Christopher R. Genovese, Department Head
Rebecca Nugent, Director of Undergraduate Studies
Christopher Peter Makris, Programs Administrator
Samantha Nielsen, Academic Advisor
Email: statadvising@stat.cmu.edu
Department Office: Baker Hall 132

Overview

Uncertainty is inescapable: randomness, measurement error, deception, and incomplete or missing information complicate all our lives. Statistics is the science and art of making predictions and decisions in the face of uncertainty. Statistical issues are central to big questions in public policy, law, medicine, industry, computing, technology, finance, and science. Indeed, the tools of Statistics apply to problems in almost every area of human activity where data are collected.

Statisticians must master diverse skills in computing, mathematics, decision making, forecasting, interpretation of complicated data, and design of meaningful comparisons. Moreover, statisticians must learn to collaborate effectively with people in other fields and, in the process, to understand the substance of these other fields. For all these reasons, Statistics students are highly sought-after in the marketplace.

Recent Statistics majors at Carnegie Mellon have taken jobs at leading companies in many fields, including the National Economic Research Association, Boeing, Morgan Stanley, Deloitte, Rosetta Marketing Group, Nielsen, Proctor and Gamble, Accenture, and Goldman Sachs.  Other students have taken research positions at the National Security Agency, the U.S. Census Bureau, and the Science and Technology Policy Institute or worked for Teach for America.  Many of our students have also gone on to graduate study at some of the top programs in the country including Carnegie Mellon, the Wharton School at the University of Pennsylvania, Johns Hopkins, University of Michigan, Stanford University, Harvard University, Duke University, Emory University, Yale University, Columbia University, and Georgia Tech.
 

The Department and Faculty

The Department of Statistics and Data Science at Carnegie Mellon University is world-renowned for its contributions to statistical theory and practice. Research in the department runs the gamut from pure mathematics to the hottest frontiers of science. Current research projects are helping make fundamental advances in neuroscience, cosmology, public policy, finance, and genetics.

The faculty members are recognized around the world for their expertise and have garnered many prestigious awards and honors. (For example, three members of the faculty have been awarded the COPSS medal, the highest honor given by professional statistical societies.) At the same time, the faculty is firmly dedicated to undergraduate education. The entire faculty, junior and senior, teach courses at all levels. The faculty are accessible and are committed to involving undergraduates in research.

The Department augments all these strengths with a friendly, energetic working environment and exceptional computing resources. Talented graduate students join the department from around the world, and add a unique dimension to the department's intellectual life. Faculty, graduate students, and undergraduates interact regularly.
 

How to Take Part

There are many ways to get involved in Statistics at Carnegie Mellon:

  • The Bachelor of Science in Statistics in the Dietrich College of Humanities and Social Sciences (DC) is a broad-based, flexible program that helps you master both the theory and practice of Statistics. The program can be tailored to prepare you for later graduate study in Statistics or to complement your interests in almost any field, including Psychology, Physics, Biology, History, Business, Information Systems, and Computer Science.
  • The Minor (or Additional Major) in Statistics is a useful complement to a (primary) major in another Department or College. Almost every field of inquiry must grapple with statistical problems, and the tools of statistical theory and data analysis you will develop in the Statistics minor (or Additional Major) will give you a critical edge.
  • The Bachelor of Science in Economics and Statistics provides an interdisciplinary course of study aimed at students with a strong interest in the empirical analysis of economic data. Jointly administered by the Department of Statistics and Data Science and the Undergraduate Economics Program, the major's curriculum provides students with a solid foundation in the theories and methods of both fields. (See Dietrich College Interdepartmental Majors as well later in this section)
  • The Bachelor of Science in Statistics and Machine Learning is a program housed in the Department of Statistics and Data Science and is jointly administered with the Department of Machine Learning. In this major students take courses focused on skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. The program is geared toward students interested in statistical computation, data science, and "big data" problems.
  • The Statistics Concentration and the Operations Research and Statistics Concentration in the Mathematical Sciences Major (see Department of Mathematical Sciences) are administered by the Department of Mathematical Sciences with input from the Department of Statistics and Data Science.
  • There are several ongoing exciting research projects in the Department of Statistics and Data Science, and the department enthusiastically seeks to involve undergraduates in this work. Both majors and non-majors are welcome.
  • Non-majors are eligible to take most of our courses, and indeed, they are required to do so by many programs on campus. Such courses offer one way to learn more about the Department of Statistics and Data Science and the field in general.

Curriculum

Statistics consists of two intertwined threads of inquiry: Statistical Theory and Data Analysis. The former uses probability theory to build and analyze mathematical models of data in order to devise methods for making effective predictions and decisions in the face of uncertainty. The latter involves techniques for extracting insights from complicated data, designs for accurate measurement and comparison, and methods for checking the validity of theoretical assumptions. Statistical Theory informs Data Analysis and vice versa. The Department of Statistics and Data Science curriculum follows both of these threads and helps the student develop the complementary skills required.

Below, we describe the requirements for the Major in Statistics and the different categories within our basic curriculum, followed by the requirements for the Major in Economics and Statistics, the Major in Statistics and Machine Learning, and the Minor in Statistics.

Note: We recommend that you use the information provided below as a general guideline, and then schedule a meeting with a Statistics Undergraduate Advisor (email: statadvising@stat.cmu.edu) to discuss the requirements in more detail, and build a program that is tailored to your strengths and interests.

B.S. in Statistics

Academic Advisor: Samantha Nielsen
Faculty Advisors: Peter Freeman and Mark Schervish
Office: Baker Hall 132
Email: statadvising@stat.cmu.edu

Students in the Bachelor of Science program develop and master a wide array of skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. In addition, Statistics majors gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration. The requirements for the Major in Statistics are detailed below and are organized by categories #1-#7.

Curriculum

1. Mathematical Foundations (Prerequisites)29–39 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.

Calculus*:

Complete one of the following three sequences of mathematics courses at Carnegie Mellon, each of which provides sufficient preparation in calculus:

Sequence 1
21-111Differential Calculus10
21-112Integral Calculus10
and one of the following
21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9
Sequence 2
21-120Differential and Integral Calculus10
and one of the following
21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9

Notes:

  • Other sequences are possible, and require approval from the undergraduate advisor.
  • Passing the MSC 21-120 assessment test is an acceptable alternative to completing 21-120.
Linear Algebra**:

Complete one of the following three courses:

21-240Matrix Algebra with Applications10
21-241Matrices and Linear Transformations10
21-242Matrix Theory10

* It is recommended that students complete the calculus requirement during their freshman year.

