Live Day 2- Basic To Intermediate Statistics
HTML-код
- Опубликовано: 2 окт 2024
- Join the community session ineuron.ai/cou... . Here All the materials will be uploaded.
The Oneneuron Lifetime subscription has been extended.
In Oneneuron platform you will be able to get 100+ courses(Monthly atleast 20 courses will be added based on your demand)
Features of the course
1. You can raise any course demand.(Fulfilled within 45-60 days)
2. You can access innovation lab from ineuron.
3. You can use our incubation based on your ideas
4. Live session coming soon(Mostly till Feb)
Use Coupon code KRISH10 for addition 10% discount.
And Many More.....
Enroll Now
OneNeuron Link: one-neuron.ine...
Direct call to our Team incase of any queries
8788503778
6260726925
9538303385
866003424
🎯 Key Takeaways for quick navigation:
02:18 📊 Mean, median, and mode are measures of central tendency used to find the center of a data distribution.
12:19 📏 Median is robust against outliers and is suitable for datasets with extreme values.
19:56 📈 Mode is useful for categorical variables and finding the most frequent element in a dataset.
26:21 📚 The choice between mean, median, or mode depends on the specific dataset and domain knowledge, considering factors like outliers and data type.
29:25 📉 Variance and standard deviation are measures of dispersion used to quantify the spread or variability of data points within a distribution.
52:11 📊 Percentiles are values below which a certain percentage of observations lie.
53:07 📈 To calculate the percentile rank of a value, use the formula: (Number of values below x / Total sample size) * 100.
55:07 🧮 To find the percentile ranking of a value, use the formula: Value = (Percentile / 100) * (n + 1).
57:54 🧾 For example, the 25th percentile value in a dataset of 21 elements can be calculated as (25 / 100) * (21 + 1) = 5.25, which corresponds to the 5th element.
01:00:12 🖋️ Pay attention to index positions when finding percentile values in data sets.
01:02:15 📊 The 75th percentile is calculated as 75 divided by 100 multiplied by the number of data points, and it helps identify a specific data point in a dataset.
01:05:25 📉 The process of removing outliers from a dataset involves defining a lower fence and an upper fence based on the interquartile range (IQR).
01:08:08 🧮 The interquartile range (IQR) is computed as the difference between the 75th percentile (Q3) and the 25th percentile (Q1) and is used to identify outliers.
01:17:39 📊 Box plots provide a visual representation of the five-number summary, including the minimum, Q1, median, Q3, and maximum values, helping identify outliers.
01:20:19 📚 The video covers various statistical concepts, including variance and the formula for sample variance, as part of a comprehensive data science course.
Made with HARPA AI
Commenting so i can comeback to read
After watching all statistics 7-day live sessions and now I have got so much confidence in stats, and I am very sure that I could learn data science subject. The way you teach is very clear and absolutely very easy to understand which I didn't find in my university. A Big Thanks to you Krish Naik ji for your great effort to help me and many students with your knowledge to shine their future and it is much appreciated. 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏
5:45 Arithmetic mean for Population & Sample
11:00 Central Tendency
13:28 effect of outliers in mean
15:30 Median
21:45 Mode
29:20 Measures of Dispersion
31:20 Variance
42:00 Standard Deviation
44:40 Importance of STD and Var
46:50 Percentile
1:04:20 Five number summary
Thanks
Life Saver
Thank you...
here does the index start from 0 or 1?
You are the god for statistics 🙏🙏🙏
Thank you thank you so much sir
I used box plots before and was always struggling to explain it to my users.. now I think I can explain it to anyone!
Though the year is 2050 ur videos will be rocking definitely.......💥
Thank you for this amazing playlist krish
Just a request plz complete your SQL playlist as well so that learners can be in regular practice
Well structured and mannered explanation
Sir till now I used to draw the box plot but still have no idea. How this box plot was actually made but after this I exactly understand how to make the box plot. Thank you so much for these amazing classes.
Krish you are amazing .Thumbs up guys for Krish 👍
5:45 Arithmetic mean for Population & Sample
11:00 Central Tendency
13:28 effect of outliers in mean
15:30 Median
21:45 Mode
29:20 Measures of Dispersion
31:20 Variance
42:00 Standard Deviation
44:40 Importance of STD and Var
46:50 Percentile
1:04:20 Five number summary
Now i got why should i choose box plot for data visualization. Thank you so much sir .
Very helpful thanks a lot, great work 👌👌👌
loved the series sir. Making notes too along the way.
Just found this, you're the GOAT bro.
nice bro,
I Like the way of You Teaching..
Thanks Krish
Thank you sir , you are simply the best.
Explanation wad superb krish...
Amazing way of teaching
Isliye hi aap top par ho teching me awesome teaching process
Thank you for session
Please do live sessions on SQL and excel for data analyst role.
Awesome ,, explanation Sir
This series is amazing sir ☺️
Great job!!!!
what an explanation 😮😮😮
yes sir you are audible
Hi Krish,
You are doing amazing work. Thanks for these videos.
You are great.
