From Web Development to Data Science, Wadi's journey
Last summer, Wadi, a former telecoms engineer, joined our web development training to learn how to code. In January 2020 he returned to Le Wagon to add to his skillset and train in data science on our very first bootcamp. Learn all about this super alumni!
Why did you want to train in Data Science after having followed the Web Development bootcamp?
I wanted to do the data science bootcamp for the same reasons that led me to do the web development training. When I did the web development training, I came out of an inconclusive entrepreneurial experience during which the technical dimension escaped me.
Despite my background as an engineer, I had never developed applications or mobile applications for that matter. All the missions that I had until then were oriented around the service and not the product. So I depended too much on my co-founders. The experiment was therefore unsuccessful.
Later, when I tried to improve my skills on the right tools by myself, I came up against an infinity of information on the internet. We find ourselves alone facing our computer, without knowing where to start. This is where I decided to take on Le Wagon.
Why Data Science?
The last job I had before my entrepreneurial break was a business analyst. I was working on a long-standing project for which I had to do a variety of data reporting. This is where I developed a love for "data science".
The bootcamp has demystified a lot of complicated concepts such as Machine Learning, Artificial Intelligence, Neural Networks and so on.
At the time, my favourite tool was Microsoft Excel. I then felt the need to expand my range of tools and find an engineering approach in the profession I practiced.
Why did you choose to join the Le Wagon bootcamp?
Even before the end of the web development bootcamp at Le Wagon, I already had a project I wanted to start that was perfect for a data science bootcamp. So, a month later after my training at Le Wagon, I joined a competing brands' data bootcamp, Le Wagon didn't have one yet. From the start, the educational and organizational shortcomings were felt. Le Wagon having set the bar quite high.
At Le Wagon, there was no distinction of age, sex or background. The diversity made discussions way more enriching.
Two weeks later, Le Wagon announced the opening of its first data training in Paris. I immediately stopped my training in progress and embarked on the Le Wagon one!
How would you describe the teaching team and the community?
The educational team is very often made up of former students and is an integral part of the community. At Le Wagon, there is no distinction of age, sex or background. The variety of routes post-bootcamp makes exchanges enriching and full of good ideas! New perspectives are exchanged during organized events. Sometimes even exciting opportunities.
This is, in my opinion, is what Le Wagon connects the most: the agility of the mind.
Joining Le Wagon also means joining an ecosystem, far beyond a simple network. Having tried a bootcamp where the community was nonexistent, that makes all the difference.
How would you describe the program and your learning experience?
The program is intense and the learning experience all the more so. By doing the bootcamps, beyond the technical skills that we acquire, we return to essential things. We relearn to learn, to look for complex information, absorb new concepts.
Taking advantage of the bootcamp also means enjoying meetings and moments of relaxation.
The program gives a very good representation of data science, and this results in a rewarding learning experience.
Did the program meet your expectations?
Yes! I was able to get a fairly clear overview of what data science is. After the bootcamp I realise that its demystified a lot of complicated concepts such as Machine Learning, Artificial Intelligence, Neural Networks and so on.
The bootcamp is built to cover all of the skills and trades related to data science, which makes it possible to easily identify the positions that you might want to do after graduating.
What did you prefer and find the most difficult in this training?
What I found most difficult in the training was to understand the machinery of the algorithms that we are handling. Understanding mathematical concepts can sometimes be complicated.
When I tried to improve my skills on the right tools by myself, I came up against an infinity of information on the internet. We find ourselves alone facing our computer, without knowing where to start.
What I preferred, however, is the infinite ways data science can be applied to the common good. For example: my end project at Le Wagon, with my group, was to develop a tool for cardiologists to identify whether or not a patient's electrocardiogram signal presented a risk of heart disease. That’s one of my favourite things.
What are your plans after the bootcamp?
The range of possibilities is incredible. I would like to continue this momentum and transform my career! I hope to be able to join a research laboratory in the R&D office in order to really deepen my knowledge of data science.
Any advice for people who want to take the training?
Generally, the advice I would give is to come with a specific objective: to learn a particular skill, to better understand certain concepts etc. This allows you to have a significant feeling of achievement at the end of the bootcamp. Do your research beforehand!
Finally, I recommend having fun! In addition to training, the bootcamp is full of events. Taking advantage of the bootcamp also means enjoying meetings and moments of relaxation, attending tech talks, socials, community events and networking opportunities.