Admissions and Requirements for the Medical College of Wisconsin Biostatistics and Data Science MA Program
Individuals interested in the Master of Arts degree program must apply on the application webpage. The MCW Graduate School operates on a rolling admissions basis, which means that we accept applications and related materials at any time. Applicants are encouraged to submit their applications in advance of the priority deadline to ensure that they receive full consideration. The priority deadline for fall admission is March 1 followed by rolling admissions through a final deadline of July 1. Students are admitted once per year. No GRE is required for admission to the program.
Admissions and Tuition
Minimum Admission Requirements
Students should satisfy the minimum requirements for admission:
- Baccalaureate degree - official transcripts required
- Prior coursework in calculus (including integrals, such as Calculus II), probability and/or statistics, linear/matrix algebra, and computer programming experience
- An overall grade point average of 3.0 or better
- Three letters of recommendation
- Applicants who studied overseas or via an online U.S.-based institution are required to take a Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) and make arrangement for an official score report to be sent directly to the Graduate School. A TOEFL score is 100 or higher or a band score of 6.0 or higher on the IELTS is ideal. Our Institution Code is 1519.
Tuition Information
If you have questions regarding tuition or your account, please contact the Office of Student Accounts, at (414) 955-8172 or mcwtuition@mcw.edu. Please refer to the All Student Handbook (PDF) for tuition payment policies and information.
Master’s, Certificate & Non-Degree Students Students seeking financial aid for MPH, MS or MA degree programs, visit the Financial Aid Office website.Current MCW Employees
Tuition Course Approval Form - Human Resources (PDF)
Late Fees
There is a $250 late payment fee for tuition not paid on time according to the Tuition Payments policy in the All Student Handbook (PDF)
Key Areas of Focus
Prospective students aiming for admission into the Biostatistics Master of Arts program at the Medical College of Wisconsin (MCW) should focus on several key areas to enhance their competitiveness:
Strong Academic Background:
Maintain a strong academic record, particularly in mathematics, statistics, computer science, and related quantitative disciplines. Completing coursework in calculus, linear algebra, probability, and statistics can provide a solid foundation for graduate-level studies in biostatistics.
Relevant Coursework:
Take relevant coursework in biostatistics, epidemiology, data analysis, and research methods to demonstrate a strong interest and aptitude in the field. Courses in programming languages such as R or Python can also be beneficial for acquiring essential data analysis skills.
Research Experience:
Gain research experience through internships, independent study projects, or research assistantships in fields related to biostatistics, data science, public health, or biomedical research. This demonstrates research skills, critical thinking abilities, and a commitment to the field.
Strong Letters of Recommendation:
Obtain strong letters of recommendation from academic instructors, research mentors, or professionals who can attest to your academic abilities, research potential, and suitability for graduate studies in biostatistics.
Relevant Experience or Background:
Highlight any relevant professional experience, such as work in healthcare, research, or data analysis roles. Demonstrating experience or skills related to biostatistics, data analysis, programming, or healthcare can strengthen your application.
Statement of Purpose:
Craft a compelling statement of purpose that outlines your academic background, research interests, career goals, and reasons for pursuing graduate studies in biostatistics at MCW. Clearly articulate how the program aligns with your interests and how you envision contributing to the field.
Standardized Test Scores:
Prepare for and perform well on standardized tests such as the GRE (Graduate Record Examination) if required by the program. Strong scores can enhance your application, but admissions committees also consider other aspects of your application holistically.
Interviews:
Be prepared for potential interviews as part of the admissions process. Use this opportunity to showcase your enthusiasm for the field, discuss your academic and research experiences, and articulate your goals for graduate studies in biostatistics.
By focusing on these areas of preparation, prospective students can strengthen their applications and increase their competitiveness for admission into the Biostatistics Master of Arts program at MCW.
Strong Academic Background:
Maintain a strong academic record, particularly in mathematics, statistics, computer science, and related quantitative disciplines. Completing coursework in calculus, linear algebra, probability, and statistics can provide a solid foundation for graduate-level studies in biostatistics.
Relevant Coursework:
Take relevant coursework in biostatistics, epidemiology, data analysis, and research methods to demonstrate a strong interest and aptitude in the field. Courses in programming languages such as R or Python can also be beneficial for acquiring essential data analysis skills.
