| Program start date | Application deadline |
| 2024-03-15 | - |
Program Overview
The M.S. Data Science program at the University of Wisconsin-Madison equips students with computational and statistical skills to address data-rich problems. The curriculum focuses on emerging areas of applied statistical and computing research and practice, preparing graduates for careers as data analysts and data scientists in various industries. The program is taught by experienced faculty from the Statistics and Computer Sciences Departments, using a combination of lectures, discussions, hands-on labs, and research projects.
Program Outline
Degree Overview:
The M.S. Data Science program is a joint professional program offered by the Statistics and Computer Sciences Departments at the University of Wisconsin-Madison. It equips students with computational and statistical thinking skills, which can be combined with domain knowledge to address data-rich problems from diverse fields and industries. Graduates acquire competencies in critical thinking about data, managing, processing, modeling, and analyzing data to extract meaning and knowledge, and using data responsibly and ethically. The curriculum focuses on emerging and rapidly growing areas of applied statistical and computing research and practice.
Outline:
Course Schedule:
The program consists of 30 credits, including:
- Statistics Core (9 credits): Statistical Models for Data Science, Statistical Inference for Data Science, Statistical Methods for Data Science
- Computer Sciences Core (9 credits): Select one course from each category: Algorithms, Systems, Humans and Data
- Machine Learning Core (6 credits): Select two courses from: Introduction to Artificial Intelligence, Machine Learning, Mathematical Foundations of Machine Learning, Advanced Deep Learning, Introduction to Machine Learning and Statistical Pattern Classification, Introduction to Deep Learning and Generative Models, Statistical Learning
- Data Science Electives (16 credits): Select from a list of courses in areas such as Optimization, Operating Systems, Database Management Systems, Bioinformatics, Data Visualization, Human-Computer Interaction, Security and Privacy for Data Science, Stochastic Modeling Techniques, Integer Optimization
Assessment:
The assessment methods used in the program may include:
- Assignments and projects
- Exams and quizzes
- Presentations
- Research papers
Teaching:
The program is taught by experienced faculty from the Statistics and Computer Sciences Departments. Teaching methods include:
- Lectures
- Discussions
- Hands-on labs
- Research projects
Careers:
Graduates of the M.S. Data Science program are prepared for careers as data analysts and data scientists in various industries, including:
- Technology
- Healthcare
- Finance
- Manufacturing
- Retail
- Government They can also pursue further education in data science, statistics, computer science, or related quantitative and computational fields.
University of Wisconsin–Madison
Overview:
University of Wisconsin–Madison is a public research university located in Madison, Wisconsin. It is known for its strong academic programs, extensive research enterprise, and vibrant campus life. The university is guided by the Wisconsin Idea, a philosophy that emphasizes the importance of using knowledge to improve the lives of people beyond the campus.
Services Offered:
The university offers a wide range of services to students, including:
Academic Support:
Academic advising, tutoring, writing centers, and career services.Student Life:
Student organizations, recreational facilities, cultural events, and health services.Technology:
Access to computer labs, online resources, and software.Housing:
On-campus residence halls and off-campus housing options.Financial Aid:
Scholarships, grants, loans, and work-study programs.Student Life and Campus Experience:
Students at UW–Madison can expect a vibrant and engaging campus experience. The university boasts a diverse student body, a wide range of student organizations, and a lively social scene. The city of Madison offers a variety of cultural attractions, restaurants, and entertainment options.
Key Reasons to Study There:
Strong Academic Programs:
UW–Madison is home to a wide range of academic programs, including highly ranked programs in engineering, business, medicine, and the humanities.Research Opportunities:
The university is a leading research institution, offering students opportunities to participate in groundbreaking research projects.Wisconsin Idea:
The university's commitment to public service provides students with opportunities to make a positive impact on the world.Vibrant Campus Life:
UW–Madison offers a lively and engaging campus experience with a diverse student body, a wide range of student organizations, and a variety of cultural events.Location:
Madison is a beautiful and vibrant city with a strong sense of community.Academic Programs:
UW–Madison offers a wide range of undergraduate and graduate programs across various disciplines, including:
Engineering:
The College of Engineering is highly ranked and offers programs in areas such as computer science, electrical engineering, and mechanical engineering.Business:
The Wisconsin School of Business is known for its strong programs in finance, marketing, and entrepreneurship.Medicine:
The School of Medicine and Public Health is a leading institution in medical research and education.Humanities:
The university offers a wide range of programs in the humanities, including English, history, philosophy, and art history.Other:
Athletics:
UW–Madison is a member of the Big Ten Conference and has a strong athletic tradition.Alumni Network:
The university has a large and active alumni network, providing students with valuable connections after graduation.Sustainability:
UW–Madison is committed to sustainability and has a number of initiatives to reduce its environmental impact.Entry requirements:
- Applicants to the Master's program in data science should have completed courses equivalent to the following UW-Madison requirements.
- Calculus and Mathematical Foundation > Calculus 1 > Calculus 2 > Elementary matrix and linear algebra
- Programming foundation >> Data Science Programming >> Programming 2 >> Data science programming 2
- Recommended courses: >> R for statistics >> R for statistics >> R for statistics >> Data Science with
Language proficiency requirements:
Every applicant with a native language that isn't English, or who has not received an undergraduate education in the English language, is responsible for submitting an English proficiency exam score. The results of these tests must also meet the minimum Graduate school requirements.