**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.

2. Data Analysis:36–45 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 or 36-201 draw examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. One of these courses is therefore recommended for students in the College. (Note: A score of 4 or 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement). Other courses emphasize examples in business (36-207), engineering and architecture (36-220), and the laboratory sciences (36-247).

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.

Sequence 1 (For students beginning their freshman or sophomore year)
Beginning*

Choose one of the following courses:

36-200Reasoning with Data9
36-201Statistical Reasoning and Practice9
36/70-207Probability and Statistics for Business Applications9
36-220Engineering Statistics and Quality Control9
36-247Statistics for Lab Sciences9
*Or extra data analysis course in Statistics

Note: Students who enter the program with 36-225 or 36-226 should discuss options with an advisor.  Any 36-300 or 36-400 level course in Data Analysis that does not satisfy any other requirement for a Statistics Major and Minor may be counted as a Statistical Elective.

Intermediate*

Choose one of the following courses

36-202Methods for Statistics and Data Science **9
36/70-208Regression Analysis9
36-309Experimental Design for Behavioral and Social Sciences9
*Or extra data analysis course in Statistics
** Must take prior to 36-401
Advanced

Choose one of the following courses:

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
and take the following two courses:
36-401Modern Regression9
36-402Advanced Methods for Data Analysis9

Students can also take a second 36-46x (see section #5).

Sequence 2 (For students beginning later in their college career)
Advanced

Choose two of the following courses:

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added. See section 5 for details.

and take the following two courses:

36-401Modern Regression9
36-402Advanced Methods for Data Analysis9

3. Probability Theory and Statistical Theory:18 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 take the following two courses:

36-225Introduction to Probability Theory **9
and one of the following two courses:
36-226Introduction to Statistical Inference9
36-326Mathematical Statistics (Honors)9


**It is possible to substitute 36-217 or 21-325 for 36-225. (36-225 is the standard introduction to probability, 36-217 is tailored for engineers and computer scientists, and 21-325 is a rigorous probability theory course offered by the Department of Mathematics.)

Comments:

(i) In order to be a Major or a Minor in good standing, a grade of at least a C is required in 36-225, 36-226 and 36-401. In particular, a grade of C or higher is required in order to be able to continue in the major.

(ii) In special cases, and in consultation with the Statistics Advisor, the theory requirement can be satisfied by taking a single graduate level class 36-700 Probability and Mathematical Statistics or  36-705 Intermediate Statistics which is much more mathematically rigorous. This option should be considered by strong Statistics Majors who are also majoring in Computer Science, Operations Research, or Mathematics and/or who are considering graduate study in Statistics. This option does require special permission from the advisor. Students who end up satisfying the theory requirement by taking either 36-700 or 36-705 are required to take an additional statistics elective (see category #6, Statistical Electives, below).

4. Statistical Computing:9 units

36-350Statistical Computing *9

*In rare circumstances, a higher level Statistical Computing course, approved by your Statistics advisor, may be used as a substitute.

5. Special Topics9 units

The Department of Statistics and 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.). Two of these courses will be offered every year, one per semester. Past topics included Statistical Learning, Data Mining, Statistics and the Law, Bayesian Statistics, Nonparametric Statistics, Statistical Genetics, Multilevel and Hierarchical Models, and Statistical Methods in Epidemiology. 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.

To satisfy the Special Topics requirement choose one of the 36-46x courses (which are 9 units).

Note: All 36-46x courses require 36-401 as a prerequisite or, in rare cases, instructor permission.

6. Statistical Elective:9–10 units

Students are required to take one* elective which can be within or outside the Department of Statistics and Data Science. Courses within Statistics can be any 300 or 400 level course (that is not used to satisfy any other requirement for the statistics major). 

The following is a partial list of courses outside Statistics that qualify as electives as they provide intellectual infrastructure that will advance the student's understanding of statistics and its applications. Other courses may qualify as well; consult with the Statistics Undergraduate Advisor.

10-601Introduction to Machine Learning (Master's)12
15-110Principles of Computing10
15-112Fundamentals of Programming and Computer Science12
15-121Introduction to Data Structures10
15-122Principles of Imperative Computation10
15-388Practical Data Science9
21-127Concepts of Mathematics10
21-260Differential Equations9
21-292Operations Research I9
21-301Combinatorics9
21-355Principles of Real Analysis I9
80-220Philosophy of Science9
80-221Philosophy of Social Science9
80-310Formal Logic9
85-310Research Methods in Cognitive Psychology9
85-320Research Methods in Developmental Psychology9
85-340Research Methods in Social Psychology9
88-223Decision Analysis9
88-302Behavioral Decision Making9

Note: Additional prerequisites are required for some of these courses. Students should carefully check the course descriptions to determine if additional prerequisites are necessary.

* Students who end up satisfying the theory requirement using 36-700 or 36-705 are required to take two electives only one of which can be outside the Department of Statistics and Data Science. (In general, any waived requirement is replaced by a statistical elective.)

7. Tracks*:

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 the abstract tools to real problems.

The Concentration Area is a set of four related courses outside of Statistics 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 the Statistics Undergraduate Advisor. While these courses are not in Statistics, the concentration area must compliment the overall Statistics 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 graduate work in Statistics could take further courses in advanced Mathematics.

The concentration area can be fulfilled with a minor or additional major, but not all minors and additional majors fulfill this requirement. Please make sure to consult the Undergraduate Statistics Advisor prior to pursuing courses for the concentration area. Once the concentration area is approved, any changes made to the previously agreed upon coursework requires re-approval by the Undergraduate Advisor.

Concentration Approval Process

  • Submit the below materials to the Undergraduate Statistics Advisor
    • List of possible coursework to fulfill the concentration*
    • 150-200 word essay describing how the proposed courses complement the Statistics degree.

* These courses can be amended later, but must be re-approved by the Statistics Undergraduate Advisor.

