lsa.umich.edu

What is Statistics at U-M LSA?

Statistics at U-M LSA is the University of Michigan's statistics program within the College of Literature, Science, and the Arts, as presented on Statistics | U-M LSA. It covers applied statistics and data science study, taught by faculty with research opportunities for students.

Who it suits

• Undergraduates considering a statistics or data science major or minor.
• Students who want to combine statistical methods with another field, such as economics, biology, or social science.
• Prospective graduate students looking at applied statistics programs.

H3: What to expect The page emphasizes applied work and data science alongside core statistical theory, plus faculty expertise and research participation. That mix suits students who want to analyze real data, not only prove theorems.

H3: Practical next step Check the program's degree requirements and course sequence before applying, then compare with the broader university's statistics offerings. If you are choosing a major, weigh whether you prefer an applied, data-focused path or a more theoretical mathematics track.

How do I apply to the statistics program at U-M LSA?

Applying to Statistics at U-M LSA means first deciding which program you're applying to, because the department hosts several distinct tracks with separate application routes. The page itself is a department hub, so treat it as your starting point for finding the right program page, not as the application portal.

Pick your program level first

Practical next step

Go to Statistics | U-M LSA and open the program page for your level — that page carries the actual requirements, deadlines and application link. Graduate applicants should also check Rackham Graduate School, which handles graduate admissions for most U-M programs, and confirm whether the statistics program uses Rackham's application or its own.

What to prepare

• Transcripts showing calculus, linear algebra and prior statistics coursework — the single biggest factor in whether you're competitive.
• Statement of purpose that names specific research areas or faculty where relevant.
• Letters of recommendation, ideally from quantitative instructors who can speak to your mathematical preparation.
• Test scores only if the program page still lists them as required; many programs have dropped them, so verify rather than assume.

A concrete scenario

If you're a current U-M undergraduate in another major, you don't reapply to the university — you declare or apply to the major internally through LSA, usually after completing the prerequisite math and statistics courses. That's a different process from an outside applicant, and the department page will route you accordingly.

When in doubt, email the department's undergraduate or graduate coordinator with your specific situation; they can tell you in one reply which application and deadline apply to you.

What courses are required for a statistics degree at U-M LSA?

The Department of Statistics at the University of Michigan's College of Literature, Science, and the Arts (LSA) does not publish a single universal course list for "a statistics degree," because requirements differ by program. The department offers several distinct pathways, and the courses you take depend on which one you choose.

Main pathways and their focus

• Statistics major (LSA): Built on a calculus and linear algebra foundation, then core probability and mathematical statistics, followed by applied modeling and computing courses.
• Data science programs: Blend statistics with computer science and domain applications, so expect additional programming and data-handling coursework.
• Applied statistics master's-level programs: More advanced probability, statistical theory, and methods, often with electives in areas like machine learning or biostatistics.

Typical course sequence

Most students move through roughly this arc:

1. Prerequisites: Calculus I–III and linear algebra.
2. Core theory: Probability and mathematical statistics.
3. Methods and computing: Regression, statistical computing, experimental design or sampling.
4. Electives: Upper-level courses in areas such as Bayesian methods, time series, or machine learning.
5. Capstone or research: Some programs require a project, thesis, or research experience.

How to get the exact list

Because the precise requirements vary by degree and by catalog year, check the department's own program pages and the LSA course catalog for your intended major. The department site at Statistics | U-M LSA is the right starting point, and the university's course catalog will give you the authoritative, year-specific requirements.

A practical next step: decide whether you want the general statistics major or a data science track, then request an advising appointment. Advisors can map your math placement and transfer credits onto the current requirement sheet, which is far more reliable than any general summary.

What research opportunities in statistics are available for undergraduates at U-M LSA?

Undergraduate statistics students at U-M LSA can get involved in research through several routes, ranging from formal programs to informal work with faculty.

