Hey everyone! I’m from a Non-IT background, but I’ve been exploring the world of Data Analytics and Data Science lately. My long-term goal is to become a Data Scientist or work in AI/ML, and I’ve picked up some basics through self-study.

However, I’m confused about where to begin seriously: Some people say I should start with Data Analytics (Excel, SQL, dashboards, etc.) to build a solid foundation, while others suggest I can dive directly into Python, statistics, ML, and modeling even as a non-tech person.

If you’ve taken either of these routes, I’d love your input:

• Does starting with analytics help when transitioning into AI/ML later?

• Or is it better to directly jump into core Data Science concepts if I already know the basics?

• Also, how important are tools like Power BI/Tableau vs learning Python, ML algorithms, and statistics in the early phase?

Bias can enter data through sampling errors, uneven user behavior, external events, or flawed data collection mechanisms. These biases can distort conclusions if left unchecked.

Share a scenario where you discovered subtle but influential bias  like a demographic overrepresentation, seasonal skew, or product usage distortion.

How did you detect it, validate its impact, and adjust your analysis?

Hi everyone,
I’m considering to make a career change into data analysis and recently completed the Google Data Analytics Certificate on Coursera. While it was a solid introduction, I found that it didn’t go very in-depth on tools like SQL, Excel, Tableau, or R.

My Coursera membership has expired. While I am open to signing up again I was curious if there are other websites you would recommend instead? I know its free on Youtube but I prefer a more structure learning course.

Thank you for any help you can provide!

Data analysts are often at the crossroads of numbers and narratives. Every dataset has stories waiting to be discovered, but it’s not just about charts or reports it’s about the insights that drive meaningful change. We want to hear from you: the moments when your analysis revealed something surprising, counterintuitive, or game-changing.

Whether it was spotting a hidden trend, correcting a misconception, or uncovering a customer behavior that reshaped a strategy, your experiences can inspire and teach the community. Share the context, the data challenge, and the insight that made all the difference. Let’s celebrate the power of analysis and the impact data professionals have behind the scenes!

From open-source frameworks like Spark, dbt, or PyTorch to enterprise platforms like Snowflake or Databricks, tools shape the way data professionals work.

But with so many options, the choice of “must-have” tools reveals a lot about priorities: scalability, speed, cost efficiency, or flexibility.

By asking this question, you invite members to share both personal preferences and reasoning behind them.

The discussion not only surfaces new tools for others to explore but also shows how people evaluate technologies based on context  whether they’re working in startups, enterprises, or research labs.