Research Experience:
Gain research experience through internships, independent study projects, or research assistantships in fields related to biostatistics, data science, public health, or biomedical research. This demonstrates research skills, critical thinking abilities, and a commitment to the field.
Strong Letters of Recommendation:
Obtain strong letters of recommendation from academic instructors, research mentors, or professionals who can attest to your academic abilities, research potential, and suitability for graduate studies in biostatistics.
Relevant Experience or Background:
Highlight any relevant professional experience, such as work in healthcare, research, or data analysis roles. Demonstrating experience or skills related to biostatistics, data analysis, programming, or healthcare can strengthen your application.
Statement of Purpose:
Craft a compelling statement of purpose that outlines your academic background, research interests, career goals, and reasons for pursuing graduate studies in biostatistics at MCW. Clearly articulate how the program aligns with your interests and how you envision contributing to the field.
Standardized Test Scores:
Prepare for and perform well on standardized tests such as the GRE (Graduate Record Examination) if required by the program. Strong scores can enhance your application, but admissions committees also consider other aspects of your application holistically.
Interviews:
Be prepared for potential interviews as part of the admissions process. Use this opportunity to showcase your enthusiasm for the field, discuss your academic and research experiences, and articulate your goals for graduate studies in biostatistics.
By focusing on these areas of preparation, prospective students can strengthen their applications and increase their competitiveness for admission into the Biostatistics Master of Arts program at MCW.
Recommended Courses
Prospective applicants for the Biostatistics Master of Arts program at the Medical College of Wisconsin (MCW) should consider taking specific courses as part of their bachelor's degree to strengthen their preparation for graduate studies in biostatistics. While specific requirements may vary by program, here are some recommended courses:
Biology or Public Health (optional but recommended)
Biology or Public Health (optional but recommended)
Courses in biology or public health can provide context for applying biostatistical methods in healthcare and biomedical research settings. Topics may include epidemiology, health behavior, environmental health, or disease biology.
Computer Science
Computer Science
Programming:
Gain proficiency in programming languages commonly used in data analysis and statistical computing, such as R or Python. Take courses in programming fundamentals, data structures, and algorithms.
Data Manipulation and Visualization:
Learn techniques for data manipulation, cleaning, and visualization using software packages like R, Python (with libraries like Pandas and Matplotlib), or other data analysis tools.
Gain proficiency in programming languages commonly used in data analysis and statistical computing, such as R or Python. Take courses in programming fundamentals, data structures, and algorithms.
Data Manipulation and Visualization:
Learn techniques for data manipulation, cleaning, and visualization using software packages like R, Python (with libraries like Pandas and Matplotlib), or other data analysis tools.
Mathematics
Mathematics
Calculus:
Take courses in single-variable calculus (Calculus I, II) and possibly multivariable calculus (Calculus III). These courses provide a foundation in mathematical concepts and techniques essential for understanding statistical theory.
Linear Algebra:
Complete a course in linear algebra, which covers topics such as matrices, vectors, systems of linear equations, and eigenvalues/eigenvectors. Linear algebra is important for understanding statistical models and methods.
Take courses in single-variable calculus (Calculus I, II) and possibly multivariable calculus (Calculus III). These courses provide a foundation in mathematical concepts and techniques essential for understanding statistical theory.
Linear Algebra:
Complete a course in linear algebra, which covers topics such as matrices, vectors, systems of linear equations, and eigenvalues/eigenvectors. Linear algebra is important for understanding statistical models and methods.
Statistics
Statistics
Introductory Statistics:
Take an introductory statistics course that covers basic statistical concepts, probability theory, data analysis techniques, and inferential statistics.
Probability Theory:
Complete a course in probability theory, which provides a deeper understanding of probability distributions, random variables, and stochastic processes. Probability theory is fundamental to statistical inference and modeling.
Take an introductory statistics course that covers basic statistical concepts, probability theory, data analysis techniques, and inferential statistics.
Probability Theory:
Complete a course in probability theory, which provides a deeper understanding of probability distributions, random variables, and stochastic processes. Probability theory is fundamental to statistical inference and modeling.