The Mathematics for Data and Risk Analysis program requires 10 distinct courses for at least 30 credits as described below. Note that while some courses may be used to fulfill more than one requirement it is still considered only a single course and may only contribute once to the total course count. Finally, at most one course from each of the following groupings may be used to fulfill the minimum course and credit requirement (i.e.: minimum of ten courses and at least 30 credits): Intro Linear Algebra (MATH 320, MATH 340, MATH 341, MATH 375), Intro Differential Equations (MATH 319, MATH 320 or MATH 376), and Intro Probability (MATH/STAT 309 or MATH/STAT 431).
Code | Title | Credits |
---|---|---|
Core Math Requirement (minimum of six distinct MATH courses for at least 18 credits) ^{1} | ||
Linear Algebra | 3-5 | |
Linear Algebra and Differential Equations | ||
or MATH 340 | Elementary Matrix and Linear Algebra | |
or MATH 341 | Linear Algebra | |
or MATH 375 | Topics in Multi-Variable Calculus and Linear Algebra | |
Probability (Complete at least one) | 3 | |
Introduction to the Theory of Probability | ||
Introduction to Probability and Mathematical Statistics I | ||
Probability Theory | ||
Statistics ^{1} | 3 | |
Introduction to Probability and Mathematical Statistics II (Statistics) | ||
Intermediate Mathematics Requirement (complete at least one) | 0-6 | |
Applied Mathematical Analysis and Applied Mathematical Analysis | ||
Linear Algebra | ||
Topics in Multi-Variable Calculus and Linear Algebra | ||
The Theory of Single Variable Calculus | ||
Advanced Mathematics Requirement (select one) | 3 | |
Numerical Analysis | ||
Analysis I | ||
Probability Theory | ||
Mathematical Methods in Data Science | ||
Linear Algebra II | ||
Electives to reach required six courses for at least 18 credits in MATH | 3-6 | |
At least one elective must come from: ^{2} | ||
Numerical Linear Algebra | ||
Numerical Analysis | ||
Ordinary Differential Equations | ||
Analysis I | ||
Analysis II | ||
Linear Optimization | ||
Probability Theory | ||
Mathematical Methods in Data Science | ||
Linear Algebra II | ||
Modern Algebra | ||
Modern Algebra | ||
Stochastic Methods for Biology | ||
Analysis of Partial Differential Equations | ||
Introduction to Fourier Analysis | ||
Introduction to Measure and Integration | ||
Introduction to Stochastic Processes | ||
An Introduction to Brownian Motion and Stochastic Calculus | ||
Remaining courses/credits may be selected from: | ||
Techniques in Ordinary Differential Equations | ||
Applied Mathematical Analysis | ||
Applied Mathematical Analysis | ||
Topics in Multi-Variable Calculus and Differential Equations | ||
Applied Dynamical Systems, Chaos and Modeling | ||
The Theory of Single Variable Calculus | ||
Introduction to Combinatorial Optimization | ||
Introduction to Cryptography | ||
Applied Linear Algebra | ||
Introduction to Number Theory | ||
Introduction to Combinatorics | ||
Data/Risk Requirement (Four Courses distinct from the above for at least 12 credits) ^{3} | ||
Select a distinct introduction course or sequence: | 3-6 | |
Actuarial Sciences: | ||
Theory of Interest | ||
Statistics: | ||
Applied Regression Analysis and Statistical Experimental Design | ||
Data Science: | ||
Data Science Modeling II and Statistical Experimental Design | ||
Select remaining courses/credits from: ^{4} | 6-14 | |
Actuarial Mathematics I | ||
Actuarial Mathematics II | ||
Loss Models I | ||
Loss Models II | ||
Regression and Time Series for Actuaries | ||
Health Analytics | ||
Machine Learning for Business Analytics | ||
Introduction to Time Series | ||
Introductory Nonparametric Statistics | ||
An Introduction to Sample Survey Theory and Methods | ||
Applied Categorical Data Analysis | ||
Introduction to Machine Learning and Statistical Pattern Classification | ||
Introduction to Deep Learning and Generative Models | ||
Applied Multivariate Analysis | ||
Financial Statistics | ||
Introduction to Computational Statistics | ||
Introduction to Combinatorics | ||
Linear Optimization | ||
Statistical Methods for Spatial Data | ||
Introduction to Stochastic Processes | ||
Statistical Methods for Clinical Trials | ||
Statistical Methods for Epidemiology | ||
Total Credits | 30 |
RESIDENCE AND QUALITY OF WORK
- 2.000 GPA on all MATH courses and courses eligible for the major.^{5}
- 2.000 GPA on at least 15 credits of upper level credit in the major.^{6}
- 15 credits in MATH in the major taken on the UW-Madison campus.^{7}
Footnotes
- ^{ 1 }
Students taking STAT 312 to satisfy the Statistics requirement will not be able to use this course towards the six courses/18 credits of MATH courses.
