Statistics, MS

Program Overview

The Master of Science in Statistics program prepares students for careers as practicing statisticians in various fields. It emphasizes theoretical and applied coursework, focusing on writing and communication skills. The program was designed in collaboration with industry professionals, ensuring alignment with current job market demands. Graduates are equipped with the knowledge and skills to succeed as professional statisticians, with career opportunities in data science, statistics, quantitative analysis, market research, biostatistics, and actuarial science.

Program Outline

Degree Overview:

Description:

The Master of Science in Statistics prepares students for careers as practicing statisticians in various fields, including business, government, and industry. The program emphasizes theoretical and applied coursework in statistics and advanced statistical techniques. The skills-based coursework focuses on writing and communication, preparing students with practical project experience. This program was designed in collaboration with industry professionals, aligning with current job market demands.

Objectives:

This program aims to:

• Equip students with the necessary knowledge and skills to succeed as professional statisticians.
• Provide a strong theoretical foundation in statistical principles and techniques.
• Develop strong analytical, problem-solving, and critical thinking skills.
• Enhance communication and presentation abilities through writing and project-based coursework.
• Foster collaboration with industry partners to ensure program relevance and student preparedness for real-world applications.

Outline:

Core Courses:

• CS 200W - Graduate Technical Writing: Develops technical writing proficiency, a crucial skill for effective communication in the field.
• MATH 163 - Probability Theory: Fundamentals of probability theory and methods, providing a strong foundation for statistical modeling and inference.
• MATH 164 - Mathematical Statistics: Statistical inference, including estimation and hypothesis testing, using mathematical rigor.
• MATH 167R - Statistical Programming with R: Introduction to the R statistical software, essential for data analysis and statistical computing.
• MATH 167PS - Introduction to Python Programming and SQL: Covers the basics of Python programming and SQL (Structured Query Language), enhancing data manipulation and analysis capabilities.
• MATH 261A - Regression Theory and Methods: Regression modeling techniques, including linear, nonlinear, and generalized linear models.
• MATH 269 - Statistical Consulting: Practical experience in statistical consulting, involving data analysis, interpretation, and effective client communication.

Electives or Specialization:

Students can choose between completing a specialization or selecting elective courses.

• Specialization in Machine Learning:
• MATH 250 - Mathematical Data Visualization
• MATH 251 - Statistical and Machine Learning Classification
• MATH 252 - Cluster Analysis
• One additional elective from:
• MATH 258 - Categorical Data Analysis
• MATH 264 - Bayesian Data Analysis
• MATH 267 - Computational Statistics
• Elective Courses:
• MATH 261B - Design and Analysis of Experiments
• Additional electives selected in consultation with the graduate advisor, up to 9 units.

Culminating Experience:

• Project Option: MATH 203, 288I, or 298, involving applied research projects or internships in statistics.
• Elective and Exam Option: One additional elective and a comprehensive written exam covering material from MATH 163, 164, and 261A.

Assessment:

The program utilizes various assessment methods to evaluate student learning:

• Coursework Performance: Regular assignments, quizzes, and exams assess understanding and application of concepts.
• Projects: Applied projects demonstrate practical skills and the ability to solve real-world problems.
• Presentations: Students present their research or project findings, fostering communication and presentation abilities.
• Comprehensive Exam (Elective and Exam Option): A written exam tests overall knowledge and understanding.

Teaching:

The program employs effective teaching methods:

• Expert Faculty: Renowned faculty with expertise in statistics teach courses, ensuring high-quality instruction.
• Interactive Lectures: Engaging lectures stimulate active learning and foster critical thinking.
• Hands-on Projects: Practical projects provide valuable experience and reinforce theoretical concepts.
• Research Opportunities: Students may engage in research projects under faculty guidance, enhancing research and analytical skills.
• Collaboration with Industry: Industry partnerships offer opportunities for applied research and internships, bridging theory with practical applications.

Careers:

Graduates of the MS Statistics program are equipped for a wide range of career opportunities:

• Data Scientist: Analyze and interpret data to derive meaningful insights.
• Statistician: Develop statistical models and methods to solve complex problems.
• Quantitative Analyst: Apply statistical techniques in financial analysis and risk management.
• Market Researcher: Conduct market research and analyze data to understand consumer behavior and market trends.
• Biostatistician: Apply statistical methods to analyze and interpret biological and health-related data.
• Actuary: Use statistical and mathematical principles to assess risk and design insurance products.