Statistics Minor
The Statistics Minor program at University of Stanford provides students with foundational and advanced knowledge in statistical theory, applied mathematics, and data analysis. Students study probability, regression, statistical modeling, and computational techniques, developing analytical, quantitative, and problem-solving skills. The curriculum emphasizes data analysis, statistical modeling, probability theory, and quantitative reasoning, equipping students to interpret data, solve complex problems, and apply statistical methods across diverse fields.
Over four years of full-time study, learners progress from introductory courses in probability, descriptive statistics, and applied mathematics to advanced topics in regression analysis, multivariate statistics, and data-driven modeling. Students engage in hands-on projects, research assignments, and computational exercises, gaining practical experience in analyzing datasets and drawing evidence-based conclusions. As students advance, they strengthen their expertise in regression analysis, computational statistics, data visualization, and applied modeling, preparing them for careers in finance, research, analytics, or graduate studies in mathematics, statistics, and related disciplines. The program also fosters skills in critical thinking, problem-solving, and quantitative reasoning.
Key information
Key Facts
- Program Title: Statistics Minor
- Degree Type: Bachelor of Science
- Duration: 4 years
- Mode of Study: Full-time, On Campus
- Application Deadline: 05 January 2026
- Location: Stanford University, United States
- Field of Study: Applied Mathematics
- Language of Instruction: English
Program Structure
Year 1 – Foundations of Statistics and Applied Mathematics
· Introduction to Probability and Statistics
· Calculus and Linear Algebra
· Academic Writing and Research Skills
· Foundations of Applied Mathematics
Year 2 – Developing Analytical and Computational Skills
· Intermediate Statistical Methods
· Data Analysis and Visualization
· Regression and Modeling Techniques
· Research Methods in Statistics
Year 3 – Advanced Study & Applied Projects
· Multivariate Statistics and Predictive Modeling
· Computational Statistics and Algorithms
· Applied Data Projects
· Electives in Mathematics, Computer Science, or Economics
Year 4 – Honors Thesis & Professional Integration
· Honors Thesis / Independent Research
· Advanced Seminar in Statistics
· Applied Data Analysis Projects
· Academic Portfolio and Presentation Development
Career Opportunities
Graduates of this program are prepared for careers that require strong analytical, computational, and problem-solving skills. Many find opportunities in data analytics and business intelligence, financial analysis and risk management, research and academia, and computational modeling and consulting, applying their expertise to analyze datasets, develop models, and support evidence-based decision-making. The program equips students with statistical, mathematical, and computational skills highly valued in research, finance, technology, and analytics-driven industries. Additionally, graduates are well-prepared for graduate studies in statistics, applied mathematics, data science, or related fields, providing pathways to professional and academic leadership roles in data-focused careers.
Why Choose This Program
This program is ideal for students who wish to develop strong quantitative, analytical, and computational skills while applying statistics to real-world problems. Coursework integrates probability, regression, statistical modeling, data visualization, and applied mathematics, allowing learners to analyze complex datasets and derive meaningful insights. Students participate in research projects, applied assignments, and computational exercises, developing strong critical thinking, problem-solving, and technical skills. The curriculum emphasizes applied learning, interdisciplinary study, and data-driven decision-making, preparing graduates for professional or academic pathways that demand expertise in statistics and quantitative analysis. This preparation is further strengthened through comprehensive statistical and data analysis training, equipping students with the knowledge and skills to succeed in analytics, research, and data-intensive careers.
Contact Information
For further information, please contact the admissions office at:
Phone: +1 650 000 0000
Email: admissions@standord.edu
Address: University of Standord, 450 Serra Mall, Stanford, CA 94305, United States