Mathematical Statistics Track46–52 units
21-127Concepts of Mathematics10
21-355Principles of Real Analysis I9
36-410Introduction to Probability Modeling9

And two of the following:

36-700Probability and Mathematical Statistics12
or 36-705 Intermediate Statistics
21-228Discrete Mathematics9
21-257Models and Methods for Optimization9
21-292Operations Research I9
21-301Combinatorics9
21-356Principles of Real Analysis II9
Statistics and Neuroscience Track45–54 units
85-211Cognitive Psychology9
85-219Biological Foundations of Behavior9

And three electives (at least one from Methodology and Analysis and at least one from Neuroscientific Background):

Methodology and Analysis
36-700Probability and Mathematical Statistics12
or 36-705 Intermediate Statistics
10-601Introduction to Machine Learning (Master's)12
18-290Signals and Systems12
85-314Cognitive Neuroscience Research Methods9
42/86-631Neural Data Analysis9
Neuroscientific Background
03-362Cellular Neuroscience9
03-363Systems Neuroscience9
15-386Neural Computation9
85-414Cognitive Neuropsychology9
85-419Introduction to Parallel Distributed Processing9

* Note: The concentration/track requirement is only for students whose primary major is statistics and have 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:146-185*
Total Number of Units for the Degree:360

* Note: This number can vary depending on the calculus sequence and on the concentration area a student takes. In addition this number includes the 36 units of the “Concentration Area” category which may not be required (see category 7 above for details).

Recommendations

Students in the 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 or 36-201 by the end of their Freshman year.

The linear algebra requirement is a prerequisite for the course 36-401. It is therefore essential to complete this requirement during your junior year at the latest. 

Recommendations for Prospective PhD Students

Students interested in pursuing a PhD in Statistics or Biostatistics (or related programs) after completing their undergraduate degree are strongly recommended to pursue the Mathematical Statistics Track.

Additional Major in Statistics

Students who elect Statistics as a second or third major must fulfill all Statistics 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 typically take additional advanced 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 a Major in Statistics.

Many departments require Statistics courses as part of their Major or Minor programs. Students seeking transfer credit for those requirements from substitute courses (at Carnegie Mellon or elsewhere) should seek permission from their advisor in the department setting the requirement. The final authority in such decisions rests there. The Department Statistics does not provide approval or permission for substitution or waiver of another department's requirements.

If a waiver or substitution is made in the home department, it is not automatically approved in the Department of Statistics and Data Science. In many of these cases, the student will need to take additional courses to satisfy the Statistics major requirements. Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Statistics.

Research

One goal of the Statistics program is to give students experience with statistical research. There is a wide variety of ongoing research projects in the department, and students have several opportunities to get involved in a project that interests them.

Before graduation, students are encouraged to participate in a research project under faculty supervision. Students can do this through projects in specific courses (such as 36-303), through an independent study, or through a summer research position.

Qualified students are also encouraged to participate in an advanced research project through 36-490 Undergraduate Research or independent study under the supervision of a Statistics faculty advisor.  Students who maintain a quality point average of 3.25 overall may also apply to participate in the Dietrich College Senior Honors Program.

Sample Programs

The following sample programs illustrate three (of many) ways to satisfy the requirements of the Statistics Major. 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 first schedule uses calculus sequence 1.

The second schedule is an example of the case when a student enters the program through 36-225 and 36-226 (and therefore skips the beginning data analysis course). The schedule uses calculus sequence 2, and includes two advanced electives (36-315 and 36-303), both within the Department of Statistics and Data Science. This schedule has more emphasis on statistical theory and probability.

The third schedule is an example of the Mathematical Statistics track.

In these schedules, C.A. refers to Concentration Area courses.

Schedule 1

FreshmanSophomore
FallSpringFallSpring
36-200 Reasoning with Data36-202 Methods for Statistics and Data Science21-256 Multivariate Analysis36-315 Statistical Graphics and Visualization
21-111 Differential Calculus21-112 Integral CalculusC.A.21-240 Matrix Algebra with Applications

JuniorSenior
FallSpringFallSpring
36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference36-401 Modern Regression36-402 Advanced Methods for Data Analysis
36-350 Statistical ComputingC.A.C.A.36-46x Special Topics
C.A.

Schedule 2

FreshmanSophomore
FallSpringFallSpring
21-120 Differential and Integral Calculus21-256 Multivariate Analysis36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference
21-240 Matrix Algebra with Applications

JuniorSenior
FallSpringFallSpring
36-350 Statistical Computing36-315 Statistical Graphics and VisualizationC.A.C.A.
36-401 Modern Regression36-402 Advanced Methods for Data Analysis36-46x Special Topics36-303 Sampling, Survey and Society
C.A.C.A.

Schedule 3 - Mathematics Track Only

FreshmanSophomore
FallSpringFallSpring
21-120 Differential and Integral Calculus21-256 Multivariate Analysis36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference
21-260 Differential Equations21-127 Concepts of Mathematics21-241 Matrices and Linear Transformations

JuniorSenior
FallSpringFallSpring
36-350 Statistical Computing36-315 Statistical Graphics and Visualization36-46x Special Topics36-410 Introduction to Probability Modeling
36-401 Modern Regression36-402 Advanced Methods for Data Analysis21-355 Principles of Real Analysis I36-303 Sampling, Survey and Society
21-228 Discrete Mathematics21-341 Linear Algebra

B.S. in Economics and Statistics

Academic Advisor: Samantha Nielsen
Faculty Advisors: Rebecca Nugent and Edward Kennedy
Executive Director, Undergraduate Economics Program: Carol Goldburg
Associate Director, Undergraduate Economics Program: Kathleen Conway
Office: Baker Hall 132
Email: statadvising@stat.cmu.edu

The Major in Economics and Statistics provides an interdisciplinary course of study aimed at students with a strong interest in the empirical analysis of economic data. With joint curriculum from the Department of Statistics and Data Science and the Undergraduate Economics Program, the major provides students with a solid foundation in the theories and methods of both fields. Students in this major are trained to advance the understanding of economic issues through the analysis, synthesis and reporting of data using the advanced empirical research methods of statistics and econometrics. Graduates are well positioned for admission to competitive graduate programs, including those in statistics, economics and management, as well as for employment in positions requiring strong analytic and conceptual skills - especially those in economics, finance, education, and public policy.

All economics courses counting towards an economics degree must be completed with a grade of "C" or higher. 

