp’s teaching mode was not new to me. They offer a combination of short videos, informative content, and interactive sessions. You also get to take online assessments and participate in real-life projects.
Why Choose DataCamp?
When I’m super excited about a brand, I tend to spew out garbled information. So please stay with me as I reign in my thoughts on DataCamp’s awesomeness!
1. Career & Skills Tracks
Unlike many other interactive study platforms, DataCamp lets you choose a skill or career track, and the algorithm creates a learning plan that will suit your exact needs.
So if you want a job as a machine learning engineer, you can choose a career track as a Machine Learning Scientist. Your choice will build out a learning portfolio that starts with Supervised and Unsupervised Learning, Linear Classifiers in Python, and so much more.
Choosing a Data Science track will be the best choice for a beginner who is just starting in the industry. The course built by the system will include Basic Python or R and other foundations required to advance in the industry.
The skills track is also a helpful feature because even experienced data scientists can benefit from the courses offered. I’ve used some of the modules as refreshers, especially for EDA (Exploratory Data Analysis) tasks I don’t perform frequently.
2. Leaderboard & Groups
Leaderboards have their upsides and downsides. For example, some learners don’t need the extra pressure of watching others outperform them. On the other hand, some students prefer to track their progress on a Leaderboard.
No matter what you prefer—like it or hate it, Leaderboards do have their uses. They improve learning satisfaction and performance, track results, and introduce gamification into learning, which can be fun!
One of the great things about DataCamp is how modularized the system is. If you’re not interested in the Leaderboard, then you don’t have to join up. It does not affect your learning experience on the platform at all.
3. Skill Level Assessments
Imagine studying relentlessly. Then comes the exam, and your house of cards comes crashing down! So naturally, the first phrase on your mind is: You Know Nothing Jon Snow!
Yep! You thought you knew it all. You thought you had grasped all the fundamental concepts necessary to be the next Andrew Ng or Michael I. Jordan. But you find out at the last minute that you just don’t know enough!
I love assessments and will gladly be tested every day of my career. Why? Because every assessment is a revelation of what I don’t know. Frequently testing your skills reveal the gaps in your knowledge, and with this knowledge comes WISDOM!
4. Self Guided Projects
DataCamp’s interactive learning method guides you through tasks and projects. This type of hands-on learning is great when you’re a newbie. For example, suppose one of the first tasks you’re assigned is to carry out sentiment analysis on a data set or create an unsupervised model to solve a business problem. In that case, you’ll be truly thankful for the experience these self-guided projects offer.
Data scientists need real-world projects to hone their skills, and the self-guided projects on DataCamp offer this option. Thankfully, learning on the job is not a thing anymore. In every field, not just data science, recruitment is now focused on choosing people who can bring value and experience to the job.
As a data scientist, you’re never going to stop learning. Chances are you’ll have to toss a lot of what you already know in a knowledge bin and start all over as new technology and advancements in AI occur.
DataCamp and many other awesome learning platforms make acquiring knowledge easy. So if you want to stay ahead in the industry, continuous learning should be your epithet.
How do data scientists keep learning?
- Go to AI, ML, and data science events – Too many to mention
- Join a community of like-minded data scientists – Kaggle
- Focus on asking the right questions
- Participate in competitions – Kaggle
- Take online courses – DataCamp
- Keep reading books, blogs, and articles – Amazon