Thanks Krish👍
Awesome Krish!!!!!!!!!!!!!! No teacher is comparable to you ! Excellent style of teaching and great service
excellent explanation sir😃😃
i saw this session now is was so good after completed my R programming im here to learn such the best man and best explanation of the all the stuffs.......
good work man Krish Naik
Tnku so much sir ❤❤
sir, u are best i read many books and watched many tutorials for boxplot,but no one made me understand.But,you made it in 3 min
@10;08 should be n-1for sample mean right Krish
The same type of things that are there available on the internet.. everyone teaches what but no one teaches why and how??? Just writing the formulas but why we use them and how they come into existence, no one describes... hope someday you will take care of that part also.
Excellent lecture! I thoroughly enjoyed Day 2 and am looking forward to Day 3
like it
Krish u r 1 of them who give high quality content, relevant to interview perspective, in free sessionS aswell..... commendable 🙏
4.98 likes are very less for your efforts Krish, who wants to really become a data scientist, your video help full for them
Kitna bhi acha padha lo. Kahin kuch galati karte hai. toh puri duniya gaali deti hai XD
*Joking apart*
You do an amazing job!!! My sincere gratitude to all the efforts you put in :)
Krish sir is the saviour everytime when problems arise 💖🙌
savior of all DS intermediate
I couldn't understand it in my class because am struggling from headache, Insomnia, Stress, Anxiety and Depression took some medicines felt little good got enough brain to consume this good quality lecture this was amazing experience
Superb
Very well explained and described, very usefull compratively to other youtube videos, they are not going in depth as yours
Awesome
it is very good tutorials beginners like me very easy to understand thanku for ur explanation sir
First time i understand the logic behind box plot .......thnk u sirji 👍👍👍👍 u are doing really good work sir ..
We dont need to calculate Q1 , Q3 after removing the outlier as value of n changes in that case?
Same Question
@@gautammittal1481 Same doubt here too
I wanted to buy Ineuron full stack data science course , then i looked a quora post and now i changed my mind
sir if u could show us in python with dataset and business problem that would be very helpful
What is the purpose of removing an outliers?...is it necessary to do?
1:21:00
Amazing method of teaching Krish clearly getting all the things. Thank's a lot for these videos.
Sir, I have a doubt. Can you say that how 5 is 1.5 standard variation from the mean. If mean is 2.83 then 5 will far 2.17 standard deviation from mean 2.83....and if mean is 4.17 then 5 will far 0.83 standard deviation from the mean 4.17...... 46:34
Superb and step by step process followed.😊👍
I am big fan of you sir seeing this video 😇
Krish Sir u r Awesome and Genious.... Thanks for ur efforts for making us expert and getting depth knowledge in Stats..
Median 5
wonderful explanation!😁😁
Is RTX 3050 better than GTX 1650
For mining or machine learning related computing?
Yes
@@jeremyccc for deep learning and ML
@@shireeshkumar6631 RTX3050 is much better
excellent explanation Krish....you made IQR explation so easy
Please, which device are you using? And, how can I get it?
at 1:16:16 the q1 and q2 are computed from orignal data or the data remained after removing outlie?
?
22:10 funny moment 😂😂
Amazing explanation for the Boxplot
very good explanation respectable sir
at 19:00 - Said as Mode , But it is median
hello, sir while the index is 15.75 why we are taking 15th position and not the average of two points that is 15 and 16th position
Awesome video, please continue your good work.
hello sir, how to get the pdf materials, as the mentioned link is not working, Kindly reply!
Ek galti duniya Juta marti hai ...Gajab ki line hai maja aaya ... but thanks for Stream
Sir I m able to find pdf of notes.Is it still thereon website or deleted.Pls replie
👍👍👍👍👌👌👌👌🤩🤩
super explanantion and unique way of teaching-: thank you for your classes👌👌👌
Krish sir the material link is not working can i have different link for material 😢😢
Where can I find pdf of your notes ??
Can someone 😢please tell me how does 5 fall in 1.5 s.d from the mean (2.83) ?
thanku sir
lower fance and higher fance how that 6 will be come
Homework: why do we use n-1 is used in the formula for calculating sample variance instead of n?
To answer this let us understand the difference between the sample mean and population mean. Population mean is the average computed for the entire data and sample mean is the average computed for that particular sample. Therefore there would be some differences between them. the sample mean will be close to the population mean.
Now coming to sample variance and population variance, the difference will be huge between the sum of the squares of the difference between data and sample mean and the sum of squares of (data - population mean). This is due to the small difference between the sample mean and population mean. When it is squared the difference is more. To compensate for the difference we use n-1 in the denominator to increase the value such that the sample variance is closer to the population variance. Why n-1 the value comes closer when we compute considering n-1 than to n-2, n-3, and others.
Happy learning!!
Thank You so much for your valuable class ..
thank you very much...............
🙏
Lord of Data Science Community and Saviour of Data Science aspirants!!! #KingKrish
Dear Mr.Krish I am really thankful to you from the bottom of my heart to launch Statistics and ML playlist. Its really helps me to fastly recall everything.....
Interesting,informative but 59:00 lol hahahahaha 🤣🤣🤣🤣🤣🤣🤣
Great lecture sir ❤️🎉.. thanking you very much air
Krish,,could you please suggest any external python certification .
can i get that pdf notes?
where is pdf sir??
Can't thank you enough for this playlist. Your teaching style is really amazing. I've watched this video nearly 6 months ago, watching this from yesterday.
Someone please send me notes .
How can i get the notes ??
Sir please provide sql
iPad glitch of switching background is fixed in new version, in new version it will make sure black inc in white background and vice versa
Thanks you sir
Thank You