What people say about studying at Chalad Labs
A selection of honest accounts from learners at different stages — what worked, what took longer than expected, and what they've done with the skills since.
Back to HomeFrom learners across Thailand
Pattaravee Thongchai
Bangkok · Intro programme
I had tried two other online coding courses before this one and both of them moved way too fast for me. What I liked here was that the first few weeks genuinely assumed I knew nothing — and that turned out to be exactly what I needed. The feedback on exercises was specific and didn't feel rushed. I finished the guided project in week eight and felt like I actually understood what I'd built.
May 2025
Kirati Sriwichai
Chiang Mai · Data Science
The data science programme covered more ground than I expected — in a good way. I had some Python experience but had never worked with real datasets properly, and the exercises pushed me to actually apply things rather than just follow along. The portfolio piece took me longer than the suggested eight weeks but the mentor was patient about it. The output is something I've since shown in job applications.
April 2025
Wanlaya Lertporn
Nakhon Ratchasima · Intro
I'm a teacher and enrolled during a school holiday period, unsure whether I'd keep up once term started. The recorded sessions made the difference — I could rewatch anything confusing without feeling like I was slowing the class down. Aroon from support responded to a platform question on the same day, in Thai, which I appreciated. Would recommend it to colleagues in the same position.
May 2025
Noppadon Chaiyaporn
Khon Kaen · Advanced AI
I came in having completed the data science programme here and also having read a fair bit independently. The advanced programme was genuinely challenging — the model evaluation section in particular made me rethink some assumptions I'd picked up elsewhere. The career support component helped me put together a portfolio that reflected the work rather than just describing it. I started a new role in April.
April 2025
Siriporn Rattanaphon
Udon Thani · Data Science
Solid programme. The visualisation modules were the most immediately useful for me — I work in market research and I've already used the charting techniques in a client presentation. The machine learning section took me longer to get comfortable with, and I think I'd have benefited from slightly more worked examples there. That said, the mentor's feedback on my project submissions was genuinely useful and very specific.
May 2025
Anuwat Thanapongsathorn
Rayong · Intro programme
I was nervous about starting because I'm 47 and work full-time. The programme page said 4–6 hours a week and that turned out to be accurate — a bit more some weeks, less on others. The content doesn't make you feel stupid for not knowing things already, which matters. I finished it and have signed up for the data science programme now.
May 2025
Learner journeys in more detail
Kirati Sriwichai, 29 — Data Science Essentials
Finance sector, Chiang Mai
Challenge
Working in financial analysis, Kirati was handling large spreadsheets manually and could see that colleagues at other firms were using Python-based tools to do the same work faster. He had no programming background and wasn't sure where to begin.
What he did
He started with the intro programme to build a Python foundation, then moved to Data Science Essentials. The exercises used datasets he found familiar from his day job, which helped. He spent about nine weeks on the portfolio project, using real financial data with anonymised figures.
Outcome
He now runs a weekly automated report at his company using pandas and matplotlib — something that previously took half a day is done in around twenty minutes. He's considering the advanced programme in late 2025.
"The portfolio piece is the part I'm most proud of. It's not a toy project — it's something I actually use."
Noppadon Chaiyaporn, 34 — Advanced AI Development
Software developer, Khon Kaen
Challenge
Noppadon was a backend developer who wanted to move into ML engineering. He had theoretical knowledge from reading but lacked structured practice with model development, evaluation, and the kind of applied project work that would show up well in job applications.
What he did
He joined the advanced programme directly, having completed Data Science Essentials the previous year. The most valuable part for him was the model evaluation and responsible deployment sections — areas he felt he'd underestimated. Career support helped him reframe his portfolio around outcomes rather than techniques.
Outcome
He moved into an ML engineering role in April 2025. The programme took him about five months working around existing commitments. He credits the mentor feedback as the main thing that helped him catch poor habits early.
"I'd read a lot independently before this, but the structured feedback showed me where my reasoning was weak in ways that self-study hadn't."
Have a question before enrolling?
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Nakhon Ratchasima 30000
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