Why Statistics Is Super Important For Data Science? Must Watch
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- Опубликовано: 11 сен 2024
- Complete Stats Playlist: • Live Day 1- Introducti...
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Few use cases of statistics in predictive modeling:
1. Validating distribution of target variable
2. Before building models we do have a few hypothesis in our mind or business. After getting the data and target finalization we have to test our hypothesis using various tests.
3. Hypothesis testing used in feature selection as well. Like if a variable adding significant value to a model predictions
4. After models building statistics used in modeling monitoring such as significant reduction in model performance accuracy or KS or hit rate etc
Hi Krish, Your Statistics related videos are a real boon for anyone breaking into the Data field. Kudos to all your efforts!!!
Happy Teacher's Day. On the occasion of this auspicious day we at @iNeuron Intelligence are offering 30% off on all the courses. Use Krish30 as the coupon code. Happy Learning!!
Check out all the courses link below. This offer is valid till 10th September
FSDS Bootcamp 2.0 With Job Guaranteed: bit.ly/3qalRaF
Data Science Industry Ready Projects : bit.ly/3qmKGAe
30 Days Data Science Interview Prep: bit.ly/3ASKOfy
Java Full stack Developer: bit.ly/3ei8HWa
Devops Pro: bit.ly/3D4KI7l
Ji sir thank you so much 😊
But teachers day to 3 din pahle tha na🤔
Hello sir i want to learn ai from your channel and i like your teaching way ...but I can't able to find the playlist... please help me here❤️🙏
Love From Nepali 🇳🇵🇳🇵 student yrr 🥰
Please make a playlist for Linear Algebra and Calculus, I recently finished your 7 day live Stat and found it amazing, hope you can do the same for Linear Alg and Calculus
To find how your model perform, most of the times we use different measurements like f1-score etc... are also statistical measurements only. A small question to all, why we have to divide with n-1 for Variance or SD for sample , but when we calculate for population Var or SD, we divide with n ( n is the no of subjects in the sample or sample size) . Dont escape with avoid Bias. let us make it more statistical
Well, this can be explained two ways : downward bias and the degrees of freedom.
Let me explain in terms of degrees of freedom, dof is number of observations minus the number of parameters estimated. So while calculating SD, we already have an estimated parameter (mean of all the observations) in the numerator, which brings down the dof to n-1, so divide the numerator of SD by n-1 not n.
Much needed video at right time
Wow thanks 🤩❣️
More power to Krish bhai😇🙏
Keep making such kind of videos related to data science, its very helpful for us. Thank you Krish❤️❤️
Hello sir i want to learn ai from your channel and i like your teaching way ...but I can't able to find the playlist... please help me here❤️🙏
Love From Nepali 🇳🇵🇳🇵 student yrr 🥰
Hi Krish,
I am a Mech Engg graduate, aerodynamics post graduate from one of the top IITs . I got into EdTech sector in content team 3 years ago in campus placement at IIT.
Present CTC 10 LPA.
How is the scope in today's market ? I want to switch but need guidance.
Excellent Krish ! You always rock :)
I have gone through your statistics video thanks Krish 🙏
Sir video header alli spelling mistake --- MUST WATCH pls correct it. Thanks.
Krish ..can you specify why exactly hypothesis testing was used ??
Can you please continue NLP Live playlist please?
Please share Roadmap to NLP Engineer
Hi Krish, Is SPSS useful tool to learn? For Business/data analyst
I want to get in data science but i m very weak in dsa . So can i go in data science without dsa
Must Watch*
Hi Krish , you should create a discord server......
EDA and ML sir
Ride video at right time
Why stat is important for data science ? Answer: Data Science is nothing but mathematics implemented by computer technologies.
Guys the name Data Science and people think they can do it without Maths and Stats 😂😅?
Many people choosing this because of misconceptions like no coding, maths etc.😂🤣but it is completely opposite
During EDA
Hello
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