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      Labmentix

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      Reviews voor Labmentix

      A great platform for AI and Data Science learning - werkgeversreview Data Analyst bij Labmentix

      4,0
      26 okt 2025
      Data analyst
      Aanbevelen
      Goedkeuring directeur
      Zakelijk vooruitzicht

      Pluspunten

      Labmentix provides a supportive and learning-oriented environment for interns. I worked on real-world AI and Data Science projects that enhanced my technical and analytical skills. The mentors were cooperative, and the training process helped me build confidence in applying machine learning and data visualization techniques effectively.

      Minpunten

      Limited time duration of the internship — extending the program would allow deeper exploration of advanced topics and more collaboration opportunities.

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      5,0
      6 jul 2026
      Intern in ai/ml
      Aanbevelen
      Goedkeuring directeur
      Zakelijk vooruitzicht

      Pluspunten

      Gained practical exposure to real-world projects and industry practices. Improved technical, analytical, and problem-solving skills. Received valuable guidance and support from mentors. Enhanced teamwork, communication, and professional skills. Increased confidence and gained a better understanding of workplace expectations.

      Minpunten

      I have no cons to be listed

      4,0
      16 feb 2026
      Data analyst
      Aanbevelen
      Goedkeuring directeur
      Zakelijk vooruitzicht

      Pluspunten

      Worked on 4 real projects end-to-end (cleaning → analysis → dashboards). Used Excel, SQL, Python, Power BI, Streamlit (great tech exposure). Helped me build a portfolio I can show in interviews. Improved my data cleaning, SQL queries, and visualization skills. Clear learning path and good for beginners. Motivated me to present insights and think like a business analyst.

      Minpunten

      Feedback could be more detailed and faster (especially on submissions). Would help to have more live sessions / Q&A during the program. Sometimes project instructions can be more clear (expected output, rubric). More examples/templates would help for Power BI and Streamlit projects. A bit more guidance on best practices (naming, formatting, documentation).

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