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[tanziyaashaq]

What's the biggest mistake you made when learning Data Science, and what did it teach you?

[v-kathullac · Accepted solution]

Hi @tanziyaashaq,

Thank you for sharing your question. I will keep this thread open for now so that other community members can share their experiences, suggestions, and possible solutions. Hopefully, we can gather some additional insights from others who may have encountered a similar scenario.

One of the biggest mistakes we made when learning Data Science was focusing too much on learning tools and technologies rather than understanding the fundamentals. I spent a lot of time trying to learn Python, SQL, machine learning, and visualization tools simultaneously.

Over time, I realized that having a strong understanding of statistics, data cleaning, problem-solving, and business context is much more important than simply knowing how to use different tools.

The biggest lesson I learned is to focus on fundamentals, practice with real-world datasets, and build projects from end to end. Tools can always be learned when needed, but strong analytical thinking and problem-solving skills are what truly make a difference.

Thanks,

Chaithanya.

[v-kathullac]

Thankyou @Prince0011   for Addressing the issue.

Hi @tanziyaashaq   ,

Thank you for reaching out to Microsoft Fabric Community Forum,

As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided for the issue worked? or Let us know if you need any further assistance?

Regards,

Chaithanya

[v-kathullac]

Thankyou @Prince0011   for Addressing the issue.

Hi @tanziyaashaq   ,

Thank you for reaching out to Microsoft Fabric Community Forum,

As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided for the issue worked? or Let us know if you need any further assistance?

Regards,

Chaithanya

[Prince0011 · Accepted solution]

Biggest lesson I learned in Data Science: stop trying to learn every tool under the sun at once.

Early on, I got overwhelmed jumping between ML algorithms, Deep Learning, and dozens of libraries. It wasn't until I slowed down and focused on the core stuff—SQL, data cleaning, solid statistics, and problem-solving—that things actually started to click.

Don't get stuck in tutorial hell. Pick one core skill, build a small end-to-end project around it, and learn by doing. It saves so much time and builds real confidence way faster.

[v-kathullac · Accepted solution]

Hi @tanziyaashaq,

Thank you for sharing your question. I will keep this thread open for now so that other community members can share their experiences, suggestions, and possible solutions. Hopefully, we can gather some additional insights from others who may have encountered a similar scenario.

One of the biggest mistakes we made when learning Data Science was focusing too much on learning tools and technologies rather than understanding the fundamentals. I spent a lot of time trying to learn Python, SQL, machine learning, and visualization tools simultaneously.

Over time, I realized that having a strong understanding of statistics, data cleaning, problem-solving, and business context is much more important than simply knowing how to use different tools.

The biggest lesson I learned is to focus on fundamentals, practice with real-world datasets, and build projects from end to end. Tools can always be learned when needed, but strong analytical thinking and problem-solving skills are what truly make a difference.

Thanks,

Chaithanya.