The main paths into research

• Faculty research assistantships. Professors in the department regularly bring undergraduates onto their projects, often after a student has taken a few core courses and done well. This is the most direct route and usually starts with a conversation.
• Independent study or directed research courses. These let you earn credit while working on a defined project under a faculty mentor, which is useful if you want a structured commitment during the semester.
• Summer research programs. Summer is the most common window for intensive work, both through university-run programs and external research experiences for undergraduates (REUs) at other institutions.
• Capstone or thesis options. Some students build a sustained research project into their final year, which can be valuable if you're considering graduate school.
• Research groups and labs. Applied statistics and data science work is often collaborative, so joining an existing group can be easier than pitching a solo project.

What the work actually looks like

Undergraduate statistics research rarely means proving theorems alone. More often it involves cleaning and analyzing real datasets, writing code in R or Python, running simulations, reproducing or extending published methods, and helping prepare results for papers or presentations. You may also present at a campus symposium or a regional conference.

How to decide which fits you

Practical next step

Identify two or three faculty whose research genuinely interests you, read one recent paper of theirs, and email them a short note: who you are, the courses you've taken, the specific paper you read, and what you'd like to learn. That specificity is what gets replies. If you're not sure where to start, ask your academic advisor or a course instructor which faculty currently have room for undergraduates, and check whether the department hosts an undergraduate research info session early in the term.

For program-level details, start with Statistics | U-M LSA.

What career paths do graduates of U-M LSA statistics programs pursue?

Graduates of U-M LSA statistics programs typically move into roles where they turn data into decisions, rather than into a single narrow job title. The department's applied statistics and data science focus points toward practical, computation-heavy work, so alumni tend to spread across industry, government, healthcare, and academia.

Common destinations

• Data science and analytics: data scientist, machine learning engineer, analytics consultant, or product/data analyst at technology, retail, finance, and consulting firms.
• Biostatistics and health research: clinical trial design, epidemiological modeling, and outcomes research at hospitals, universities, and public health agencies.
• Finance and insurance: quantitative analyst, risk modeler, and actuarial roles, where statistical modeling and uncertainty quantification are central.
• Government and policy: statistician or research methodologist at agencies that run surveys, censuses, and program evaluations.
• Academia and further study: master's and PhD programs in statistics, biostatistics, data science, or related fields, leading to research and teaching careers.

How to choose among them

The deciding factor is usually the type of data and the cost of being wrong. If you enjoy messy, fast-moving data and shipping models into products, industry data science fits. If you want rigorous inference with high stakes, biostatistics or government statistics is a better match. If you like mathematical foundations, a graduate degree keeps more options open.

A useful next step is to look at the department's research areas and course listings, then compare them with the skills listed in job postings you would actually apply for. For broader context on the field, see American Statistical Association and University of Michigan.

How does the statistics program at U-M LSA compare to similar programs at other universities?

The University of Michigan's LSA Statistics program is best understood as a strong public-university option that blends rigorous statistical theory with applied data science, rather than as a single program competing on one axis. Statistics | U-M LSA describes top-ranked applied statistics and data science programs, expert faculty, and diverse research opportunities. That combination—theory plus application plus research access—is the usual basis for comparing it with peers.

What actually differs between programs

Most statistics programs at this level cover similar core material: probability, inference, linear models, computing. The meaningful differences show up in:

• Theory vs. applied balance. Some departments lean heavily mathematical; others emphasize statistical computing, consulting, and data science. U-M LSA's stated focus on applied statistics and data science places it toward the applied end, while still being a traditional statistics department.
• Research access for undergraduates. At large public universities, getting into a lab often depends on initiative. Programs with structured undergraduate research pipelines make this easier.
• Class size and faculty contact. Large publics typically have bigger introductory lectures; smaller privates or honors tracks offer more seminar-style teaching.
• Adjacent strengths. A statistics program is more useful when the university around it is strong in the fields you want to apply statistics to—biology, economics, public health, engineering, social science.

How to compare concretely

A practical next step

Pick three or four peer programs you're considering—typically other large public research universities and a couple of smaller private ones—and compare them on the same four questions: What theory is required? What computing is required? Can undergraduates join research? What do graduates do? Then look at specific course catalogs rather than program marketing pages, since catalogs reveal the real requirements.

If you want a concrete scenario: a student aiming for a statistics PhD should prioritize theory depth and research access; a student aiming for a data science job in industry should prioritize computing, applied projects, and internships. The same program can be a good or poor fit depending on which of those you are.