Statistics and Data Science, B.S.
B.S. in Statistics and Data Science
Peter Freeman, Undergraduate Program Director
Location: Baker Hall 229
pfreeman@andrew.cmu.edu
Zach Branson, Assistant Director of the Undergraduate Program
Location: Baker Hall 232
zbranson@andrew.cmu.edu
Amanda Mitchell, Lead Senior Academic Advisor
Glenn Clune, Academic Program Manager
Sylvie Aubin, Academic Program Manager
Peter Long, Academic Advisor
Location: Baker Hall 129
statadvising@andrew.cmu.edu
The Bachelor of Science in Statistics and Data Science trains students in statistical theory, methods, applications, and judgment, such that they can make real-world impacts. This degree provides students the flexibility to apply statistical methods to areas they are most interested in. In particular, students form a self-defined concentration outside of statistics, such that this degree is relevant for students who want to form their own context-specific expertise to make data-driven contributions. Thus, this degree is also relevant for students who wish to pursue graduate programs and data-related fields.
Curriculum
1. Mathematical Foundations (Prerequisites)39–52 units
Mathematics is the language in which statistical models are described and analyzed, so some experience with basic calculus and linear algebra is an important component for anyone pursuing a program of study in Statistics & Data Science.
Complete the following:
| 21-090 | Precalculus | 10 |
| Complete one of the following options: | ||
| 21-111 | Differential Calculus | 10 |
| 21-112 | Integral Calculus | 10 |
| OR | ||
| 21-120 | Differential and Integral Calculus | 10 |
| And one of the following four courses: | ||
| 21-256 | Multivariate Analysis | 9 |
| 21-259 | Calculus in Three Dimensions | 10 |
| 21-266 | Vector Calculus using Matrix Algebra | 10 |
| 21-268 | Multidimensional Calculus | 11 |
| And one of the following three courses: | ||
| 21-240 | Matrix Algebra with Applications | 10 |
| 21-241 | Matrices and Linear Transformations | 11 |
| 21-242 | Matrix Theory | 11 |
Notes:
- Passing the Mathematical Sciences assessment tests available during First-Year Orientation is an acceptable alternative to completing 21-090 and/or 21-120.
- It is recommended that students complete the calculus requirement during their freshman year.
- 21-266 and 21-268 are intended only for students with a very strong mathematical background.
- The linear algebra requirement needs to be completed before taking 36-401 Modern Regression.
- 21-241 and 21-242 are intended only for students with a very strong mathematical background - students should consult the recommended prerequisite courses before enrolling.
2. Data Analysis36-45 units
Data analysis is the art and science of extracting insight from data. The art lies in knowing which displays or techniques will reveal the most interesting features of a complicated data set. The science lies in understanding the various techniques and the assumptions on which they rely. Both aspects require practice to master.
The Beginning Data Analysis courses give a hands-on introduction to the art and science of data analysis. The courses cover similar topics but differ slightly in the examples they emphasize. 36-200 draws examples from many fields and satisfies the Dietrich College Core Requirement in Statistical Reasoning. This course is therefore required for students in the college. (Note: a score of 5 on the Advanced Placement [AP] Exam in Statistics may be used to waive this requirement). 36-220 emphasizes examples in engineering.
The Intermediate Data Analysis courses build on the principles and methods covered in the introductory course, and more fully explore specific types of data analysis methods in more depth.
The Advanced Data Analysis courses draw on students' previous experience with data analysis and understanding of statistical theory to develop advanced, more sophisticated methods. These core courses involve extensive analysis of real data with emphasis on developing the oral and writing skills needed for communicating results.
Beginning Data Analysis
Choose one of the following courses:
| 36-200 | Reasoning with Data * | 9 |
| 36-220 | Engineering Statistics and Quality Control | 9 |
*A score of 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement. 36-220 emphasizes examples in engineering and Architecture.
Note: Students who enter the program with credit for probability and statistical inference should discuss options with an advisor.
