Course Description
Fundamental concepts of data representation and organization, measures of central tendency, elementary probability theory, frequency distributions, basic sampling theory, hypothesis testing, correlation and regression, Chi-square tests and t-tests. Students may use currently available software to analyze data. This course serves as an introduction to fundamental statistical concepts and their practical implementation using the R programming language. This course aims to equip students with the essential statistical knowledge and computational skills necessary for data analysis, interpretation, and visualization using R. .
Course Outcomes
At the end of the course students are expected to:
- Understand key statistical concepts, including probability, descriptive statistics, hypothesis testing, confidence intervals, correlation, and regression analysis, and recognize their foundational role in modern data analytics and AI applications.
- Understand and identify various probability distributions and use their associated functions in R, and interpret their relevance in modeling uncertainty across domains such as cybersecurity and machine learning.
- Demonstrate proficiency in using the R programming language for basic statistical analysis, data manipulation, and visualization using packages such as ggplot2.
- Â鶹ֱ²¥ statistical methods in R to analyze datasets, including examples from domains like cybersecurity and digital forensics, business, health, and social sciences, and interpret results to draw meaningful, data-informed conclusions.
- Productively use the R statistical software package and extend R by installing and using add-on packages.
