Statistics for Data Science & Machine Learning

The most in demand skills in the world right now are in Data Science & Machine Learning! In this one video I will teach you a key part of Machine Learning and Data Science which is Statistics.

I took everything in a standard 500 page text book on Statistics and put it in this one video. I will cover every formula, but also will solve real world problems with each formula.

After this video I will continue with the Math of Machine Learning by covering Linear Algebra, Calculus, and the Python Data Science / Machine Learning Frameworks. If you want to see those videos CLICK THE NOTIFICATION BELL.

MY UDEMY COURSES ARE 87.5% OFF TIL March 26th ($9.99) ONE IS FREE

#LearnWithMe #Statistics #DataScience #MachineLearning

Here is a Table of Contents that will allow you to jump around in the video and learn what ever you are interested in.

TABLE OF CONTENTS

00:00 Intro
00:25 Basics
01:25 Categorical Data
01:59 Numerical Data
02:10 Continuous Data
02:25 Qualitative Data
02:56 Quantitative Data
03:11 Cross Table
03:36 Pie Charts
03:52 Bar Charts
03:59 Pareto Charts
04:16 Frequency Distribution Table
04:32 Histograms
04:54 Mean
05:44 Median
06:03 Mode
06:26 Variance
07:24 Standard Deviation
08:02 Coefficient of Variation
09:01 Covariance
10:23 Correlation Coefficient
11:12 Maximize Profit
13:45 Probability Distribution
14:26 Relative Frequency Histogram
14:38 Normal Distribution
15:21 Standard Normal Distribution
16:32 Central Limit Theorem
16:55 Standard Error
17:14 Z Score
17:50 Z Table
18:35 Confidence Interval
19:22 Alpha
20:05 Margin of Error
20:13 Confidence Interval Example
20:57 Critical Probability
21:49 Student's T Distribution
22:43 Degrees of Freedom
22:50 T Distribution Example
23:33 T Table
24:30 Dependent Samples
25:29 Independent Samples
26:26 Hypothesis
27:02 Null Hypothesis
27:20 Alternative Hypothesis
27:37 Significance Level
29:06 1 Sided Tests
29:35 Type 1 Errors
29:57 Type 2 Errors
30:32 Hypothesis Error Example
31:23 Means Testing
33:33 P Value
34:25 Regression
36:24 Regression Example
37:53 Correlation Coefficient
39:11 Coefficient of Determination
41:14 Root Mean Squared Deviation
41:46 Residual
43:27 Chi Square Tests
47:03 Chi Square Table

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[derekbanas]

TABLE OF CONTENTS

00:00 Intro
00:25 Basics
01:25 Categorical Data
01:59 Numerical Data
02:10 Continuous Data
02:25 Qualitative Data
02:56 Quantitative Data
03:11 Cross Table
03:36 Pie Charts
03:52 Bar Charts
03:59 Pareto Charts
04:16 Frequency Distribution Table
04:32 Histograms
04:54 Mean
05:44 Median
06:03 Mode
06:26 Variance
07:24 Standard Deviation
08:02 Coefficient of Variation
09:01 Covariance
10:23 Correlation Coefficient
11:12 Maximize Profit
13:45 Probability Distribution
14:26 Relative Frequency Histogram
14:38 Normal Distribution
15:21 Standard Normal Distribution
16:32 Central Limit Theorem
16:55 Standard Error
17:14 Z Score
17:50 Z Table
18:35 Confidence Interval
19:22 Alpha
20:05 Margin of Error
20:13 Confidence Interval Example
20:57 Critical Probability
21:49 Student's T Distribution
22:43 Degrees of Freedom
22:50 T Distribution Example
23:33 T Table
24:30 Dependent Samples
25:29 Independent Samples
26:26 Hypothesis
27:02 Null Hypothesis
27:20 Alternative Hypothesis
27:37 Significance Level
29:06 1 Sided Tests
29:35 Type 1 Errors
29:57 Type 2 Errors
30:32 Hypothesis Error Example
31:23 Means Testing
33:33 P Value
34:25 Regression
36:24 Regression Example
37:53 Correlation Coefficient
39:11 Coefficient of Determination
41:14 Root Mean Squared Deviation
41:46 Residual
43:27 Chi Square Tests
47:03 Chi Square Table

