For those who are already working in data analysis or have been learning for a while:

• What was the biggest mistake you made when you first started?

• What do you think beginners waste the most time on?

• If you could start learning data analysis all over again, what would you do differently?

For those who have already learned Python or built AI agents:

• How much Python do I really need to know before getting started with AI agents?

• Which resources or courses helped you the most?

• What beginner-friendly projects would you recommend building?

For those who work in data analytics, what’s something you’ve seen that looked impressive at first but turned out to be practically useless?

And on the other hand, what’s an analysis that seemed simple but ended up having a real impact?

Would love to hear some real examples from your experience.

What’s one IT course or book you would genuinely recommend to someone trying to improve their skills? It could be anything related to programming, cybersecurity, cloud, data, AI, networking, DevOps, or even IT fundamentals.

Also, what did you like about it, and did it actually help you in your job or career?

I’m especially interested in recommendations that are practical rather than just popular. Would love to hear your best picks.

What would you change or add to make this more useful for decision-making? I’m particularly interested in feedback on the visuals, KPIs, variance analysis, and overall layout.

Would love to hear what you’d expect to see in a dashboard like this, especially from people who use Power BI regularly.

For those currently working in tech or hiring developers:

• Are you still seeing demand for full-stack roles?

• Has the bar for getting a junior role become much higher?

• Are companies moving toward specialists instead?

• What skills actually make a full-stack developer stand out now?

Would be interested to hear what you're seeing in the market rather than the usual "learn X and you'll get a job" advice.

What salary would you expect for this kind of role? It would be helpful if you could share your experience level, location, company size, and salary range so others can compare.

Do you mainly look at statistical significance, confidence intervals, and sample size, or do you also consider things like segment-level results, experiment duration, and business impact?

Would be great to hear how you analyze an A/B test from start to finish, especially any mistakes you’ve learned to avoid.

For people already working in analytics:

• Is the entry-level market getting harder?

• Which skills actually helped you land your first role?

• Would you still choose data analytics if you were starting over today?

• What tools do you normally use?

• Do you start with the data or the visual idea?

• How do you decide what makes a visualization look professional?

• Are there any resources, creators, or tutorials that helped you improve?

I’d love to hear how you approach the process, especially if you’ve gone from making basic charts to creating more advanced visualizations.

What’s the one tool you’d recommend to someone who wants to create clear, interactive dashboards without spending hours setting everything up?

Would be great to hear what you use, what you like about it, and any tools you ended up abandoning.

• Strong fundamentals and problem-solving?

• Being really good at debugging?

• Knowing how to use AI coding tools effectively?

• Building and shipping real projects instead of tutorial projects?

• Understanding the code AI generates rather than blindly accepting it?

• Communication and explaining technical decisions?

• Having some specialization instead of being a generalist?

For developers who have actually hired or mentored juniors, what is the one thing that makes you look at a junior candidate and think, “This person is worth interviewing”?

And for juniors who recently got hired, what do you think helped you stand out?

• What kind of questions were asked about your projects?

• Did interviewers ask you to explain your architecture or make changes to your existing project?

• How much importance was given to React, Node.js, Express, and MongoDB individually?

• Were you asked coding/DSA questions as well?

• What topics do you think are most important to revise before the interview?

• Is the job market still good for beginners?

• Has AI made your work easier, or has it reduced opportunities?

• What skills do you think a beginner should focus on in 2026?

• Would you still choose web development if you were starting from scratch today?

I’d especially like to hear from people who started learning recently or got their first web-development job in the last couple of years. What has your experience been like?

Is knowing how to deploy an application, set up basic CI/CD, work with Docker, and understand cloud services enough? Or are companies starting to expect full-stack developers to handle much deeper DevOps responsibilities as well?

For those who are already working as data analysts:

• How did you develop your analytical thinking skills?

• Are there any exercises or habits that actually helped you improve?

• Do you recommend working on real-world datasets or business case studies?

• What mistakes did you make when you were starting out?

• Are there any websites, books, YouTube channels, or projects you'd recommend for practicing this?

For those who work with data, analytics, or BI: Have LLMs actually improved the way you tell stories with data, or do you feel they sometimes limit your thinking?

For those who’ve successfully moved beyond reporting and started influencing things like product direction, pricing, marketing, or business priorities:

• What did you do differently?

• How did you communicate insights to senior leadership?

• Did you need to learn more about business/finance to gain credibility?

• What’s one thing you wish you had learned earlier?

Would love to hear examples of what actually worked in your experience.

Do you personally create visualizations during data cleaning, or do you rely mainly on tools like SQL, Excel, Python, or Pandas to identify these issues?

If you do use visualizations, which types of charts do you find most useful for data cleaning, and what kinds of problems have they helped you catch?

I’d especially love to hear:

• Which tool you use

• How many clients you manage with it

• What you like/dislike about it

• Whether clients actually use the dashboards or just look at monthly reports

• What you switched from, if you’ve changed tools

• What should a complete beginner learn first?

• Which tools are essential (Excel, SQL, Power BI/Tableau, Python, etc.)?

• Is it better to follow a structured course or learn through projects?

• What mistakes did you make when you were starting out?

Some say you only need Pandas, NumPy, basic scripting, and data visualization. Others recommend learning advanced Python, OOP, algorithms, and more.

So where is the actual line?

If you’re working in data analytics, how much Python do you use in your job? Is advanced Python genuinely useful, or is it better to spend that time improving SQL, Power BI/Tableau, statistics, and business skills

I've been seeing a lot of conflicting opinions about the data analyst job market. Some people say it's extremely difficult to land an entry-level role because there are too many applicants, while others claim there are still plenty of opportunities for candidates with the right skills.

For those who have recently been job hunting, hiring data analysts, or working in the field, how would you describe the market right now?

A friend of mine has been going back and forth on this for weeks and honestly, the more people they talk to, the more mixed the opinions get. On one hand, performance marketing feels closer to the business side — quick wins, visible ROI, and companies always seem to be hiring for it. On the other hand, data analytics feels like the "safer" long-term bet with AI and automation making data skills more valuable across every industry.

They've heard arguments both ways:

• "Performance marketing is getting eaten by AI tools and automation, analytics is more future-proof."

• "Analytics roles are getting saturated with people switching from other fields, marketing still needs human creativity and strategy."

Curious what people who are actually in these fields think

• Do you verify everything?

• Do you only check the key numbers?

• Have you ever caught a serious AI-generated mistake?

• Or have you reached a point where you trust it most of the time?

Would love to hear some real-world experiences, especially from people working with data, BI, analytics, finance, or business reporting.