Why Statistics?
Statistics is an increasingly important discipline, driven by the continuing proliferation of complex and rich data and by the evolution of the ways in which data are collected and used. Statistical analysis plays a central role in all disciplines that include making evidence-based decisions.
Studying statistics gives you the conceptual foundations and the necessary toolbox to intelligently extract information from the data and make insightful conclusions in any field of application. Having a deep understanding of theoretical, applied and ethical aspects of statistical analysis will make a difference in your career as well as giving you a solid background to pursue graduate studies.
To learn more about careers in Statistics, visit This is Statistics!
About the Program
The undergraduate program is organized around three tracks:
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Mathematical statistics track: it emphasizes the mathematical foundations of statistical methods
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Data Science track: it emphasizes the link between statistics and computer science
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Applied Statistics track: it allows students to explore the use of statistical methods in specific scientific domains.
All tracks share the same set of admission and core requirements, differing on the structure of their breath requirements. Students do not need to select a particular track when applying to the program, but they must do so before graduation. To learn more about the admissions process visit the admissions page, while you can learn more about the different tracks in the Statistics B.S. Tracks page.
The system of three tracks and the Honors Program allow a great deal of flexibility in the curriculum: the major prepares students for graduate studies, as well as to be competitive on the job market and it encourages students interested in pursuing a double major or double degree to do so. Students also have the opportunity of engaging in internships as an integral part of their training.
The program is characterized by an active community of students and Faculty that engages in several extra-curricular activities including the Directed Reading Program, DataFest and the Statistics and Probability Association.
What will you learn?
The Statistics major at the University of Washington combines a strong mathematical foundation with statistical thinking, inference, computation, and applied modeling. Students graduating with this major should be prepared for careers in science, industry, government positions, or for further academic studies in statistics. Specifically, majors should have the following skills and abilities:
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Frame a scientific question and understand how to address it by appropriately designing a study, collecting data, performing a statistical analysis, and interpreting.
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Develop a wide toolkit for statistical learning, modeling, and prediction, and understand the scope of each method.
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Use statistical software to explore, summarize, analyze, and model data by numerical, graphical, or other means in a reproducible manner.
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Apply the fundamentals of probability theory to assess the quality of estimators, statistical tests, and models, as well as their uncertainty.
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Critically evaluate the use of statistics in scientific literature and beyond, including the ability to understand reliability, replicability, and ethical implications of such work.
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Communicate effectively about data, statistical methods, and results to both technical and non-technical audiences.
What will you do when you graduate?
Based on the exit survey in years 2020-2023, a month after graduation roughly 50% of our students pursue further education in Statistics and related fields (Data Science, Machine Learning, etc.) at several prestigious institutions in the US, and another 40% find employment in several different sectors including the Tech industry (Amazon, Zillow, Micrsoft, Meta), consultancy (EY, Deloitte), finance and insurance (Clear Street, Mutual), healthcare (Epic System Corporation), sports (Seattle Mariners) and education (Trio, National Center of Analysis of Longitudinal Data in Education Research). Job titles vary and include Data Scientist, Data Analyst, Financial Analyst, Software engineers and developers.
Alumni