[Krazness]

Derek, I've been following you for years, and I'm not exaggerating when I said you've been influential in my life. I hated computer science in high school, and I avoided it in college. I ran across your Java playlist years ago and you showed me how great and fun programming can be when taught by the right teacher. Because of you, I took software engineering courses, and I've been a developer for 7 years now. Now I'm looking into learning data science and ML, and I came across this video. I wish I could repay you for everything you've given me. Sincerely, thank you for all you've done for the community.

[masoud]

believe me or not, this is the most comprehensive and understandable tutorial in statistics that I've ever seen. It's a treasure. Thanks Derek

[austincarter]

At 23:41 the t-value is supposed to be:

In this case:

α = 1 - 0.95 = 0.05
α/2 = 0.025 (for a two-tailed test)
Degrees of Freedom = 29

The critical t-value for this scenario is approximately 2.045

In addition at 37:30 the sum of xi (-) x-bar is backwards. If you put that where 3199.6 is then you get the right slope. Putting this here just in case anybody was confused :)

Great video Derek you're a life changer. You're an amazing person for going out of your way to enrich the lives of others

[dystopian_]

I have huge admiration for people who are spreading knowledge. From my point of view, they are richer than billionaires.

[Esparzamx]

Once again, thank you man! Nothing beats your fast, condensed style.
Your videos are literally worth their weight in gold. (I know that doesn't make any sense) (but at the same time it does!) (They are pretty valuable tho...)
You rock man!, I owe you my job man! And now I'm sure I'll owe you my certifications in ML

[saketnarendra]

This is exactly how you should teach someone statistics for data science. Kudos 👌

[digvijaysingh]

Thanks so much Derek for listening to all of us! Appreciate it 3000!

[AllisWell]

Thank you very much. This helped me to summarize math related to machine learning and data science in half a day that I originally learned from other paid online tutorials for 6 months. Much appreciated. Well done. Keep up the great work!

[lazytocook]

I first learnt Java in 2014 from the legendary Derek Banas. Awesome channel.

[DjokerNole]

I've been learning from you since 2015 when I had to clear a Java exam in my Bachelor's of CS and I've been coming back to your channel to learn ever since. Thanks for all your effort. Love your style of teaching.

[VivekMore]

So glad you chose this topic - I was trying to find a way to ease into the world of ML

[Filaxsan]

This video, along with the probability one, is very well made! Thanks for making it!

[jrgunes]

y'know what'd make this even better? A cheat sheet that covers all sections of the video :p

[tonynikolaos]

Big Thanks, Derek! As always you are doing marathons worth the time and effort.

[psyjax]

Derek, is there anything you DON'T know how to do?

[shamirgeorge]

Great tutorial, it actually has a bunch of stuff I've been dying to know like that darn confidence interval. I was actually doing a Jamovi course (a free statistical spreadsheet) and I came across all these concepts I didn't understand. I had to spend loads of time searching for videos and articles. Now, it's all in one video. Amazing! This will really help me fill in the gaps of what I missed. I feel like I've become one of those people who go like, "I was just thinking that, and then a video going like 'HHheeellooo INNNnnntterrnneet'" pops up.

[harshpatel-zccq]

Thanks Derek. Your skill to summarize topic is fantastic. Looking for more videos on data science related topics.

[AngaarUriakhil]

On the example at 24:20, shouldn't you be looking up alpha = 0.025, not 0.05? Since we are using alpha/2 in the formula and thats what we did for the normal distribution. Thanks.

[rubayetalam]

I have been looking for all these tutorial so Thanks a lot!