COURSE OVERVIEW
Introduction to Statistics
Importance and Applications of Statistics
types of Statistics
Qualitative vs Quantitative
Data Collection and Sampling
Population and Sample
Sampling Techniques
Data Organization and Representation
Graphical Representation
Mean
Median
Mode
Measures of Dispersion
Variance
Standard Deviation
What is Skewness?
What is Kurtosis?
Correlation and Regression
Probability and its components
Hypothesis Testing
Null and Alternate Hypothesis
Type I and Type II Errors
Z-test
T-test
Chi-Square test
Analysis of Variance (ANOVA)
F-test
Simple Linear Regression
Course Outcome
By the end of this course, you’ll be able to:
- Covers basics of descriptive & inferential statistics.
- Introduces probability, distributions, and sampling.
- Focus on hypothesis testing and statistical inference.
- Teaches correlation, regression, and model validation.
- Hands-on application using Python with real datasets.
- Builds statistical foundation for machine learning and AI.