The requirements for the B.S. in Economics and Statistics are the following:

I. Prerequisites38-39 units

1. Mathematical Foundations38-39 units

Calculus

21-120Differential and Integral Calculus10

and one of the following three:

21-122Integration and Approximation10
21-127Concepts of Mathematics10
21-257Models and Methods for Optimization9

and one of the following:

21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9

Note: Passing the MSC 21-120 assessment test is an acceptable alternative to completing 21-120.

Note: Taking both 21-111 and 21-112 is equivalent to 21-120. The Mathematical Foundations total is then 48-49 units. The Economics and Statistics major would then total 201-211 units.

Linear Algebra

One of the following three courses:

21-240Matrix Algebra with Applications10
21-241Matrices and Linear Transformations10
21-242Matrix Theory10

Note: 21-241 and 21-242 are intended only for students with a very strong mathematical background.

II. Foundations18-36 units

2. Economics Foundations18 units
73-102Principles of Microeconomics9
73-103Principles of Macroeconomics9
3. Statistical Foundations9-18 units

Sequence 1 (For students beginning their freshman or sophomore year)

Beginning*

Choose one of the following courses

36-200Reasoning with Data9
36-201Statistical Reasoning and Practice9
36/70-207Probability and Statistics for Business Applications9
36-220Engineering Statistics and Quality Control9
36-247Statistics for Lab Sciences9

*Or extra data analysis course in Statistics

Note: Students who enter the program with 36-225 or 36-226 should discuss options with an advisor.  Any 36-300 or 36-400 level course in Data Analysis that does not satisfy any other requirement for the Economics and Statistics Major may be counted as a Statistical Elective.

Intermediate*

Choose one of the following courses:

36-202Methods for Statistics and Data Science **9
36-208Regression Analysis9
36-309Experimental Design for Behavioral and Social Sciences9

*Or extra data analysis course in Statistics

**Must take prior to 36-401

Sequence 2 (For students beginning later in their college career)

Advanced
Choose one of the following courses:

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added.

III. Disciplinary Core126 units

1. Economics Core45 units
73-230Intermediate Microeconomics9
73-240Intermediate Macroeconomics9
73-270Strategic Professional Communication for Economists9
73-274Econometrics I9
73-374Econometrics II9
2. Statistics Core36 units
36-225Introduction to Probability Theory *#9

 and one of the following two courses:

36-226Introduction to Statistical Inference *9
36-326Mathematical Statistics (Honors) *9

and both of the following two courses:

36-401Modern Regression *9
36-402Advanced Methods for Data Analysis9

 *In order to be a major in good standing, a grade of C or better is required in 36-225 (or equivalents), 36-226 or 36-326 and 36-401.  Otherwise you will not be allowed to continue in the major.

#It is possible to substitute 36-217 or 21-325 for 36-225. (36-225 is the standard introduction to probability, 36-217 is tailored for engineers and computer scientists, and 21-325 is a rigorous Probability Theory course offered by the Department of Mathematics.)

3. Computing9 units
36-350Statistical Computing *9

*In rare circumstances, a higher level Statistical Computing course, approved by your Statistics advisor, may be used as a substitute.

4. Advanced Electives36 units

Students must take two advanced Economics elective courses (numbered 73-300 through 73-495, excluding 73-374 ) and two advanced Statistics elective courses (numbered 36-303, 36-315, or 36-410 through 36-495).

Students pursuing a degree in Economics and Statistics also have the option of earning a concentration area by completing a set of interconnected electives. While a concentration area is not required for this degree, this is an additional option that allows students to pursue courses that are aligned with a particular career path. The two electives that are already required for this degree could count towards your concentration area, please make sure to consult an advisor when choosing these courses.

Total number of units for the major191-201 units
Total number of units for the degree360 units

Professional Development

Students are strongly encouraged to take advantage of professional development opportunities and/or coursework. One option is 73-210 Economics Colloquim I, a fall-only course that provides information about careers in Economics, job search strategies, and research opportunities. The Department of Statistics and Data Science also offers a series of workshops pertaining to resume preparation, graduate school applications, careers in the field, among other topics. Students should also take advantage of the Career and Professional Development Center. 

Additional Major in Economics and Statistics

Students who elect Economics and Statistics as a second or third major must fulfill all Economics and Statistics degree requirements. Majors in many other programs would naturally complement an Economics and 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 six courses (three Economics and three Statistics) that do not count for their primary major. If students do not have at least six, they typically take additional advanced 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 a Major in Economics and Statistics.

Many departments require Statistics courses as part of their Major or Minor programs. Students seeking transfer credit for those requirements from substitute courses (at Carnegie Mellon or elsewhere) should seek permission from their advisor in the department setting the requirement. The final authority in such decisions rests there. The Department of Statistics and Data Science does not provide approval or permission for substitution or waiver of another department's requirements.

If a waiver or substitution is made in the home department, it is not automatically approved in the Department of Statistics and Data Science. In many of these cases, the student will need to take additional courses to satisfy the Economics and Statistics major requirements. Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Economics and Statistics.

Sample Program

The following sample program illustrates one way to satisfy the requirements of the Economics and Statistics Major.  Keep in mind that the program is flexible and can support other possible schedules (see footnotes below the schedule).

FreshmanSophomore
FallSpringFallSpring
21-120 Differential and Integral Calculus36-202 Methods for Statistics and Data Science21-122 Integration and Approximation **21-240 Matrix Algebra with Applications
36-200 Reasoning with Data21-256 Multivariate Analysis36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference
73-102 Principles of Microeconomics73-103 Principles of Macroeconomics73-230 Intermediate Microeconomics73-240 Intermediate Macroeconomics
-----*----------73-274 Econometrics I
--------------------
-----

JuniorSenior
FallSpringFallSpring
36-350 Statistical Computing36-402 Advanced Methods for Data AnalysisStatistics ElectiveEconomics Elective
36-401 Modern Regression73-270 Strategic Professional Communication for EconomistsEconomics ElectiveStatistics Elective
73-374 Econometrics II---------------
--------------------
---------------

*In each semester, ----- represents other courses (not related to the major) which are needed in order to complete the 360 units that the degree requires.

