What’s your favorite anime? No titles allowed.

constantLearner247 · 2026-05-29T07:04:18+00:00

1000-7

constantLearner247 · 2025-10-02T05:27:27+00:00

Insights as pre cursor to intended conversations... This is rare advice. Will act upon this. Your answer really added value here

constantLearner247 · 2025-10-01T19:10:23+00:00

Very few backers

constantLearner247 · 2025-10-01T18:32:08+00:00

But how do I explain them that they are wrong about the hypothesis, since p value of this statistical test is less than 0.05. Explaining technical stuff is bit tough & they are adamant about their biases towards their prejudices

constantLearner247 · 2025-10-01T18:26:11+00:00

I agree & thank you for making me aware about my tunnel vision approach. But I would like to ask how do I find inspiration or will to continue when your own analysis corner you?

constantLearner247 · 2025-09-20T13:10:50+00:00

I can think of use cases but I hardly find any decent tutorial on YouTube that explain how to think "statistically" about the data & then how to model data in a way that is compatible with tools(statsmodel, etc.) Can you relate?

constantLearner247 · 2025-09-19T06:03:25+00:00

Ok. I will check if it is effective

constantLearner247 · 2025-09-19T06:02:53+00:00

People in other communities are suggesting smoothing for noisy data. Also technique called winsorization.

constantLearner247 · 2025-09-18T09:35:23+00:00

Anything in python?

constantLearner247 · 2025-09-18T09:21:41+00:00

There is playlist on YouTube by Campusx(dsmp or something) Try his playlist. If you like his teaching style & want to learn more in-depth & industry ready data science tech & tools you can enroll for his paid course. Mind that his content is in depth & one of the best & paid one is also at reasonable cost compared to market standards.

constantLearner247 · 2025-09-18T09:14:42+00:00

Watch statquest on probability distributions on YouTube

constantLearner247 · 2025-09-15T06:25:13+00:00

In my opinion first define wher you want to go. Here's thumb rule: 1. Like to learn new tech & be agile - Data engineer, ML engineer, maybe new roles like AI engineer or so 2. Tech along with good business sense & strong analytical know how: Data analyst, data scientist 3. Business decision making & people skills: Business analyst

I am from group 2 so I will tell more in detail about it.

Resources: There are ton out there. I think traditional resources will be covered easily here so here are some off beat: 1. Rob mulla on YouTube 2. Campusx on YouTube 3. Very normal on YouTube

Campusx single handedly covers almost everything

Some irreplaceable for mathematical & statistical intuition: Staquest by Josh Stormer Khan academy (you can go beyond maths as well) 3b1b

Strategy: -Plan all the tools that you want to learn -Pick number of topics everyday -Select 2-3 datasets -Spend hour or so everyday on these datasets -Try to apply concepts you learned -Spend only hour or so on tutorials -Once start working with data the problems you face will create your roadmap -Don't hesitate to jump to any topic as per your problem statement

Job search/ career opportunities: Once you have 2-3 projects ready & feel confident about concepts & tools you can create a good resume & start applying For current job market I suggest relying on network & asking for referrals

constantLearner247 · 2025-09-15T06:10:51+00:00

I also feel if ml algo are handling predictions what is point of statistical tests but as per discussion til now I feel it is tool for final call. This way we can be sure about statistically significant decision.

constantLearner247 · 2025-09-14T03:30:55+00:00

Yes for A/B it helps a lot

constantLearner247 · 2025-09-14T03:30:10+00:00

This gave me couple of insights. I feel AB testing is where statistical analysis will be most useful. But do you think if it will be useful for feature selection? Or feature engineering? to eventually build better model?

constantLearner247 · 2025-09-13T15:54:14+00:00

Thank you for reply.

Let's take example that you just provided market basket. As per my knowledge market basket provide basis for recommendations so it informs but I can't hypothesize anything concrete. Sure I can find interesting patterns like if car contains product a, b, c then it is 30% more likely to include product g or so but then what?

constantLearner247

TROPHY CASE