Know each & every concept - Descriptive, Inferential Statistics & Probability become expert in Stats for Data Science
What you'll learn
- Looking for in-depth knowledge of Statistics for Data Science
- Each and every concepts like Measure of Central Tendency, Measure of Spread with various example
- Get the in-depth knowledge of Regression, Covariance Matrix, Karl Pearson Correlation Coefficient and Spearman Rank Correlation Coefficient
- Detailed understanding of Normal Distribution
- Understanding of Skewness, Kurtosis, Symmetric distribution and KDE
- Detailed knowledge on Basics of Probability, Conditional Probability
- Permutations and Combinations
- Combinatorics and Probability
- Understanding of Random Variables its variance and mean
- Various distributions like Binomial, Bernoulli, Geometric and Poisson
- Sampling Distribution and Central Limit Theorem
- Confidence Interval
- Margin of error
- T-statistic and Z statistic in detail
- Significance testing
- Type 1 and Type 2 Errors
- Comparing two proportions
- Comparing two means
- Introduction to Chi Squared Distribution
- Chi Square test for Homogeneity and association
- Advanced Regression
- Anova and FStatistic
Requirements
- Passion to Learn Statistics , Rest we will take care of it
Description
This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability.
We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work.
Extensive coverage of statistics in detail:
- The measure of Central Tendency (Mean Median and Mode)
- The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation)
- Regression and Advanced regression in details with Hypothesis understanding (P-value)
- Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examples
- Detailed understanding of Normal Distribution and its properties
- Symmetric Distribution, Skewness, Kurtosis, and KDE.
- Probability and its in-depth knowledge
- Permutations and Combinations
- Combinatorics and Probability
- Understanding of Random Variables
- Various distributions like Binomial, Bernoulli, Geometric, and Poisson
- Sampling distributions and Central Limit Theorem
- Confidence Interval
- Margin of Error
- T-statistic and F-statistic
- Significance tests in detail with various examples
- Type 1 and Type 2 Errors
- Chi-Square Test
- ANOVA and F-statistic
Who this course is for:
- Anyone looking for a career in Data Science and Machine Learning
- Anyone looking to learn Statistics from basics to Advanced
