The Department of Applied Mathematics offers a minor in statistics and data science. Declaration of a minor is open to any undergraduate student enrolled at CU Boulder, regardless of college or school. For more information, see the university's minor requirements on the Policies & Requirements page.
The minor in statistics and data science was developed to provide in-depth training in data science, statistical methods and techniques well beyond the training usually received by science and engineering majors. The ability to understand, visualize and analyze data is becoming an increasingly important skill in many disparate fields. This minor offers undergraduate students from any major the opportunity to develop their statistical knowledge.
Requirements
Prerequisites for the Statistics and Data Science minor are two semesters of calculus and computing experiences such as provided by APPM 1650 (preferred), CSCI 1300 or CHEN 1310. A student cannot earn both a minor in statistics and data science and a minor in applied mathematics with the probability and statistics emphasis.
Students may earn both a BS in Applied Mathematics and a minor in Statistics and Data Science. However, the 12 upper-division credits of statistics required for the minor may not be counted towards the 25 credits of upper-division applied math courses. The 12 upper-division credits of statistics may, however, count towards the 24 credits of Area of Application required for all applied math majors.
Residency
A minimum of 23 credits at the 2000 level and above is required. At least three APPM or STAT courses, two of which must be at the 3000 level or above, need to be taken on the Boulder campus. No more than nine credit hours may be applied from transfer work; of those nine, no more than six may be 3000 level or above.
Minimum Grades
A cumulative GPA of 2.00 or better is required in the courses that are used to satisfy the requirements for this minor. Each individual course that is counted towards these degree requirements must be passed with a grade of C- or better.
Required Courses and Credit Hours
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses | ||
| APPM 2350 | Calculus 3 for Engineers | 4-5 |
| or MATH 2400 | Calculus 3 | |
| or APPM 2340 | Calculus 3 for Statistics and Data Science | |
| APPM 3310 | Matrix Methods and Applications | 3 |
| STAT 2600 | Introduction to Data Science | 4 |
| APPM 3570/STAT 3100 | Applied Probability | 3 |
| STAT 3400 | Applied Regression | 3 |
| Elective Courses | ||
| Select two of the following courses: 1 | 6 | |
| Stochastic Analysis for Finance | ||
| Data Assimilation in High Dimensional Dynamical Systems | ||
| Applied Deep Learning 1 and Applied Deep Learning 2 | ||
| Advanced Statistical Modeling | ||
| Spatial Statistics | ||
| Theory of Machine Learning | ||
| High-Dimensional Probability for Data Science | ||
| Introduction to Mathematical Statistics | ||
| Introduction to Time Series | ||
| Markov Processes, Queues, and Monte Carlo Simulations | ||
or STAT 4100 | Markov Processes, Queues, and Monte Carlo Simulations | |
| Random Graphs | ||
| Statistical Learning | ||
| Computational Bayesian Statistics | ||
| Total Credit Hours | 23-24 | |
| 1 | Any one of APPM's 3-credit special topics courses in probability or statistics may also be used to meet this requirement. |