Dive into a comprehensive 5.5-hour tutorial on statistics for data science, covering both descriptive and inferential statistics. Learn key concepts such as population and sampling techniques, variable types and measurement scales, measures of central tendency and dispersion, probability, hypothesis testing, and various statistical distributions. Explore practical applications with Python, including outlier detection and inferential statistics implementation. Master essential topics like confidence intervals, z-tests, t-tests, chi-square tests, and correlation analysis. Gain valuable insights for data analysis and interpretation, preparing you for real-world data science challenges and interview questions.
Overview
Syllabus
Introduction
Descriptive Statistics
Inferential Stats
What is Statistics
Types of Statistics
Population And Sample
Sampling Teechniques
What are Variables?
Variable Measurement Scales
Mean, Median, Mode
Measure of dispersion with Variance And SD
Percentiles and Quartiles
Five number summary and boxplot
Gaussian And Normal Distribution
Stats Interview Question 1
Finding Outliers In Python
Probability, Additive Rule, Multiplicative Rule
Permutation And combination
p value
Hypothesis testing, confidence interval, significance values
Type 1 and Type 2 error
Confidence Interval
One sample z test
one sample t test
Chi square test
Inferential stats with python
Covariance, Pearson correlation, spearman rank correlation
Deriving P values and significance value
Other types of distribution
Taught by
Krish Naik
