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UMN Biostatistics

An introduction to the Division of Biostatistics at the University of Minnesota
by

Julian Wolfson

on 7 October 2015

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Transcript of UMN Biostatistics

Biostatistics @
University of Minnesota

What is biostatistics?
The development and application of statistical methods for solving scientific problems in human biology, public health, and medicine.
What do biostatisticians do?
Develop new statistical methods.
Collaborate with biomedical scientists to help plan, design, and analyze clinical and population studies.
Act as consultants for government, industry, and legal proceedings.
Teach and fulfill administrative duties.
About Biostatistics
What kinds of problems do biostatisticians tackle?
Disease mapping:
What does the distribution of asthma cases in the United States tell us about environmental risk factors for asthma?
Personalized medicine:

Can we target treatments at groups/individuals with specific risk factors?
Statistical genetics:
Which combinations of genes put you at highest risk for heart disease?
Diagnostic testing:
How effective are population screening programs for breast and prostate cancer?
What kinds of skills do biostatisticians need?
Mathematical/computing skills
Interpersonal/communication skills
Curiosity about science

"The best thing about being a (bio)statistician is that you get to play in everyone's backyard."
- J. Tukey
UMN Biostatistics
Division of Biostatistics, School of Public Health
Minneapolis, Minnesota
26 faculty, 65 staff, ~60 students (40 PhD, 25 MS/MPH)

Collaborations with research units across campus and around the world.

Involved in grants totaling over $150 million.

Academic programs
Offering the MPH, MS, and PhD
MPH
2 year program
Coursework in applied (mostly) and theoretical (bio)statistics, plus public health
Master’s Thesis, Presentation, and Practicum
Graduates are prepared to work as interdisciplinary practitioners of statistics in public health.
MS
2 year program
Coursework in applied (mostly) and theoretical (bio)statistics, plus public health
Written Exam, Master’s Thesis and Presentation
Graduates are prepared to work as statistical collaborators and consultants in public and private research institutions.

PhD
4-5 year program
Courses in theoretical and applied (bio)statistics
Written and Oral Exams
PhD Thesis and Defense
Graduates are prepared to work as independent researchers at universities, government institutions (eg. NIH, CDC) and private companies.
Undergraduate students whose eventual goal is to obtain a PhD are encouraged to apply directly to our PhD program.

Applying
Basic math background (same for all programs):
Calculus (3 semesters, up to multivariable)
Linear Algebra (1 semester)

Highly recommended:

Courses in probability and mathematical statistics

Useful for PhD admission:
Real analysis
Pre-requisites
Applicant pool, Fall 2015
Why Minnesota?
A highly ranked program
In recent surveys, UMN has ranked #5-7 out of >40 biostatistics programs in U.S.
World-class faculty
Leading experts in a wide variety of areas, with particular strength in spatial statistics, Bayesian analysis, clinical trials, and statistical genetics.
Dynamic students
UMN students have won prestigious national and international awards for their research.
Some Masters, most PhDs have at least one peer-reviewed scientific publication before graduation.
More information
http://www.facebook.com/umnbiostat
@umnbiostat
http://www.biostat.umn.edu
Faculty on Twitter:
@DrJWolfson
@JSKoopmeiners
@docvock
Sally Olander, major coordinator:
sally@biostat.umn.edu
209 total applicants

MS/MPH
90 applicants, 53 accepted (59%)
Averages for accepted students
3.64 GPA
165/155/4 GRE Q/V/AW

PhD
119 applicants, 30 accepted (25%)
Averages for accepted students
3.84 GPA
166/162/4 GRE Q/V/AW

A "few" faculty research interests:
Bayesian analysis, cancer, cardiovascular disease, causal inference, clinical trials, computational biology, diagnostic testing, dynamic treatment regimes, HIV/AIDS, machine learning, medical imaging, pulmonary disease, spatial statistics, sports statistics, statistical computing, statistical genetics, survival analysis.
A diverse student body spanning multiple cultures and countries.
Full transcript