** Students can also take 21-127 or 21-257. Students should consult with their advisor.

Prospective PhD students might add 21-127 fall of sophomore year, replace 21-240 with 21-241, add 21-260 in spring of junior year and 21-355 in fall of senior year.


B.S. in Statistics and Machine Learning

Academic Advisor: Samantha Nielsen
Faculty Advisors: Ryan Tibshirani and Ann Lee
Office: Baker Hall 132
Email: statadvising@stat.cmu.edu

Students in the Statistics and Machine Learning program develop and master a wide array of skills in computing, mathematics, statistical theory, and the interpretation and display of complex data. In addition, Statistics and Machine Learning majors gain experience in applying statistical tools to real problems in other fields and learn the nuances of interdisciplinary collaboration. This program is geared towards students interested in statistical computation, data science, or “Big Data” problems.  The requirements for the Major in Statistics and Machine Learning are detailed below and are organized by categories.

Curriculum

1. Mathematical Foundations (Prerequisites)49–59 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 and Machine Learning.

Calculus*:

Complete one of the following sequences of mathematics courses at Carnegie Mellon, each of which provides sufficient preparation in calculus:

Sequence 1
21-111Differential Calculus10
21-112Integral Calculus10

and one of the following:

21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9
Sequence 2
21-120Differential and Integral Calculus10

and one of the following:

21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9

Notes:

  • Other sequences are possible, and require approval from the undergraduate advisor.
  • Passing the Mathematical Sciences 21-120 assessment test is an acceptable alternative to completing 21-120
Integration and Approximation
21-122Integration and Approximation10
Linear Algebra**:

Complete one of the following three courses:

21-240Matrix Algebra with Applications10
21-241Matrices and Linear Transformations10
21-242Matrix Theory10

* It is recommended that students complete the calculus requirement during their freshman year.

**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.

Mathematical Theory:
21-127Concepts of Mathematics10

2. Data Analysis45–54 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 and 36-201 draw examples from many fields and satisfy the Dietrich College Core Requirement in Statistical Reasoning. One of these courses is therefore recommended for students in the College. (Note: A score of 4 or 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement). Other courses emphasize examples in business (36-207), engineering and architecture (36-220 ), and the laboratory sciences (36-247 ).

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.

Sequence 1
Beginning*

Choose one of the following courses:

36-200Reasoning with Data9
36-201Statistical Reasoning and Practice9
36/70-207Probability and Statistics for Business Applications9
36-220Engineering Statistics and Quality Control9
36-247Statistics for Lab Sciences9
*Or extra data analysis course in Statistics

Note: Students who enter the program with 36-225 or 36-226 should discuss options with an advisor.  Any 36-300 or 36-400 level course in Data Analysis that does not satisfy any other requirement for a Statistics Major and Minor may be counted as a Statistical Elective.

Intermediate*

Choose one of the following courses:

36-202Methods for Statistics and Data Science **9
36/70-208Regression Analysis9
36-309Experimental Design for Behavioral and Social Sciences9
*Or extra data analysis course in Statistics
**Must take prior to 36-401
Advanced

Choose two of the following courses:

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added.

and take the following two courses:

36-401Modern Regression9
36-402Advanced Methods for Data Analysis9
Sequence 2
Advanced

Choose three of the following courses:

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added.

and take the following two courses:

36-401Modern Regression9
36-402Advanced Methods for Data Analysis9

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 take the following two courses**:

36-225Introduction to Probability Theory9
36-226Introduction to Statistical Inference9
or 36-326 Mathematical Statistics (Honors)


**It is possible to substitute 36-217 or 21-325 for 36-225 . (36-225 is the standard introduction to probability, 36-217 is tailored for engineers and computer scientists, and 21-325 is a rigorous Probability Theory course offered by the Department of Mathematics.) 36-326 Mathematical Statistics (Honors) can be substituted for 36-226 Introduction to Statistical Inference and is considered an honors course.

Comments:

(i) In order to be a Major or a Minor in good standing, a grade of at least a C is required in 36-225 , 36-226 and 36-401. In particular, a grade of C or higher is required in order to be able to continue in the major.

(ii) In special cases, and in consultation with the Statistics Advisor, the theory requirement can be satisfied by taking a single graduate level class 36-700 Probability and Mathematical Statistics or 36-705 Intermediate Statistics which is much more mathematically rigorous. This option should be considered by strong Statistics Majors who are also majoring in Computer Science, Operations Research, or Mathematics and/or who are considering graduate study in Statistics. This option does require special permission from the advisor. Students who end up satisfying the theory requirement by taking either 36-700 or 36-705 are required take an additional statistics elective.

4. Computing64–67 units

Statistical modeling in practice nearly always requires computation in one way or another. Computational algorithms are sometimes treated as “black-boxes”, whose innards the statistician need not pay attention to. But this attitude is becoming less and less prevalent, and today there is much to be gained from a strong working knowledge of computational tools. Understanding the strengths and weaknesses of various methods allows the data analyst to select the right tool for the job; understanding how they can be adapted to work in new settings greatly extends the realm of problems that he/she can solve. While all Majors in Statistics are given solid grounding in computation, extensive computational training is really what sets the Major in Statistics and Machine Learning apart.

36-350Statistical Computing *9
15-112Fundamentals of Programming and Computer Science12
15-122Principles of Imperative Computation10
15-351Algorithms and Advanced Data Structures12
10-601Introduction to Machine Learning (Master's)12
or 10-401 Introduction to Machine Learning (Undergrad)

*In rare circumstances, a higher level Statistical Computing course, approved by your Statistics advisor, may be used as a substitute.

and take one of the following courses:

10-605Machine Learning with Large Datasets12
15-381Artificial Intelligence: Representation and Problem Solving9
15-386Neural Computation9
16-720Computer Vision12
16-311Introduction to Robotics12
11-411Natural Language Processing12
11-761Language and Statistics12
*PhD level ML course as approved by Statistics advisor
** Independent research with an ML faculty member
Total number of units for the major176–198 units
Total number of units for the degree360 units

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 Reasoning with Data or 36-201 Statistical Reasoning and Practice by the end of their Freshman year.

The linear algebra requirement is a prerequisite for the course 36-401 . It is therefore essential to complete this requirement during your junior year at the latest! 