Sequence 1
Intermediate Data Analysis
Choose one of the following courses:
| 36-202 | Methods for Statistics & Data Science * | 9 |
| 36-290 | Introduction to Statistical Research Methodology | 9 |
| 36-303 | Sampling, Survey and Society | 9 |
| 36-309 | Experimental Design for Behavioral & Social Sciences | 9 |
| 36-315 | Statistical Graphics and Visualization | 9 |
Advanced Data Analysis Elective
Choose one of the following courses:
| 36-311 | Statistical Analysis of Networks | 9 |
| 36-313 | Statistics of Inequality and Discrimination | 9 |
| 36-318 | Introduction to Causal Inference | 9 |
| 36-319 | Statistics and Machine Learning for the Physical Sciences | 9 |
| 36-390 | Study Abroad Experience in Statistics and Data Science | 9 |
| 36-396 | Tartan Athletics Analytics | 9 |
| 36-490 | Undergraduate Research | 9 |
| 36-493 | Sports Analytics Capstone | 9 |
| 36-497 | Corporate Capstone Project | 9 |
Students can also take a second Special Topics (36-46x or 36-47x) course to fulfill an advanced data analysis elective requirement (see section #5).
Sequence 2 (For students beginning later in their college career)
Advanced Data Analysis Electives
Choose two of the following courses:
| 36-311 | Statistical Analysis of Networks | 9 |
| 36-313 | Statistics of Inequality and Discrimination | 9 |
| 36-318 | Introduction to Causal Inference | 9 |
| 36-319 | Statistics and Machine Learning for the Physical Sciences | 9 |
| 36-390 | Study Abroad Experience in Statistics and Data Science | 9 |
| 36-396 | Tartan Athletics Analytics | 9 |
| 36-490 | Undergraduate Research | 9 |
| 36-493 | Sports Analytics Capstone | 9 |
| 36-497 | Corporate Capstone Project | 9 |
Students can also take a second Special Topics (36-46x or 36-47x) course to fulfill an advanced data analysis elective requirement (see section #5).
3. Probability Theory and Statistical Theory18 units
The theory of probability gives a mathematical description of the randomness inherent in our observations. It is the language in which statistical models are stated, so an understanding of probability is essential for the study of statistical theory. Statistical theory provides a mathematical framework for making inferences about unknown quantities from data. The theory reduces statistical problems to their essential ingredients to help devise and evaluate inferential procedures. It provides a powerful and wide-ranging set of tools for dealing with uncertainty.
To satisfy the theory requirement, complete the following:
| 36-235 | Probability and Statistical Inference I * | 9 |
| 36-236 | Probability and Statistical Inference II ** | 9 |
Note: Students who enter the program with credit for probability and statistical inference should discuss options with an advisor.
*It is possible to substitute 36-218, 36-219, 36-225, 15-259 or 21-325 for 36-235. 36-235 is the standard (and recommended) introduction to probability, 36-219 is tailored for engineers and computer scientists, 36-218 and 15-259 are more mathematically rigorous classes for Computer Science students and more mathematically advanced, and 21-325 is a rigorous probability theory course offered by the Department of Mathematics.
**It is possible to substitute 36-226 or 36-326 (honors course) for 36-236 . 36-236 is the standard (and recommended) introduction to statistical inference.
Please note that students who complete 36-235 are expected to take 36-236 to complete their theory requirements. Students who choose to take 36-225 instead will be required to take 36-226 afterward. They will not be eligible to take 36-236.
Comment:
(i) In order to meet the prerequisite requirements, a grade of at least a C is required in 36-235 (or equivalent) and 36-236 (or equivalent).
4. Statistical Computing19 to 21 units
Fundamental to the practice of statistics and data science is the ability to effectively code data processing and analysis tasks. Within the domain of statistics, the use of the programming language R is ubiquitous, and thus we expose students to it throughout the curriculum (and in depth in Statistical Computing). Within the larger domain of data science, the use of the programming language Python is also ubiquitous, and thus we require all majors to gain, at a minimum, basic competency in the language by taking either Principles of Computing, or Fundamentals of Programming and Computer Science. We would advise those students who are considering receiving course credit for one of these two courses given their score on the AP Computer Science A exam to actually take one (or both) of them at Carnegie Mellon instead, as within data science as a whole Python is far more widely used than Java.
| Take one of the following courses: | ||
| 15-110 | Principles of Computing | 10 |
| 15-112 | Fundamentals of Programming and Computer Science | 12 |
| 02-120 | Programming for Scientists | 12 |
| Complete the following course: | ||
| 36-350 | Statistical Computing | 9 |
5. Special Topics9 units
The Department of Statistics & Data Science offers advanced courses that focus on specific statistical applications or advanced statistical methods. These courses are numbered 36-46x (36-461, 36-462, etc.) or 36-47x (36-470, 36-471, etc.) The objective of the course is to expose students to important topics in statistics and/or interesting applications which are not part of the standard undergraduate curriculum.
