回复攻略 · Data science
What data scientists and ML engineers post on X, reply angles that get a reply back, six sample post and reply pairs, and the mistakes to avoid.
Data X mixes analysts, data scientists, ML engineers and researchers. They post charts that look suspicious, notebook screenshots, opinions on tools and models, interview questions, and careful threads about a paper or an experiment.
The culture rewards skepticism done politely. A one-sentence question about the baseline or a possible leak is read as respect for the work. A reply that says "AI is changing everything" is read as someone who did not look at the chart.
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A plot, a model score, an A/B test result. The best replies ask about the axis or the sample.
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SQL against dataframes, notebooks in production, the pipeline tool everyone is trying this month. Room for a war story.
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Someone summarises a result. Ask about the eval set, or the ablation you would like to see.
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Analyst to data scientist, what interviews test, which skills get you hired. Concrete experience beats a reading list.
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"Compared to what?" is the most useful question in the field. Make it specific: last year's model, a simple average, a random guess, the rule the team used before.
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If a score looks too good, ask gently whether a feature knows the future. As a question it reads as help, and you might be wrong.
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The null that meant something, the timezone in the logs, the user IDs that got reused. Practitioners love these and reply with their own.
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When someone posts a deep learning win, ask how a gradient boosted tree or a logistic regression did on the same split.
05
The y-axis that starts at 97, the legend sitting on top of the data, the fourteen colours. Specific and kind.
示例帖子为本页编写，并非真实账号。
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Our churn model hit 0.94 AUC on the first try. Pretty happy with this one.
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0.94 on the first try makes me want to check one thing: is any feature computed after the churn date? "days since last login" did this to us once
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Unpopular opinion: most companies don't need a data scientist. They need someone who writes clean SQL and says no to new dashboards.
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and someone who can tell which of the 40 existing dashboards anyone opens. we checked the view logs once and it was 6
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Chart of the day: our signups doubled after the redesign.
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does that y-axis start at zero? and did the redesign ship the same week as anything else, like a pricing change or a launch post?
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Reminder that p = 0.049 and p = 0.051 are basically the same result. Stop treating 0.05 like a cliff.
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the worse habit is running the test until it crosses 0.05 and stopping there. peeking has done more damage than the threshold everywhere I've worked
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New paper claims a small model beats a much bigger one on reasoning benchmarks. Reading it this weekend.
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curious what the eval set looks like when you're done. a lot of these results shrink once someone checks how much of the benchmark was in the training data
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Moving all our notebooks into a proper pipeline this quarter. Wish me luck.
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good luck. the notebook whose cells only work when run in the order 1, 4, 2, 7 will be the last one standing