Recommendations for Prospective PhD Students

Students interested in pursuing a PhD in Statistics or Machine Learning (or related programs) after completing their undergraduate degree are strongly recommended to take additional Mathematics courses. They should see a faculty advisor as soon as possible. Students should consider 36-326 Mathematical Statistics (Honors) as an alternative to 36-226 . Although 21-240 Matrix Algebra with Applications is recommended for Statistics majors, students interested in PhD programs should consider taking 21-241 Matrices and Linear Transformations or 21-242 Matrix Theory instead. Additional courses to consider are 21-228 Discrete Mathematics, 21-260 Differential Equations, 21-341 Linear Algebra, 21-355 Principles of Real Analysis I, and 21-356 Principles of Real Analysis II.

Additional experience in programming and computational modeling is also recommended. Students should consider taking more than one course from the list of Machine Learning electives provided under the Computing section.

Additional Major in Statistics and Machine Learning

Students who elect Statistics and Machine Learning as a second or third major must fulfill all degree requirements. 

With respect to double-counting courses, it is departmental policy that students must have at least six courses (three Computer Science/Machine Learning and three Statistics) that do not count for their primary major. If students do not have at least six, they typically take additional advanced 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 a Major in Statistics and Machine Learning.

Many departments require Statistics courses as part of their Major or Minor programs. Students seeking transfer credit for those requirements from substitute courses (at Carnegie Mellon or elsewhere) should seek permission from their advisor in the department setting the requirement. The final authority in such decisions rests there. The Department of Statistics and Data Science does not provide approval or permission for substitution or waiver of another department's requirements.

If a waiver or substitution is made in the home department, it is not automatically approved in the Department of Statistics and Data Science. In many of these cases, the student will need to take additional courses to satisfy the Statistics and Machine Learning major requirements. Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Statistics and Machine Learning.

Sample Programs

The following sample program illustrates one way to satisfy the requirements of the Statistics and Machine Learning program.  Keep in mind that the program is flexible and can support other possible schedules (see footnotes below the schedule). Sample program 1 is for students who have not satisfied the basic calculus requirements. Sample program 2 is for students who have satisfied the basic calculus requirements and choose option 2 for their data analysis courses (see section #2)

Schedule 1

FreshmanSophomore
FallSpringFallSpring
36-200 Reasoning with Data36-202 Methods for Statistics and Data Science36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference
21-120 Differential and Integral Calculus21-256 Multivariate Analysis21-122 Integration and Approximation21-241 Matrices and Linear Transformations
15-112 Fundamentals of Programming and Computer Science15-112 Fundamentals of Programming and Computer Science21-127 Concepts of Mathematics15-122 Principles of Imperative Computation
-----*---------------
--------------------

JuniorSenior
FallSpringFallSpring
15-351 Algorithms and Advanced Data Structures36-402 Advanced Methods for Data Analysis10-601 Introduction to Machine Learning (Master's)10-605 Machine Learning with Large Datasets
36-401 Modern RegressionStat ElectiveStat ElectiveML Elective
--------------------
--------------------
--------------------

*In each semester, ----- represents other courses (not related to the major) which are needed in order to complete the 360 units that the degree requires.

Schedule 2

FreshmanSophomore
FallSpringFallSpring
21-256 Multivariate Analysis15-122 Principles of Imperative Computation36-217 Probability Theory and Random Processes36-226 Introduction to Statistical Inference
15-112 Fundamentals of Programming and Computer Science21-127 Concepts of Mathematics15-351 Algorithms and Advanced Data Structures21-241 Matrices and Linear Transformations
-----*----------Stat Elective
--------------------
--------------------

JuniorSenior
FallSpringFallSpring
36-350 Statistical Computing36-402 Advanced Methods for Data Analysis10-601 Introduction to Machine Learning (Master's)10-605 Machine Learning with Large Datasets
36-401 Modern RegressionStat ElectiveStat ElectiveML Elective
--------------------
--------------------
--------------------

*In each semester, ----- represents other courses (not related to the major) which are needed in order to complete the 360 units that the degree requires.
 

The Minor in Statistics

Academic Advisor: Samantha Nielsen
Faculty Advisor: Peter Freeman
Office: Baker Hall 132M
Email: statadvising@stat.cmu.edu

The Minor in Statistics develops skills that complement major study in other disciplines. The program helps the student master the basics of statistical theory and advanced techniques in data analysis. This is a good choice for deepening understanding of statistical ideas and for strengthening research skills.

In order to get a minor in Statistics a student must satisfy all of the following requirements:

1. Mathematical Foundations (Prerequisites)29–39 units

Calculus:*:

Complete one of the following two sequences of mathematics courses at Carnegie Mellon, each of which provides sufficient preparation in calculus:

Sequence 1
21-111Differential Calculus10
21-112Integral Calculus10

and one of the following:

21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9
Sequence 2
21-120Differential and Integral Calculus10

and one of the following:

21-256Multivariate Analysis9
21-259Calculus in Three Dimensions9

Note: Other sequences are possible, and require approval from the undergraduate advisor.

Note: Passing the Mathematical Sciences 21-120 assessment test if an acceptable alternative to completing 21-120.

Linear Algebra:

Complete one of the following three courses:

21-240Matrix Algebra with Applications10
21-241Matrices and Linear Transformations10
21-242Matrix Theory10

*It is recommended that students complete the calculus requirement during their freshman year. 

**The linear algebra requirement needs to be complete before taking 36-401 Modern Regression or 36-46X Special Topics.

21-241 and 21-242 are intended only for students with a very strong mathematical background.

2. Data Analysis36 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 or 36-201 draw examples from many fields and satisfy the DC College Core Requirement in Statistical Reasoning. One of these courses is therefore recommended for students in the College. (Note: A score of 4 or 5 on the Advanced Placement (AP) Exam in Statistics may be used to waive this requirement). Other courses emphasize examples in business (36-207 ), engineering and architecture (36-220 ), and the laboratory sciences (36-247 ).

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.

Sequence 1 (For students beginning their freshman or sophomore year)
Beginning Data Analysis*

Choose one of the following courses: 

36-200Reasoning with Data9
36-201Statistical Reasoning and Practice9
36/70-207Probability and Statistics for Business Applications9
36-220Engineering Statistics and Quality Control9
36-247Statistics for Lab Sciences9

*Or extra data analysis course in Statistics

Note: Students who enter the program with 36-225 or 36-226 should discuss options with an advisor.  Any 36-300 or 36-400 level course in Data Analysis that does not satisfy any other requirement for a Statistics Major and Minor may be counted as a Statistical Elective.