- ^{ 2 }
This course must be distinct from the advanced mathematics requirement.
- ^{ 3 }
The courses which follow may have prerequisites outside of this program.
- ^{ 4 }
Any MATH course from the elective list above may be used in lieu of any of the following courses.
- ^{ 5 }
This includes any course with a MATH prefix (or cross-listed with MATH) regardless of its appearance in the tables above and any non-MATH course explicitly listed in the tables above.
- ^{ 6 }
This includes any MATH course (including those crosslisted with MATH) which are numbered 307 and above, regardless of its appearance in the tables above, as well as only those non-MATH course which appear in the tables above and have the advanced LAS attribute.
- ^{ 7 }
This includes any MATH course (and those crosslisted with MATH) numbered 307 and above.
Sample Four-Year Plan
This Sample Four-Year Plan is a tool to assist students and their advisor(s). Students should use it—along with their DARS report, the Degree Planner, and Course Search & Enroll tools—to make their own four-year plan based on their placement scores, credit for transferred courses and approved examinations, and individual interests. As students become involved in athletics, honors, research, student organizations, study abroad, volunteer experiences, and/or work, they might adjust the order of their courses to accommodate these experiences. Students will likely revise their own four-year plan several times during college.
In general, your four year plan in mathematics should be organized along the following sequence: 1) Calculus, 2) Linear Algebra, 3) Required Intermediate level course, 4) Additional intermediate level courses as needed, 5) Required advanced level course, 6) Additional advanced level courses.
Freshman | |||
---|---|---|---|
Fall | Credits | Spring | Credits |
MATH 221 | 5 | MATH 222 | 4 |
Literature Breadth | 3 | Literature Breadth | 3 |
Communication A | 3 | Ethnic Studies | 3 |
Foreign Language ^{if required} | 4 | Foreign Language (if required) | 4 |
15 | 14 | ||
Sophomore | |||
Fall | Credits | Spring | Credits |
MATH 234^{1} | 4 | MATH Required Linear Algebra | 3 |
Humanities Breadth | 3 | MATH required Probability | 3 |
Communication B | 3 | Humanities Breadth | 3 |
Physical Science Breadth | 3 | Physical Science Breadth | 3 |
Elective | 3 | Elective | 3 |
16 | 15 | ||
Junior | |||
Fall | Credits | Spring | Credits |
MATH required Statistics | 3 | Required Intermediate MATH | 3 |
Data/Risk course | 3 | Data/Risk course | 3 |
Social Sciences Breadth | 3 | Social Science Breadth | 3 |
Biological Sciences Breadth | 3 | Biological Sciences Breadth | 3 |
Elective | 3 | Elective | 3 |
15 | 15 | ||
Senior | |||
Fall | Credits | Spring | Credits |
Required Advanced MATH | 3 | Advanced MATH Elective | 3 |
Data/Risk course | 3 | Data/Risk course | 3 |
Social Science Breadth | 3 | Social Science Breadth | 3 |
Elective | 3 | Elective | 3 |
Elective | 3 | Elective | 3 |
15 | 15 | ||
Total Credits 120 |
- ^{ 1 }
Students should declare their major upon the successful completion of this course