Member | Pangaea X Community

If AI can write SQL and build reports, what should junior data analysts focus on learning?

AI tools can now help with SQL queries, data cleaning, visualizations, and report generation. That changes the learning curve for people entering analytics, but it doesn’t necessarily remove the need to understand the data or the business problem. I’m curious how experienced analysts would approach this today. Would you prioritize SQL fundamentals, Python, business understanding,(Read More)

AI tools can now help with SQL queries, data cleaning, visualizations, and report generation. That changes the learning curve for people entering analytics, but it doesn’t necessarily remove the need to understand the data or the business problem. I’m curious how experienced analysts would approach this today. Would you prioritize SQL fundamentals, Python, business understanding, data modeling, or learning to validate AI-generated analysis?
If you were mentoring a junior analyst starting in 2026, which three skills would you ask them to develop first, and why?

0 4 71 2 weeks ago

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Sameena

October 14, 2026

Are Data Scientists Becoming AI Supervisors?

With the rapid adoption of agentic AI, automated feature engineering, AutoML, and AI-assisted analytics, I’m starting to wonder whether the role of a data scientist is changing faster than many expected. Tasks that once required hours of manual work—data cleaning, exploratory analysis, feature selection, model tuning, and even insight generation—can now be partially automated by(Read More)

With the rapid adoption of agentic AI, automated feature engineering, AutoML, and AI-assisted analytics, I’m starting to wonder whether the role of a data scientist is changing faster than many expected.

Tasks that once required hours of manual work—data cleaning, exploratory analysis, feature selection, model tuning, and even insight generation—can now be partially automated by AI systems.

A recent trend highlighted by industry leaders and platforms like Databricks, OpenAI, and Snowflake suggests that data professionals may spend less time building models and more time validating outputs, governing AI systems, and translating results into business decisions.

Does this mean the future data scientist will look more like an AI supervisor and strategist than a traditional model builder?

Or do you think deep statistical and machine learning expertise will remain the primary differentiator despite advances in AI tooling?

Curious to hear how others see the role evolving over the next few years.

0 3 149 2 months ago

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Sameena

September 9, 2026

Are we trying to use RAG for problems that should be solved with traditional tools?

I recently came across a discussion about using a Retrieval-Augmented Generation (RAG) system to audit CAD files (STEP/OBJ) for geometry issues, missing features, and manufacturing errors. It made me wonder whether we’re sometimes reaching for LLMs when deterministic tools might be a better fit. CAD validation already has established approaches: Geometry kernels Rule-based checks Mesh(Read More)

I recently came across a discussion about using a Retrieval-Augmented Generation (RAG) system to audit CAD files (STEP/OBJ) for geometry issues, missing features, and manufacturing errors.

It made me wonder whether we’re sometimes reaching for LLMs when deterministic tools might be a better fit.

CAD validation already has established approaches:

Geometry kernels
Rule-based checks
Mesh validation algorithms
Manufacturing and tolerance analysis tools

A RAG system could potentially help explain issues, summarize findings, or assist engineers in navigating documentation. But can it reliably detect errors in complex 3D models, or does that stretch beyond what RAG was designed for?

I’m curious how others draw the line between:

Problems that benefit from LLMs and retrieval systems
Problems that are fundamentally better handled by traditional software engineering and domain-specific algorithms

Have you encountered a project where AI initially seemed like the right solution, but a conventional approach turned out to be more accurate, scalable, or maintainable?

0 3 122 2 months ago

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Sameena

August 17, 2026

Which data science bootcamps or courses are worth it for career changers over 35?

Hello! I’m considering switching to a career in data science and wanted to get advice from the community. I’m in my late 30s and looking for programs that provide practical experience in Python, machine learning, and data analytics. Are there any bootcamps or online courses that stand out for career changers? Preferably ones that: Offer(Read More)

Hello! I’m considering switching to a career in data science and wanted to get advice from the community. I’m in my late 30s and looking for programs that provide practical experience in Python, machine learning, and data analytics.

Are there any bootcamps or online courses that stand out for career changers? Preferably ones that:

Offer hands-on projects
Are affordable
Provide support in Spanish or with subtitles

Any experiences or recommendations would be really helpful!

0 1 211 4 months ago

Subscriber

Sameena

June 29, 2026

Is AI redefining the future of data reporting?

Data reporting is rapidly evolving from static dashboards and manual reports to AI-assisted insights, automated narratives, and real-time decision systems. As organizations adopt AI-driven analytics, the role of reporting teams, reporting tools, and even dashboards themselves is starting to change. How do you see AI reshaping the future of data reporting and business intelligence?

Data reporting is rapidly evolving from static dashboards and manual reports to AI-assisted insights, automated narratives, and real-time decision systems.

As organizations adopt AI-driven analytics, the role of reporting teams, reporting tools, and even dashboards themselves is starting to change.

How do you see AI reshaping the future of data reporting and business intelligence?