Intermediate Data Analysis*

Choose one of the following courses:

36-202Methods for Statistics and Data Science **9
36/70-208Regression Analysis9
36-309Experimental Design for Behavioral and Social Sciences9

*Or extra data analysis course in Statistics

**Must take prior to 36-401

Advanced Data Analysis and Methodology

Take the following course:

36-401Modern Regression9

 and one of the following courses:

36-402Advanced Methods for Data Analysis9
36-410Introduction to Probability Modeling9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added.

Sequence 2 (For students beginning later in their college career)
Advanced Data Analysis and Methodology

Take the following course:

36-401Modern Regression9

 and take two of the following courses (one of which must be 400-level):

36-303Sampling, Survey and Society9
36-315Statistical Graphics and Visualization9
36-402Advanced Methods for Data Analysis9
36-410Introduction to Probability Modeling9
36-461Special Topics: Statistical Methods in Epidemiology9
36-462Special Topics: Data Mining9
36-463Special Topics: Multilevel and Hierarchical Models9
36-464Special Topics: Applied Multivariate Methods9
36-490Undergraduate Research9

**Special Topics rotate and new ones are regularly added.

3. Probability Theory and Statistical Theory18 units

To satisfy the theory requirement take the following two courses:

36-225Introduction to Probability Theory9
36-226Introduction to Statistical Inference9
or 36-326 Mathematical Statistics (Honors)

**It is possible to substitute 36-217 or 21-325 for 36-225 . (36-225 is the standard introduction to probability, 36-217 is tailored for engineers and computer scientists, and 21-325 is a rigorous Probability Theory course offered by the Department of Mathematics.) 36-326 Mathematical Statistics (Honors) can be substituted for 36-226 Introduction to Statistical Inference and is considered an honors course.

Comments:

(i) In order to be a Major or a Minor in good standing, a grade of at least a C is required in 36-225 , 36-226 and 36-401. In particular, a grade of C or higher is required in order to be able to continue in the major.

(ii) In special cases, and in consultation with the Statistics Advisor, the theory requirement can be satisfied by taking a single graduate level class 36-700 Probability and Mathematical Statistics or 36-705 Intermediate Statistics which is much more mathematically rigorous. This option should be considered by strong Statistics Majors who are also majoring in Computer Science, Operations Research, or Mathematics and/or who are considering graduate study in Statistics. This option does require special permission from the advisor. Students who end up satisfying the theory requirement by taking either 36-700 or 36-705 are required take an additional statistics elective.

Total number of units required for the minor83 Units

With respect to double-counting courses, it is departmental policy that students must have at least three statistics courses that do not count for their primary major. If students do not have at least three, they typically take additional advanced electives.

Sample Programs for the Minor

The following two sample programs illustrates two (of many) ways to satisfy the requirements of the Statistics Minor. Keep in mind that the program is flexible and can support many other possible schedules.

The first schedule uses calculus sequence 1, and 36-309 to satisfy the intermediate data analysis requirement. The second schedule is an example of the case when a student enters the Minor through 36-225 and 36-226 (and therefore skips the beginning data analysis course). The schedule uses calculus sequence 2, and 36-315 as an elective (to replace the beginning data analysis course).

Schedule 1

FreshmanSophomore
FallSpringFallSpring
21-111 Differential Calculus21-112 Integral Calculus21-256 Multivariate Analysis21-240 Matrix Algebra with Applications
36-201 Statistical Reasoning and Practice36-309 Experimental Design for Behavioral and Social Sciences

JuniorSenior
FallSpringFallSpring
36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference36-401 Modern Regression36-402 Advanced Methods for Data Analysis

Schedule 2

FreshmanSophomore
FallSpringFallSpring
21-120 Differential and Integral Calculus21-256 Multivariate Analysis36-225 Introduction to Probability Theory36-226 Introduction to Statistical Inference

JuniorSenior
FallSpringFallSpring
21-240 Matrix Algebra with Applications36-315 Statistical Graphics and Visualization36-401 Modern Regression36-462 Special Topics: Data Mining

Substitutions and Waivers

Many departments require Statistics courses as part of their Major or Minor programs. Students seeking transfer credit for those requirements from substitute courses (at Carnegie Mellon or elsewhere) should seek permission from their advisor in the department setting the requirement. The final authority in such decisions rests there. The Department of Statistics and Data Science does not provide approval or permission for substitution or waiver of another department's requirements.

However, the Statistics Director of Undergraduate Studies will provide advice and information to the student's advisor about the viability of a proposed substitution. Students should make available as much information as possible concerning proposed substitutions. Students seeking waivers may be asked to demonstrate mastery of the material.

If a waiver or substitution is made in the home department, it is not automatically approved in the Department of Statistics and Data Science. In many of these cases, the student will need to take additional courses to satisfy the Statistics major requirements. Students should discuss this with a Statistics advisor when deciding whether to add an additional major in Statistics.

Statistics Majors and Minors seeking substitutions or waivers should speak to the Academic Advisor in Statistics.

Course Descriptions

Note on Course Numbers

Each Carnegie Mellon course number begins with a two-digit prefix which designates the department offering the course (76-xxx courses are offered by the Department of English, etc.). Although each department maintains its own course numbering practices, typically the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. xx-6xx courses may be either undergraduate senior-level or graduate-level, depending on the department. xx-7xx courses and higher are graduate-level. Please consult the Schedule of Classes each semester for course offerings and for any necessary pre-requisites or co-requisites.

