Statistics for Data Science
Master statistical concepts essential for data science and analytics. This comprehensive course covers probability theory, descriptive and inferential statistics, hypothesis testing, confidence intervals, and regression analysis.
Perfect for aspiring data scientists, analysts, researchers, and anyone looking to make data-driven decisions with confidence and precision.
What You'll Learn:
- Probability theory and distributions
- Descriptive and inferential statistics
- Hypothesis testing and p-values
- Confidence intervals and estimation
- Linear and multiple regression analysis
- Statistical modeling for data science
Course Curriculum
Module 1: Probability Fundamentals
Probability theory, random variables, probability distributions
Module 2: Descriptive Statistics
Measures of central tendency, variability, data visualization
Module 3: Inferential Statistics
Sampling distributions, central limit theorem, estimation
Module 4: Hypothesis Testing
T-tests, chi-square tests, ANOVA, p-values and significance
Module 5: Regression Analysis
Simple and multiple regression, model evaluation, predictions
Meet Your Instructor
Dr. Rachel Kim
Ph.D. in Statistics with extensive experience in data science consulting. Dr. Kim has worked with Fortune 500 companies and has 10 years of teaching experience helping students master statistical concepts.
Student Reviews
"This course gave me the statistical foundation I needed for my career in data analytics. Highly recommended!"
"Excellent course with practical examples. The instructor makes complex statistical concepts easy to understand."