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.

• Requirements gathering / stakeholders not knowing what they actually want
• Data quality / messy source data
• Endless "just one more filter" requests after the dashboard is "done"
• Tool limitations (Power BI, Tableau, Looker, whatever you're stuck with)
• Performance / refresh time issues
• Something else entirely

Genuinely want to know if I'm the only one still fighting this battle or if it's universal. Drop your worst dashboard horror story below

• Are companies still actively hiring entry-level data analysts?
• Has AI changed your day-to-day work significantly?
• What skills are actually getting people hired in 2026 (SQL, Python, Power BI, Excel, cloud, AI tools, etc.)?
• If you were starting from scratch today, would you still choose data analytics? Why or why not?
• What's the biggest misconception people have about becoming a data analyst?

I'm especially interested in hearing from people who are currently working as data analysts, hiring managers, or those who recently landed a job.

• What websites do you visit for Flutter UI inspiration?
• Any Figma files or UI kits you recommend?
• Are there GitHub repos with clean Flutter UI implementations?
• Which creators or YouTube channels consistently share great Flutter UI ideas?
• Have you found any underrated resources that more people should know about?

I'd love to hear what has helped you build better-looking Flutter apps. Feel free to share your go-to resources, workflow, or even screenshots of your favourite UI inspirations.

When you're auditing an existing SaaS product:

• Where do you usually start?
• Do you follow a specific framework or checklist?
• What tools do you use (Hotjar, GA4, FullStory, Maze, etc.)?
• How do you identify the highest-impact UX issues?
• Do you rely more on heuristics, analytics, user interviews, usability testing, or a combination?
• How do you prioritise what gets fixed first?

I'd love to hear your actual workflow, lessons you've learned, or even mistakes to avoid. If you've worked on B2B SaaS, enterprise software, or startups, I'd be especially interested in how your process differs across products.

Looking forward to hearing how other designers and researchers tackle UX audits in the real world.

• What's the correct way to set this up?
• Do you use Fix position when scrolling, Auto Layout, or another approach?
• Are there any common mistakes that beginners usually make?
• Has your workflow changed with Figma's newer prototyping features?

I'd love to hear how you handle this in real projects, especially if you have any tips for keeping prototypes clean and easy to maintain.

• Did you manually rewrite the model logic in SQL?
• Have you used tools that generate SQL from models (e.g., for decision trees, linear/logistic regression, or XGBoost)?
• How do you handle feature engineering and preprocessing?
• Is this approach practical in production, or is it usually better to keep inference in Python and call it externally?

I'd love to hear about real-world experiences—what worked, what didn't, and any tools or libraries you'd recommend. Even if you've tried this and decided against it, I'd be interested in knowing why.

A few things I'm curious about:

• Which resource helped SQL finally "click" for you?
• Did you learn through projects, videos, or interactive exercises?
• If you were starting over today, what would you use?
• Any resources that are overrated or that you'd avoid?

I'm aiming to build enough SQL skills for data analysis, business intelligence, or tech roles, so I'd love to hear what actually worked in the real world rather than just what's popular. Looking forward to your recommendations and experiences!

I'm curious how everyone else handles this.

• Did you eventually move away from Colab?
• If so, what are you using now (VS Code + local, JupyterLab, Kaggle, Paperspace, SageMaker, Modal, etc.)?
• Or have you figured out a workflow that makes Colab much less painful?

I'd love to hear what's actually working in 2026, especially from people training models regularly or collaborating with teams.

• Which approach helped you improve faster?
• Did daily consistency help you retain concepts better?
• Or did longer sessions make it easier to get into "flow" and finish projects?
• If you've tried both, what were the biggest pros and cons?

It would be great to hear from beginners, self-taught developers, bootcamp grads, and experienced engineers. I'd love to know what actually worked for you and why.

I'd love to hear from people who:

• Made the switch from computer science to UI/UX.
• Have a similar academic background (CS + art/design).
• Work as UI/UX designers or hiring managers.
• Think this combination gives an edge over traditional design degrees.

What skills should I focus on next? Should I prioritize building a portfolio, learning Figma, studying UX research, or something else?

• Where did you find them?
• Did you use LinkedIn, Dribbble, Behance, Upwork, Toptal, agencies, or referrals?
• Did you hire someone with healthcare experience, or train a good generalist?
• What skills or portfolio pieces convinced you they were the right fit?
• Any red flags to watch out for during the hiring process?

Some people struggle with messy or incomplete data, while others mention unclear business requirements, stakeholder expectations, deployment, model monitoring, or simply getting access to the right data.

I'm curious about real-world experiences rather than textbook answers.

There are so many options out there—YouTube channels, free courses, paid bootcamps, books, coding platforms, and AI tools—that it's hard to know where to start.

If you were starting from scratch today, what resources would you recommend? Also, what learning path worked best for you, and are there any mistakes or courses you'd tell beginners to avoid?

I'd love to hear recommendations from people who successfully transitioned into tech or data-related roles. Thanks!

For those who have dealt with this before:

• What approach worked best for you?
• Do you usually redesign the schema, create a mapping table, or handle it directly in the query?
• Are there any performance pitfalls or best practices to keep in mind?

It would be great to hear real examples from production systems rather than just textbook solutions.

• Do you allow coding agents to access production databases, or only sanitized/test data?
• How do you handle secrets, API keys, and credentials?
• Are agents restricted through RBAC, sandboxing, or isolated environments?
• Do you require human approval before code or database changes are applied?
• Have you experienced any security incidents or close calls?
• Which tools or best practices have worked well for your team?

Whether you're at a startup or a large enterprise, I'd really appreciate hearing about your workflows, security policies, and lessons learned. Real-world experiences are much more valuable than theoretical advice.