数据科学的统计和假设检验
Statistics and Hypothesis Testing for Data science
- 1. Introduction to Statistics
- 1. Introduction to Statistics and its importance
- 2. Explain the role of statistics in data analysis
- 3. Introduction to Python for Statistical Analysis
- 2. Introduction to Descriptive Statistics
- 1. Types of Data
- 2. Measures of Central Tendency
- 3. Measures of Spread
- 4. Measures of Dependence
- 5. Measures of Shape and Position
- 6. Measures of Standard Scores
- 3. Introduction to Basic and Conditional Probability
- 1. Introduction to Basic Probability
- 2. Introduction to Set Theory
- 3. Introduction to Conditional Probability
- 4. Introduction to Bayes Theorem
- 5. Introduction to Permutations and Combinations
- 6. Introduction to Random Variables
- 7. Introduction to Probability Distribution Functions
- 4. Introduction to Inferential Statistics
- 1. Introduction to Normal Distribution
- 2. Introduction to Skewness and Kurtosis
- 3. Introduction to Statistical Transformations
- 4. Introduction to Sample and Population Mean
- 5. Introduction to Central Limit Theorem
- 6. Introduction to Bias and Variance
- 7. Introduction to Maximum Likelihood Estimation
- 8. Introduction to Confidence Intervals
- 9. Introduction to Correlations
- 10. Introduction to Sampling Methods
- 5. Introduction to Hypothesis Testing
- 1. 1. Fundamentals of Hypothesis Testing
- 2. Introduction to T Tests
- 3. Introduction to Z Tests
- 4. Introduction to Chi Squared Tests
- 5. Introduction to Anova Tests