u/SmoothVaper

Before starting my career, I thought certain skills would dominate my day-to-day work.

However, after gaining some real-world experience, I’ve realized that some skills seem to be emphasized much more than they’re actually used.
For those already working in the field, what skills do you think are overrated?
For example:
Advanced programming?
Knowing every machine learning algorithm?
Advanced mathematics?
Memorizing statistical methods?
Something else?
On the other hand, what skills turned out to be much more important than you expected?

For me, AI tools have made many programming tasks much easier, and I find myself using a relatively small set of statistical methods repeatedly. I’m curious whether others have had similar experiences or completely different ones.

I’m curious about what happens after the analysis is finished.
When you present your findings to colleagues, managers, or stakeholders, what questions come up most often?
For example:

Why did you choose this method instead of another?
How reliable are these results?
How confident are you in the conclusions?
Could this just be noise or coincidence?
How well does the model generalize?
What assumptions did you make?
What would you do next to validate the findings?

I’m especially interested in questions from business rather than academic settings.

What questions do you now anticipate before every presentation?

Why train not others?

In real-world data science projects, what is your typical workflow when your analysis or model performs worse than expected?

Do you usually:
Revisit the problem definition?
Check the data quality?
Engineer new features?
Try different models?
Collect more data?
Conclude that the available data simply doesn’t contain enough signal?

I’m interested in practical approaches and lessons learned rather than textbook advice.

One more question: How do you communicate disappointing results to stakeholders or your manager?

Think about section analysis of temperature field or flow field or the distribution map of a feature.

Is it to find some features or common things to reproduce the pattern/profile?

To find why the maximum or minimum happens to be there?

To find which features contributed to current patterns/profiles?

I’ve been thinking about an analogy for ensemble learning.
Imagine you own a clothing store.
No single piece of clothing can satisfy everyone. Different customers have different body types, preferences, budgets, and occasions.
Instead of trying to design one “perfect” outfit, the store offers many different options. Each item only fits a subset of customers, but together they can satisfy almost everyone.
Ensemble learning feels similar to me.
Each individual model has its own strengths and weaknesses and performs well on only part of the data. By combining multiple models, the ensemble can handle a much wider range of cases than any single model.
Does this analogy make sense, or am I missing something fundamental about how ensemble methods work?

I’ve been thinking about an analogy.
If cooked rice doesn’t taste good, the problem could be:
The rice itself is poor quality.
The rice cooker isn’t very good.

It feels similar to data science:
The data (quality, relevance, feature engineering, measurement error, etc.) is like the rice.
The model is like the rice cooker.
Even the best rice cooker can’t produce great rice from poor-quality grains, while good rice often turns out reasonably well even with an average cooker.

Do you think this analogy holds in real-world data science?

If you look back at the data science projects you’ve worked on, how would you rank the factors below by their impact on the final result?

Problem understanding
Data collection
Data quality
Feature engineering
Model selection
Hyperparameter tuning
Validation strategy
Domain knowledge
Communication with stakeholders
et al.

Background: Over 200+ features(monitoring data from equipment) , The challenge is that I don’t know whether the factors causing failures are even included in these features
Before jumping into model selection, what would your workflow look like?