Que faites-vous si votre stage en analyse de données manque d’expérience pratique ?

If your data analysis internship lacks hands-on experience, talk to your supervisor about your desire for more practical tasks. Volunteer for additional projects that provide hands-on work. Additionally, take initiative by working on independent projects outside of internship hours, such as analyzing publicly available datasets or participating in data analysis competitions on platforms like Kaggle. Enhance your skills through online courses and tutorials to bridge any gaps. Networking with colleagues and seeking mentorship can also provide insights and opportunities for more practical experience.

If your data analysis internship feels like you're just watching from the sidelines, it's time to take charge. Start by chatting with your supervisor about getting involved in real projects. Offer to help with specific tasks or ask if you can take on a small part of a larger project. Try online platforms like Kaggle, where you can find tons of datasets and exercises to hone your skills. Or i would advice you get creative with your knowledge. Create your own mini-project? Look for issues within the company and propose a data-driven solution. This shows initiative and gives you practical experience. Seek out a mentor in your workplace—a seasoned data analyst who can offer advice and feedback on your work.

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I would sit with my intern and show them a data-set I am currently working on and explain each of my steps when it comes to organizing the data; normalizing the data; running statistical analysis; and lastly, interpreting the data and how it proves, or disproves, my hypothesis. Secondly, I would present my intern with a second data-set that I've previously analyzed and I would ask them to analyze it and interpret it to the best of their understanding. I believe in the philosophy of "see one, do one, and teach one."

I always recommend creating a learning path for the topic you want to improve. In this case, when it comes to data analysis, I would say it is important to learn the concepts of data modeling, Power Query, DAX, and how to build and design reports.

Na minha opinião, trabalhar em projetos pessoais é uma excelente maneira de ganhar experiência prática. Identifique problemas ou questões de interesse e use dados disponíveis publicamente para realizar análises.

Personal projects are a great way to gain practical experience in an internship program. Here are a few things I have tried when I was an intern and I wasn't allocated any projects to work on - I did a poll to figure out top data challenges faced by the organization - The persistent issue was the lack of a centralised data repository and a dashboard for all impact data. - I was able to deliver a very basic version of the same and it was an addition to my portfolio You may try the following if your organization isn't offering you any data projects 1- Check with any non - tech team members and understand the problems they are facing 2- Investigate the core issue 3- Design a basic prototype 4- Ask the team to test.

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Engage in Self-Initiated Learning Online Courses and Tutorials. Utilize platforms like Coursera, edX, or Udemy to take courses in data analysis, programming languages (like Python or R), and specific tools (like SQL, Tableau, or Power BI). Work on personal data projects that interest you. This could include analyzing publicly available datasets on topics you are passionate about and showcasing your findings.

Study similar cases to learn Search, compare and analyze similar operational and field experiences Using useful tips to simulate operational conditions to face at the right time

Study similar cases to learn Search, compare and analyze similar operational and field experiences Using useful tips to simulate operational conditions to face at the right time

Study similar cases to learn Search, compare and analyze similar operational and field experiences Using useful tips to simulate operational conditions to face at the right time

Study similar cases to learn Search, compare and analyze similar operational and field experiences Using useful tips to simulate operational conditions to face at the right time

In the absence of practical experience during your data analysis internship, cultivate curiosity. Ask questions, seek new learning opportunities, and explore diverse datasets and methodologies. Embrace a mindset of continuous learning to enhance your skills and adaptability in the field of data analysis.

Sometimes it may happen that the purpose for which a candidate joins a company for an internship fails entirely. I have heard many instances where candidates did not learn anything in terms of solving real world cases and instead they were mainly utilized for administrative work. I am not saying administrative work is not important, it surely is. But for cases like tech companies even if it is a start up, as a fresh engineer you need to gather knowledge of applying the concepts or at least learn from senior supervisor regarding the process. Now if the intern is assigned to do paper work instead of technical staff which should have been the ideal thing to do as he or she is an engineering intern, then it does not add value to both parties👍

Study similar cases to learn Search, compare and analyze similar operational and field experiences Using useful tips to simulate operational conditions to face at the right time