Statistics

The Statistics major is a designated capstone major. Undergraduate students work in small groups to solve problems posed by real community-based or campus-based clients. The capstone gives students an opportunity to put into practice concepts and ideas that otherwise might remain theoretical and/or abstract and to synthesize the many topics they have studied. Students should demonstrate ability to restate investigative questions in terms of statistical models or algorithms, find appropriate research literature to support their work, relate theoretical concepts to real-world problems, and clearly communicate their results to nontechnical audiences.

Statistics Pre-major

Incoming freshman and transfer students may be admitted as Statistics premajors on acceptance to UCLA. Premajor students must apply for the major after completing Mathematics 33A, Statistics 20 and one course from Statistics 10 through 14, with grades of C or better, and a grade-point average of 2.5. Any students who meets the premajor requirements may declare the major with the undergraduate adviser in 8117A Math Sciences, (310) 206-3742.

Preparation for the Major 

Required: Mathematics 31A, 31B, 32A, 32B, 33A, Statistics 10 and Statistics 20 (italicized requirements are as of Fall 2014; prior to Fall 2014 one course from Statistics 10 through 14 was required instead). Each course must be completed with a grade of C or better. Students who repeat any preparation course more than once are automatically denied admission to the major.

MAJOR REQUIREMENTS 

• INTRODUCTION TO PROBABILIT
• INTRODUCTION TO MATHEMATICAL STATISTICS
• LINEAR MODELS
• INTRODUCTION TO DATA ANALYSIS REGRESSION
• INTRODUCTION TO DESIGN AND ANALYSIS OF EXPERIMENT
• INTRODUCTION TO REGRESSION AND DATA MINING
• INTRODUCTION TO COMPUTATIONAL STATISTICS WITH R
• INTRODUCTION TO COMPUTATION AND OPTIMIZATION 
• INTRODUCTION TO MONTE CARLO METHODS
• PRACTICE OF STATISTICAL CONSULTING PART 1
• PRACTICE OF STATISTICAL CONSULTING PART 2

Requirements

• To be considered for admission to UCLA, international students must have completed secondary school with a superior average in academic subjects and have earned a certificate of completion which enables the student to be admitted to a university in the home country.
• All freshman applicants are expected to take the following tests: The ACT Assessment plus the ACT Writing Test. - or - The SAT Reasoning Test. Note: you must complete the essay portion of the SAT. We will not accept your scores if you opt not to write the essay.
• UCLA requires records from all secondary schools attended, showing subjects taken and grades received as well as any national or government certificates earned. Documents of all college or university coursework must also be sent in the original language, along with English translations. Do not send these documents now. If you apply and are admitted to UCLA, your admission packet will contain instructions on how to send your original records to us.
• Test of English as a Foreign Language (TOEFL)
• A competitive score is above 100 (with sub-scores above 22).
• International English Language Testing System (IELTS)
• A competitive score is 7 or higher.

Scholarships

UCLA does not award scholarships or financial aid to undergraduate students who are not citizens or permanent residents of the United States. International students must prove that they have sufficient funds available to them to pay for their educational and living expenses. For example, students admitted to Fall Quarter 2016 will need a minimum of about $60,000 (with an additional $5,000 recommended for additional personal expenses, contingencies and summer expenses). This minimum amount usually grows each year.

UCLA also requires that all international students on non-immigrant visas have adequate medical insurance during all periods of enrollment. More information about these requirements will be sent to students when they are admitted.

The University of California offers a comprehensive program in Statistics designed to equip students with a solid foundation in statistical theory, computational methods, and data analysis skills. This program aims to prepare graduates for diverse careers in research, industry, and academia by emphasizing both the mathematical underpinnings and practical applications of statistics. Students enrolled in the program have the opportunity to develop strong analytical thinking and problem-solving skills through coursework that includes probability, statistical inference, experimental design, regression analysis, and multivariate statistics. The curriculum is often complemented by programming courses in R, Python, and other statistical software, enabling students to handle complex data sets and perform advanced analyses.

Research plays a significant role in the program, with students encouraged to participate in ongoing projects and collaborate with faculty members who are actively engaged in cutting-edge statistical research. The program also offers various seminars, workshops, and internships designed to give students real-world experience and professional exposure. Graduates of the program are well-prepared to pursue advanced degrees in statistics or related fields such as data science, biostatistics, economics, and public health. Many alumni find employment in government agencies, private industry, tech companies, and healthcare organizations, where they contribute to data-driven decision-making processes.

The program is characterized by its multidisciplinary approach, integrating insights from mathematics, computer science, and domain-specific disciplines to address complex problems. The university’s resources, including state-of-the-art computing facilities and extensive libraries, support students’ research and learning activities. Faculty members are recognized experts in their areas, providing mentorship and guidance throughout students' educational journeys. The program’s flexible structure often allows students to tailor their coursework and research focus according to their interests and career goals, whether in theoretical statistics, computational methods, or applied areas like machine learning and data analysis.

Admittance into the program requires a strong academic background in mathematics and quantitative skills, with prerequisites typically including calculus, linear algebra, and introductory statistics. The program is usually delivered both as a Bachelor’s degree in statistics or a related major, and as a graduate master’s or doctoral program, providing multiple entry points for students at different stages of their career. Overall, the University of California’s statistics program is designed to foster a rigorous understanding of statistical methodologies and foster innovation in handling large and complex data sets, thus enabling graduates to thrive in various sectors that rely on data science and statistical expertise.