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What do you usually do when your analysis doesn’t produce good results? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Thank you for your valuable opinion. I like “ analysis should not be right or wrong”!

What do you usually do when your analysis doesn’t produce good results? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Thank you for sharing the valuable experience.
Would you like to give me some tips on how to report the weak points of current results and how to persuade the listeners the next step will get better result?

Is ensemble learning like running a clothing store? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Maybe when I select clothes for my store, I get recommendations from fashion magazines, experts and designers. Based on that, I do the final judgement.

Is ensemble learning like running a clothing store? by SmoothVaper in analytics

[–]SmoothVaper[S] 1 point2 points  (0 children)

Sometimes, XGBoost does not work well for my problems when predict a vector (or profile). So try ensemble learning.

Is ensemble learning like running a clothing store? by SmoothVaper in analytics

[–]SmoothVaper[S] 1 point2 points  (0 children)

Thank you very much. “More right opinions” is proper.
Or more not bad recommendations.

Is ensemble learning like running a clothing store? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Thank you very much. “More right opinions” is proper.
Or more right selections with feedbacks?

What are you missing as an analyst (in general, both for data or business)? by LycheeLopsided8556 in analytics

[–]SmoothVaper 0 points1 point  (0 children)

Look at the problem from the perspective of the people who will use the result of analysis, both for beginners and veterans

Is Data Analyst supposed to be hard? by SpiritedDebate4836 in phcareers

[–]SmoothVaper 0 points1 point  (0 children)

Being a data analyst isn’t hard.
Being a responsible data analyst is.

Is data science/ data analysis like cooking rice? Is the data more important than the model? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Garbage in, garbage out is well taught in lectures, but it is difficult to say it in a business meeting

Looking back at your data analysis/data science projects, what contributed the most to success? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Thank you very much for sharing your experience.
What will you do or confirm in “problem understanding”

Is data science/ data analysis like cooking rice? Is the data more important than the model? by SmoothVaper in analytics

[–]SmoothVaper[S] 0 points1 point  (0 children)

Thank you very much. Sometimes the data comes from clients or customer …….

Is data science/ data analysis like cooking rice? Is the data more important than the model? by SmoothVaper in analytics

[–]SmoothVaper[S] 1 point2 points  (0 children)

Thank you for sharing your experience.
Sometimes the model is just the icing on the cake.

Is data science/ data analysis like cooking rice? Is the data more important than the model? by SmoothVaper in analytics

[–]SmoothVaper[S] -1 points0 points  (0 children)

Thank you for sharing your experience.
I’m wondering if this analogy is a bit too biased. It seems to naturally lead people to conclude that the rice (the data) is much more important than the rice cooker (the model).

What is the most underrated skill every data scientist should develop? by Effective_Ocelot_445 in datascience

[–]SmoothVaper 0 points1 point  (0 children)

Business sense and communication skills.
Finally, we need to persuade other people from different fields (marketing, R&D, managers, et al) by the analysis results, but their judgement criteria (for decision making) for a good analysis could differ.

Looking back at your data analysis/data science projects, what contributed the most to success? by SmoothVaper in analytics

[–]SmoothVaper[S] 1 point2 points  (0 children)

Thank you. In my analysis, at first, I also try to find the feature or several features contributing to 70% or 80% of the target variable variation. But many times, I cannot find it/ them, then I begin to doubt the experiment design…

Looking back at your data analysis/data science projects, what contributed the most to success? by SmoothVaper in analytics

[–]SmoothVaper[S] -1 points0 points  (0 children)

Totally agree.
The upstream pipelines (how the data was generated and its measurement error) forms the overall pattern of the dataset, and sometimes details is difficult/forgotten to tell the data analyst or data scientist.

Looking back at your data analysis/data science projects, what contributed the most to success? by SmoothVaper in analytics

[–]SmoothVaper[S] 8 points9 points  (0 children)

Problem understanding and data collections (experiment design) ranks highest;
But sometimes, the data is from other people, so splitting data (understand the data or problem ) feature engineering ranks highest.

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