Statistics for Data Science 2018 Part 1 | Statistics Tutorial for Beginners | Data Science Tutorial
https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=c27EwKNIanQ&campaign=youtube_channel&utm_source=youtube&utm_medium=statistics-tut-sumit-part-1&utm_campaign=youtube_channel
Hello and Welcome to one of the Best Data Science tutorial conducted by Acadgild. This video talks about applications of statistics for data science. Let’s check the topics covered in this tutorial.
After completing this training session, you will be able to learn,
• Introduction to Statistics
• Basic Statistics
• Introduction to the Basic Terms of Statistics
• What are Variables
• The Measure of Central Tendency the Mean, Median, and Mode
• The Measures of Dispersion
• What is a Range
• What is Sample Variance
• Standard Deviation
• Population Vs Sample
• What is Chebysheff’s Theorem
• Law of Expected Values and Variance
• Probability Density Function
Check out the basic terms used in statistics: Variable, Data (singular), Data (plural), Experiment, Parameter, Statistics.
Kinds of Variables or Types of Variable: Qualitative or Attributive or Categorical variable
Kindly, go through the complete video and learn more about statistics and please subscribe the channel for more updates on the latest technical skills and tutorials.
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How much time will ti take to brush up my statistics up to a good level for data science and machine learning
5:48 correct, please
Datum is singular and Data is Plural
nice video but respecfully it would've been nicer to see more of the slides and less of your face than the other way around
Very nice way of explanation..
WIthout indian people, my brain is empty
THE best in class lecture in the topic..I'd be more happy, if you kept your work window (PC etc) into inset PIP…cause we need to go back and forth to see formulae.
What is P(B) for last problem. Why did we use expanded form of the bayes theorem?
Hi Gentleman, you are a great communicator, your lecture delivery is simply superb, your way of teaching has attracted my attention.
at 19:50 it should be
(50*0+50*50)/100=25 this no????
Hi Sir, Could you please have a video on Time-Series, in-case you already have then please share the link.
Hi Sir, This is awesome site for newbies in Data Science to understand the concept of Statistics. I liked it the way you explained the concepts, though its simple but effective.
i guess , your salary is 20 lakh per annum# Go to 7.37
in MSc cources for data science is this part included?
Are these 2 videos of statistics enough for Data science and Machine learning??
I like it.
Please using few Hindi
Thanks for the vedios.
Excellent explanation. Thank you so much
At 19:49 I think it should be (50*0+50*100)/100 instead of (50*0+50*100)/50.
Good video! I would suggest that you give examples where everyone can relate and not just Indians.
very helpful material.
Sir, please check the example of two section of classes. In section B when you are calculating the average you are dividing by 50. I think it should be 100. Please confirm. Thanks in advance.
sir , what is k in chebysheff's theorem?
how do we calculate the value of k
full of nonsense story, you need to explain with some sample data … totally useless speach i wasted time of seeing this vied
in the example @ 19:32 why did you divide by 50 rather than 100 as the number of students were 100?
Without Indians on YouTube, I would have failed my Computer science degree! So, thank you my South Asian friends.
19:50– (50*0 + 50 *100)/50 = 50
ONE OF THE BEST CHANNEL FOR DATA SCIENCE…?
Very Informative but the examples used are too boring and old. Can't we use new examples which are actually related to current situations or data science? These are old school examples from which attention gets diverted and the video gets boring, other YouTubers are using examples from their work(data science) or current situations. please do it differently not old school teachers. thanks
@6:30, how does gender impact ATM withdrawn, not sure about variable description w.r.t. ATM example (considering gender as an factor)