This course provides a comprehensive introduction to statistical analysis with a strong focus on its practical applications in data science. Students will learn how to apply statistical techniques to analyze, interpret, and draw conclusions from real-world datasets. The course emphasizes both theoretical understanding and hands-on experience with statistical tools commonly used in the industry, including descriptive and inferential statistics, hypothesis testing, correlation, regression analysis, and probability distributions. By the end of the course, students will be able to apply statistical reasoning to support data-driven decisions and build a solid foundation for more advanced topics in machine learning and predictive analytics.
Topics Covered:
- Introduction to statistics and its role in data science
- Types of data: categorical vs. numerical
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (range, variance, standard deviation)
- Probability theory and distributions (normal, binomial, Poisson)
- Sampling methods and data collection
- Hypothesis testing and confidence intervals
- Correlation and causation
- Simple and multiple linear regression
- Introduction to statistical software/tools (e.g., Python, R, or Excel)
Who Is This Course For?
This course is ideal for:
- Aspiring data scientists and analysts seeking a solid foundation in statistics
- Professionals transitioning into data-related roles
- University students in computer science, business, engineering, or related fields
- Anyone looking to strengthen their statistical thinking for real-world data applications
- Beginners with a basic understanding of math (algebra level) and an interest in data