Session 41 - Normal Distribution | DSMP 2023

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| **Chapters** |
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00:00:00 - Session start

*📝Extension of the previous session*
00:04:53 - Recap of previous Statistics sessions
00:08:22 - How to use PDF in Data Science?
00:31:03 - 2D Density plots

*📝Normal Distribution*
00:37:15 - What is Normal distribution?
00:42:28 - Why is it so important?
00:44:18 - Equation & Parameters of Normal Distribution
00:51:04 - Intuition about the equation

*📝Standard Normal Variate*
00:58:42 - What is Standard Normal Variate?
01:02:38 - How to transform Normal Distribution to Standard Normal Variate?
01:10:40 - Why is it important & what are Z-Tables?
01:22:20 - Empirical rule

*📝Properties of Normal Distribution*
01:28:36 - Symmetricity
01:29:17 - Measures of Central Tendencies
01:29:42 - Empirical rule
01:32:05 - The area under the curve

*📝Skewness*
01:33:20 - What is Skewness?
01:38:48 - How is skewness calculated?
01:40:24 - Python example & interpretation

*📝Other important topics*
01:44:45 - CDF of Normal Distribution
01:49:51 - Use of Normal Distribution in Data-Science

01:55:20 - Discussion & Session end

#datanalytics #Stats #statistics #SQL #descriptivestatistics #campusx #dsmp2023

[AdnanAli-vmox]

WOW! such an explanation, sir your explanation is on 6 standard daviation, keep it up. This field is getting even more interesting for me because of you sir. Thank you!

[shwetaraturi]

U r a great teacher. I have not seen anyone with so much of in-depth knowledge.

[KuldeepKumar-g]

You are the Don Bradman of Machine Learning field.

[vijitsahu]

I haven't seen such an in-depth video about statistics that could relate data science in such an effective manner ... I have been watching statistics from so many teachers but couldn't relate how statistics is utilized in data science But now things are getting cleared ...

[mohankumar]

Wish you to get 75lakhs subscribers soon. Your efforts, quality of teaching and humility deserves that.

[shibandevi]

Not a single person gave me such deep intuition about the formula of normal distribution.

[sowmyaraoch]

OMG. You're extremely amazing. Every video of yours is so helpful and detailed. Please keep sharing your knowledge.

[smblog]

Thank you !
1:07:08 - standardizing data
1:50:40 - finding outliers

[McqsEtea]

Gr3at! How can I appreciate you I have no words

[shashankshekharsingh]

i wish my collage teachers can teach like you, first you clear every basic and fundamental things and make very hard topics like a piece of cake

[fachoyt]

Ap b tu Outlier ho teaching ki field me.. You are the best teacher.. Love From Pakistan

[arindamInsightFullMath]

great lecture sir ji ....as always great

[rohinisingh]

I have no words for your teaching. I'm confident for taking placement in the data science field just because of you sir 😊.you are the great one 🎉

[narendra]

Really amazing session, this video has cleared all my queries . You have explained very well.

[namansethi]

Thanks Sir for this quality teaching which was missing in our college education.

[SwiftMind]

Congratulation for 75k mark. Looking forward to 100k soon.

[aishatahir]

Hi! I dont know if you will be answering it. But you said if sepal length is less than 1.7 then chances of flower being versicolor is 95% . I think that correct sentence will be "95% of versicolor will be in this range" Not the chances of Flower Being Versicolor will be 95%. Because If we see there are 10% of other flowers, Virginica in that range. So 95% of Versicolor and 10% of Virginica will make Up 105%. Chances of flower being versicolor in that range(below 1.7) will depend on how many Total flowers are present including Virginica. And then finding versicolor percebtage out of all flower.


Please correct me If I am wrong.

[DataScienceWithAkesh]

mera teacher paid course ka sir ka contant same copy krta hai. balki kar bhi nahi pata ache se. or dusra sir ki mehnat ko khood ki batata khood pta ni lgta. or kehta hai ki one of my intern ne project bnya hai. jabki wo nitish sir ke intern ne bnaya tha

[SidIndian]

Happy Ugadhi sir

---- from Siddhartha Bangalore 🙏🙏❤️❤️❤️🙏

[radhikawadhawan]

I dont understand why have we divided by 2? The explanation : because it is combination of 2 graphs, doesn't make sense either.
As per my understanding we wanted decaying property of e^(-x) and a graph symmetric on both side of x=0. Hence the square => e^(-(x^2)). Then scaling and shifting happened. Hence e^(-((x - mu) / sigma)^2