Note: All Special Topics are not offered every semester, and new Special Topics are regularly added.
To satisfy the Special Topics requirement complete one of the following:
| 36-460 | Special Topics: Sports Analytics | 9 |
| 36-461 | Special Topics: Statistical Methods in Epidemiology | 9 |
| 36-462 | Special Topics: Statistical Machine Learning | 9 |
| 36-463 | Special Topics: Multilevel and Hierarchical Models | 9 |
| 36-464 | Special Topics: Psychometrics: A Statistical Modeling Approach | 9 |
| 36-465 | Special Topics: Conceptual Foundations of Statistical Learning | 9 |
| 36-466 | Special Topics: Statistical Methods in Finance | 9 |
| 36-467 | Special Topics: Data over Space & Time | 9 |
| 36-468 | Special Topics: Text Analysis | 9 |
| 36-469 | Special Topics: Statistical Genomics and High Dimensional Inference | 9 |
| 36-470 | Special Topics: Statistical Methods in Health Sciences | 9 |
| 36-471 | Special Topics: Time Series | 9 |
| 36-472 | Special Topics: Computational Statistical Methods in Life Sciences | 9 |
| 36-473 | Special Topics: Statistical Principles of Generative AI | 9 |
Note: All 36-46x and 36-47x courses require 36-401 as a prerequisite or corequisite.
6. Modern Regression and Advanced Methodology18 units
Central to the practice of statistics is the implementation and interpretation of statistical models. The purpose of statistical models is to represent data-generating processes, such that predictions and inferential conclusions can be made about real-world phenomena. Proper modeling involves not just coding, but also thinking critically about data, research goals, and the validity of the models themselves, given their intrinsic assumptions. The courses 36-401 and 36-402 focus on the theory of statistical models (especially linear models and their extensions), how they are applied in real data analyses, and how to interpret and present these analyses in written reports. 36-401 and 36-402 must be completed in residence at Carnegie Mellon University. Transfer credit will not be accepted.
To satisfy these requirements, complete the following:
| 36-401 | Modern Regression * | 9 |
| 36-402 | Advanced Methods for Data Analysis | 9 |
- *
In order to meet the prerequisite requirements, a grade of at least a C is required in 36-401.
7. Self-Defined Concentration Area (with advisor's approval)36 units
The power of statistics, and much of the fun, is that it can be applied to answer such a wide variety of questions in so many different fields. A critical part of statistical practice is understanding the questions being asked so that appropriate methods of analysis can be used. Hence, a critical part of statistical training is to gain experience applying abstract tools to real problems.
The Concentration Area is a set of four related courses outside of Statistics and Data Science that prepares the student to deal with statistical aspects of problems that arise in another field. These courses are usually drawn from a single discipline of interest to the student and must be approved by your the department. While these courses are not in Statistics and Data Science, the concentration area must complement the overall degree.
For example, students intending to pursue careers in the health or biomedical sciences could take further courses in biology or chemistry, or students intending to pursue careers in industry could look for appropriate business courses.
The concentration area can be fulfilled with a minor or additional major, but not all minors and additional majors fulfill this requirement. Due to other major options we already offer, we will not consider concentrations related to Economics, Machine Learning, Mathematics, or Neuroscience.
Concentration approval process: Please make sure to consult your Statistics and Data Science undergraduate advisor prior to pursuing courses for the concentration area. Students will submit a form provided by their advisor to have their concentration reviewed. Any changes or amendments to the concentration must be approved by the advisor. Once the concentration area is approved, any changes made to the previously agreed upon coursework require re-approval by an advisor.
