Faculty: Humanities and Social Science This major focuses on the collection, analysis, interpretation, and presentation of numerical data. Students explore key areas such as statistical theory, probability, data analysis, experimental design, and statistical computing. The program emphasizes the application of statistical methods to solve real-world problems in various fields, including business, healthcare, social sciences, and engineering. Graduates are prepared for careers as statisticians, data analysts, research scientists, and consultants. **Learning Objectives:** - Understand the fundamentals of statistical theory and probability. - Develop skills in data collection, analysis, and interpretation. - Learn techniques for experimental design and statistical computing. - Explore the application of statistical methods in various fields. - Understand the role of statistics in decision-making and problem-solving. - Analyze challenges and opportunities in statistical analysis. - Develop teamwork and problem-solving skills for statistical projects and research. **Major Outline:** 1. **Introduction to Statistics** - Overview of statistics, key concepts, and applications. - Fundamentals of descriptive statistics and data visualization. 2. **Probability Theory** - Principles of probability, including random variables, distributions, and probability rules. - Techniques for understanding and applying probability theory. 3. **Statistical Inference** - Fundamentals of statistical inference, including estimation, hypothesis testing, and confidence intervals. - Techniques for making inferences from sample data. 4. **Data Analysis** - Methods for data analysis, including regression analysis, ANOVA, and multivariate analysis. - Techniques for analyzing and interpreting data from various sources. 5. **Experimental Design** - Principles of experimental design, including randomized experiments, factorial designs, and response surface methodology. - Techniques for designing and analyzing experiments. 6. **Statistical Computing** - Fundamentals of statistical computing, including programming languages (e.g., R, Python) and software tools. - Techniques for performing statistical analyses using computational methods. 7. **Applied Statistics** - Application of statistical methods in various fields, including business, healthcare, social sciences, and engineering. - Techniques for solving real-world problems using statistical analysis. 8. **Research Methods in Statistics** - Principles of research methods in statistics, including study design, data collection, and analysis. - Techniques for conducting and evaluating statistical research. 9. **Capstone Project in Statistics** - Real-world project to apply learned skills in data analysis, experimental design, or statistical computing. - Techniques for delivering a comprehensive solution to statistical challenges. 10. **Electives in Statistics** - Specialized courses in areas such as time series analysis, survival analysis, and machine learning. - Opportunities to explore advanced topics and specializations in statistics. **Assessment Methods:** - Statistical analyses, probability theory applications, data analysis projects, experimental design reports, statistical computing assignments, applied statistics projects, research methodology papers, capstone projects, group projects, and internships. **Recommended Textbooks:** - "Introduction to the Practice of Statistics" by David S. Moore, George P. McCabe, and Bruce A. Craig. - "Probability and Statistics" by Morris H. DeGroot and Mark J. Schervish. - "Applied Linear Statistical Models" by Michael H. Kutner, Christopher J. Nachtsheim, John Neter, and William Li. - "Design and Analysis of Experiments" by Douglas C. Montgomery. - "R for Data Science" by Garrett Grolemund and Hadley Wickham. **Prerequisites:** Basic knowledge of mathematics, particularly algebra and calculus. Suitable for students interested in statistics, data analysis, research, and related fields. **Major Duration:** Typically 4 years for a bachelor's degree, including coursework, projects, and internships. Advanced degrees may take additional years. **Certification:** Graduates may receive a degree in Statistics and pursue professional certifications such as Certified Statistician or Data Scientist. **Target Audience:** Aspiring statisticians, data analysts, research scientists, consultants, and professionals seeking to specialize in statistical analysis and data science. This major equips students with the statistical knowledge and analytical skills needed to excel in various fields, solving real-world problems through statistical analysis and data interpretation.