Arthur was managing solar projects at EDF when he decided he wanted a change. Not a complete career shift, just a slight reorientation. He wanted to stay in energy, but approach it from a different angle: through data.
“I’d done a bit of Python at engineering school,” Arthur explains. “But I lacked practice. I needed something fast and intensive.”
He didn’t want to go back to university for two years. He wanted three months of focused learning that would get him job-ready quickly. Le Wagon’s Data Science bootcamp fit the bill.
Pôle Emploi funded the entire programme, and within two weeks of approval, Arthur was sitting in class. It felt like a gamble, jumping in without a concrete plan. But the gamble paid off.
“It’s like being dropped in a foreign country and having to learn the language,” Arthur says. “You’re immersed, and you either keep up or drown.”
The Data Science bootcamp covered everything: data analysis, data engineering, deep learning, Docker, APIs. Each week had a theme, building toward a final project that tied it all together.
Arthur’s project focused on energy, letting him combine his old expertise with his new skills. It felt like closing a loop.
“That final project was the highlight,” he recalls. “We applied everything we’d learned over three months to something real.”
The atmosphere at Le Wagon impressed him: after-work drinks, friendly competition, constant collaboration. TAs and teachers were always available, maintaining a ratio of about one instructor per batch to ensure no one got left behind.
Arthur enjoyed it so much he came back as a TA himself, helping the next cohort through the same intensive journey.
“Teaching forces you to understand things better,” Arthur explains. “Plus, I liked staying in the Le Wagon bubble a bit longer.”
Now he works as a data science analyst at EDF, a role that blends analysis with machine learning. He’s not building models from scratch, but he’s running them, interpreting them, applying them to operational problems.
“I use Python every day,” Arthur says. “Querying APIs, building DataFrames, troubleshooting issues. The bootcamp gave me the foundation.”
Pandas became his go-to library, something he uses constantly for data manipulation. He knew the word before Le Wagon, but didn’t really understand its depth.
“By the end, we’d used it so much it became second nature,” he reflects.
Arthur sees the Data Science bootcamp as the starting point, not the finish line. You learn enough to know what you’re talking about, to exchange with experts, to know where to look for answers.
“Three months doesn’t make you an expert,” Arthur admits. “But it makes you competent. And from there, you build through practice.”
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