* These courses can be amended later but must be re-approved by your Statistics Undergraduate Advisor if amended.
* Note: The concentration/track requirement is only for students whose primary major is statistics and has no other additional major or minor. The requirement does not apply for students who pursue an additional major in statistics.
| Total number of units for the major | 175-193* Units |
| Total number of units for the degree | 360 Units |
*Note: This number can vary depending on the courses chosen for the concentration area that a student takes. Speak with an academic advisor for more details.
Recommendations
Students in the Dietrich College of Humanities and Social Sciences who wish to major or minor in Statistics are advised to complete both the calculus requirement (one Mathematical Foundations calculus sequence) and the Beginning Data Analysis course 36-200 by the end of their freshman year.
The linear algebra requirement is a prerequisite for the course 36-401. It is therefore essential that students complete this requirement by their junior years at the latest.
Additional Major in Statistics and Data Science
Students who elect the B.S. in Statistics and Data Science as an additional major must fulfill all Statistics and Data Science degree requirements except for the Concentration Area requirement. Majors in many other programs would naturally complement a statistics major, including Tepper's undergraduate business program, Social and Decision Sciences, Policy and Management, and Psychology.
With respect to double-counting courses, it is departmental policy that students must have at least five statistics courses that do not count for their primary major. If students do not have at least five, they will need to take additional advanced data analysis electives.
Students are advised to begin planning their curriculum (with appropriate advisors) as soon as possible. This is particularly true if the other major has a complex set of requirements and prerequisites or when many of the other major's requirements overlap with the requirements for the B.S. in Statistics and Data Science.
Sample Programs
The following sample programs illustrate two ways (of many) to satisfy the requirements for the B.S. in Statistics. However, keep in mind that the program is flexible enough to support many other possible schedules and to emphasize a wide variety of interests.
The second schedule is an example of the case when a student enters the program through 36-235 and 36-236.
Schedule 1
| First-Year | Second-Year | ||
|---|---|---|---|
| Fall | Spring | Fall | Spring |
| 36-200 Reasoning with Data | 21-112 Integral Calculus | 36-235 Probability and Statistical Inference I | 36-236 Probability and Statistical Inference II |
| 21-111 Differential Calculus | Intermediate Data Analysis course | 21-256 Multivariate Analysis | 36-350 Statistical Computing |
| ----- | One of the following two courses: | ----- | 21-240 Matrix Algebra with Applications |
| ----- | 15-110 Principles of Computing | ----- | ----- |
| 15-112 Fundamentals of Programming and Computer Science | |||
| Third-Year | Fourth-Year | ||
|---|---|---|---|
| Fall | Spring | Fall | Spring |
| 36-401 Modern Regression | 36-402 Advanced Methods for Data Analysis | Course toward concentration | Course toward concentration |
| 36-3xx or 36-4xx Advanced Data Analysis Elective | 36-46x Special Topics course | ----- | ----- |
| Course toward concentration | Course toward concentration | ----- | ----- |
| ----- | ----- | ----- | ----- |
Schedule 2
| First-Year | Second-Year | ||
|---|---|---|---|
| Fall | Spring | Fall | Spring |
| 21-090 Precalculus | 21-120 Differential and Integral Calculus | 36-235 Probability and Statistical Inference I | 36-236 Probability and Statistical Inference II |
| 36-200 Reasoning with Data | One of the following two courses: | 21-256 Multivariate Analysis | 21-240 Matrix Algebra with Applications |
| ----- | 15-110 Principles of Computing | ----- | 36-350 Statistical Computing |
| ----- | 15-112 Fundamentals of Programming and Computer Science ----- | ----- | ----- |
| ----- | ----- | ----- | |
| Third-Year | Fourth-Year | ||
|---|---|---|---|
| Fall | Spring | Fall | Spring |
| 36-401 Modern Regression | 36-402 Advanced Methods for Data Analysis | 36-46x Special Topics | Course toward concentration |
| 36-3xx or 36-4xx Advanced Data Analysis Elective | Course toward concentration | Course toward concentration | 36-3xx or 36-4xx Advanced Data Analysis Elective |
| Course toward concentration | ----- | ----- | ----- |
| ----- | ----- | ----- | |