Over the past few months, I had the opportunity to step into a different role: teaching at Le Wagon, one
of the leading bootcamps for web development, data analysis, and data science. While I’m primarily focused
on machine learning and data science in my professional life, teaching opened a whole new perspective for
me. I led sessions on SQL (basic and advanced), Python basics, and introductory machine learning, which was
both a rewarding and challenging experience.
In this blog post, I want to share not only what I taught but also what I learned from this experience, as
it deeply enriched both my technical and interpersonal skills.
1. Breaking Down Complex Concepts for Beginners
Teaching a highly technical subject like SQL or machine learning requires a different approach than working on projects with experienced colleagues. One of the biggest challenges I faced was how to break down complex ideas into digestible chunks. Concepts that are second nature to someone in data science can feel overwhelming to a beginner.
For example, during the SQL basic and advanced lessons, I had to think critically about how to introduce ideas like joins, subqueries, and window functions. To teach effectively, I learned the importance of relating abstract concepts to real-world examples—like showing how SQL can help make sense of a customer database or filter relevant information from large datasets. These examples not only made the material accessible but also resonated with students from diverse backgrounds.
2. Learning by Teaching
There’s a popular saying that the best way to learn is by teaching, and this couldn't have been truer for me. Preparing for these lessons required me to go back to the basics and reflect on the why behind certain approaches.
While teaching Python basics, I had to remember how challenging it was to grasp concepts like variables, loops, and functions when I was first learning. Explaining these in simple terms helped me reinforce my own understanding, and in many cases, I gained a new appreciation for the elegance of the language. Similarly, teaching machine learning basics pushed me to rethink how I approached introductory topics like linear regression, classification, and model evaluation. Simplifying complex algorithms and building a foundational understanding of supervised learning helped me refine my own communication and technical skills.
3. The Power of Asking Questions
Another key takeaway from my experience was the importance of fostering an open and interactive classroom environment. Students in bootcamps often come from diverse, non-technical backgrounds, so they aren't afraid to ask questions that might seem obvious to someone experienced in the field. This diversity of thought enriched the learning experience for both them and me.
Some of the most insightful moments came when students challenged certain methods or asked, “Why do we do it this way?” These questions made me think critically about default practices in data science and machine learning, and even encouraged me to experiment with different approaches.
4. Seeing Students Progress
One of the most rewarding aspects of teaching was witnessing the students’ growth. Many came in with little to no coding experience, and by the end of the bootcamp, they were able to write SQL queries, manipulate data with Python, and even implement basic machine learning models.
Seeing their confidence grow as they grasped concepts, debugged code, and solved problems was incredibly fulfilling. It was a reminder of how transformative education can be, especially in a fast-paced, hands-on environment like Le Wagon.
Final Thoughts: Teaching as a Continuous Learning Journey
My time teaching at Le Wagon has been a valuable experience, both professionally and personally. Not only did I get to share my knowledge and contribute to the growth of future data scientists, but I also developed my ability to communicate complex ideas, deepened my own technical understanding, and gained a new appreciation for the diverse ways people learn.
As an ML Engineer, teaching has reminded me of the importance of continuous learning—whether through formal education, sharing knowledge, or simply taking the time to revisit the basics.
Teaching at Le Wagon has been a great opportunity to step back and rethink how I approach problems, how I communicate with others, and how I can make a meaningful impact. I look forward to applying these insights in my work and hopefully continuing to teach and inspire others on their data science journeys.