Overview

This course covers the following learning outcomes and goals: understanding the fundamentals of statistics for data science, learning about different types of distributions, exploring concepts like mean, median, mode, random variables, and central limit theorem, understanding correlation and covariance, mastering techniques for handling outliers and feature scaling, gaining knowledge of hypothesis testing and confidence intervals, and applying statistical concepts in real-world scenarios. The course teaches individual skills such as interpreting Gaussian and Log Normal distributions, calculating covariance, understanding the difference between population and sample, applying Z-score and IQR for outlier detection, performing hypothesis testing using P-value, and utilizing different sampling techniques. The teaching method of the course includes lectures, real-world examples, live sessions, interview question discussions, and hands-on practice with Python for statistical analysis. The intended audience for this course includes self-starters interested in data science, beginners in statistics, data science enthusiasts looking to enhance their statistical skills, and professionals seeking to apply statistical concepts in machine learning and data analysis.

Syllabus

How to Learn Statistics for Data Science As A Self Starter- Follow My Way.
Introduction To Statistics And Its Types For Starters.
Population vs Sample in Statistics.
Gaussian distribution or Normal Distribution in statisctics.
Log Normal Distribution in Statistics.
Covariance in Statistics.
STATISTICS- Mean, Median And Mode Explained Easily.
STATISTICS- Population VS Sample and it's Importance.
STATISTICS- What are Random Variables and It's Types and its Importance?.
STATISTICS- Gaussian/ Normal Distribution.
STATISTICS- What is Central Limit Theorem?.
STATISTICS- Chebyshev's InEquality.
Statistics- What is Pearson Correlation Coefficient? Difference between Correlation and Covariance.
Spearman's rank correlation coefficient- Statistics.
Statistics-Finding Outliers in Dataset using Z- score and IQR.
Standardization Vs Normalization- Feature Scaling.
What Is P Value In Statistics In Simple Language?.
Statistics-Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode.
Stats Interview Series #1- Asked In Interview.
Stats Interview Series #2-Asked In Interview.
Confidence Intervals In Statistics- Part 1.
Bernoulli distribution- Mean, Variance And Standard Deviation OF Bernoulli distribution.
Stats Interview Series #3-Asked In Interview #shorts⭐ ⭐⭐⭐⭐⭐⭐⭐.
5 Number Summary And How To handle Outliers Using IQR-Statistics.
Different Type Of Sampling Techniques With Examples| Statistics Interview Question.
Whether We Should Reduce False Positive Or Negative In Confusion Matrix-Machine Learning Interviews.
Z Score And Its Applications- Important Stats Interview Question.
Power Law Distribution And Its Examples And Application- Statistics Interview Question.
How To Perform Hypothesis Testing-Confidence Interval|Z Test Statistics| Derive Conclusion- Part 1.
All Important Topics In Probability For Data Science In 1 Video.
Permutation And Combination Easily Explained.
Why Sample Variance is Divided by n-1.
Covariance,Pearson Correlation And Spearman Correlation Coefficient With Real World Examples.
We Use Stats Everywhere!!.
Live Day 1- Introduction To statistics In Data Science.
Live Day 2- Basic To Intermediate Statistics.
Live Day 3- Intermediate Statistics With Python In Data Science.
Live Day 4- Advance Statistics With Python In Data Science.
Live Day 5- Advance Statistics With Python In Data Science.
Live Day 6- Advance Statistics With Python In Data Science.
Live Day 7- Summarizing Statistics With Python In Data Science.
How To Calculate P Value In Hypothesis Testing.