36-200 Reasoning with Data
Spring: 9 units
This course will serve as an introduction to learning how to "reason with data". While still an introductory-level course in the Statistics Department, the focus will be more on thinking about the relationship between the application and the data set and extracting useful statistical information rather than taking primarily a formula-driven approach. There will be an emphasis on thinking through a empirical research problem from beginning to end. Types of data will include continuous and categorical variables, images, text, and networks. Applications will largely drawn from interdisciplinary case studies spanning the humanities, social sciences, and related fields. Methodological topics will include basic exploratory data analysis, elementary probability, hypothesis tests, and empirical research methods. There is no calculus or programming requirement. There will be weekly computer labs for additional hands-on practice. This course is the credit-equivalent and substitute for 36-201 and will be honored appropriately as a pre-requisite for downstream Statistics courses. As such, this course is not currently open to students who have received credit for 36-201, 36/70-207, 36-220, 36-247, or any 300- or 400-level Statistics course.
36-201 Statistical Reasoning and Practice
All Semesters: 9 units
This course will introduce students to the basic concepts, logic, and issues involved in statistical reasoning, as well as basic statistical methods used to analyze data and evaluate studies. The major topics to be covered include methods for exploratory data analysis, an introduction to research methods, elementary probability, and methods for statistical inference. The objectives of this course are to help students develop a critical approach to the evaluation of study designs, data and results, and to develop skills in the application of basic statistical methods in empirical research. An important feature of the course will be the use of the computer to facilitate the understanding of important statistical ideas and for the implementation of data analysis. In addition to three lectures a week, students will attend a computer lab once a week. Examples will be drawn from areas of applications of particular interest to H&SS students. Not open to students who have received credit for 36-207/70-207, 36-220, 36-225, 36-625, or 36-247.

Course Website: http://www.stat.cmu.edu/academics/courselist
36-202 Methods for Statistics and Data Science
Spring: 9 units
This course builds on the principles and methods of statistical reasoning developed in 36-201 (or its equivalents). The course covers simple and multiple regression, analysis of variance methods and logistic regression. Other topics may include non-parametric methods and probability models, as time permits. The objectives of this course is to develop the skills of applying the basic principles and methods that underlie statistical practice and empirical research. In addition to three lectures a week, students attend a computer lab once week for "hands-on" practice of the material covered in lecture. Not open to students who have received credit for: 36-208/70-208, 36-309. Students who have completed or are enrolled in 36-401 prior to completing 36-202, are not able to take/receive credit for 36-202.
Prerequisites: 36-201 or 70-207 or 36-247 or 36-220 or 36-207
Course Website: http://www.stat.cmu.edu/academics/courselist
36-207 Probability and Statistics for Business Applications
Fall: 9 units
This is the first half of a year long sequence in basic statistical methods that are used in business and management. Topics include exploratory and descriptive techniques, probability theory, statistical inference in simple settings, basic categorical analysis, and statistical methods for quality control. Not open to students who have received credit for 36-201, 36-220, 36-625, or 36-247. Cross-listed as 70-207.
Prerequisites: 21-121 or 21-120 or 21-112
Course Website: http://www.stat.cmu.edu/academics/courselist
36-208 Regression Analysis
Spring: 9 units
This is the second half of a year long sequence in basic statistical methods that are used in business and management. Topics include time series, regression and forecasting. In addition to two lectures a week, students will attend a computer lab once a week. Not open to students who have received credit for 36-202, 36-626. Cross-listed as 70-208. Students who have completed 36-401 prior to 36-208 will not receive credit for 36-208.
Prerequisites: (21-120 or 21-112) and (36-220 or 36-201 or 70-207 or 36-207 or 36-247) and (73-100 or 73-102)

Course Website: http://www.stat.cmu.edu/academics/courselist
36-217 Probability Theory and Random Processes
All Semesters: 9 units
This course provides an introduction to probability theory. It is designed for students in electrical and computer engineering. Topics include elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, limit theorems, and an introduction to random processes. Some elementary ideas in spectral analysis and information theory will be given. A grade of C or better is required in order to use this course as a pre-requisite for 36-226 and 36-410. Not open to students who have received credit for 36-225, or 36-625.
Prerequisites: 21-259 or 21-122 or 21-123 or 21-112 or 21-256
Course Website: http://www.stat.cmu.edu/academics/courselist
36-220 Engineering Statistics and Quality Control
All Semesters: 9 units
This is a course in introductory statistics for engineers with emphasis on modern product improvement techniques. Besides exploratory data analysis, basic probability, distribution theory and statistical inference, special topics include experimental design, regression, control charts and acceptance sampling. Not open to students who have received credit for 36-201, 36-207/70-207, 36-226, 36-626, or 36-247, except when AP credit is awarded for 36-201.
Prerequisites: 21-121 or 21-120 or 21-112
Course Website: http://www.stat.cmu.edu/academics/courselist
36-225 Introduction to Probability Theory
Fall: 9 units
This course is the first half of a year long course which provides an introduction to probability and mathematical statistics for students in economics, mathematics and statistics. The use of probability theory is illustrated with examples drawn from engineering, the sciences, and management. Topics include elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, and the central limit theorem. A grade of C or better is required in order to advance to 36-226 and 36-410. Not open to students who have received credit for 36-217 or 36-625.

Course Website: http://www.stat.cmu.edu/academics/courselist
36-226 Introduction to Statistical Inference
Spring: 9 units
This course is the second half of a year long course in probability and mathematical statistics. Topics include maximum likelihood estimation, confidence intervals, hypothesis testing, and properties of estimators, such as unbiasedness and consistency. If time permits there will also be a discussion of linear regression and the analysis of variance. A grade of C or better is required in order to advance to 36-401, 36-402 or any 36-46x course. Not open to students who have received credit for 36-626.
Prerequisites: 21-325 Min. grade C or 36-217 Min. grade C or 36-225 Min. grade C or 15-359 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-247 Statistics for Lab Sciences
Spring: 9 units
This course is a single-semester comprehensive introduction to statistical analysis of data for students in biology and chemistry. Topics include exploratory data analysis, elements of computer programming for statistics, basic concepts of probability, statistical inference, and curve fitting. In addition to two lectures, students attend a computer lab each week. Not open to students who have received credit for 36-201, 36-207/70-207, 36-220, or 36-226.
Prerequisites: 21-121 or 21-120 or 21-112
36-303 Sampling, Survey and Society
Spring: 9 units
This course will revolve around the role of sampling and sample surveys in the context of U.S. society and its institutions. We will examine the evolution of survey taking in the United States in the context of its economic, social and political uses. This will eventually lead to discussions about the accuracy and relevance of survey responses, especially in light of various kinds of nonsampling error. Students will be required to design, implement and analyze a survey sample.
Prerequisites: 36-309 or 73-261 or 70-208 or 36-625 or 36-208 or 36-226 or 36-202 or 36-225 or 88-250

Course Website: http://www.stat.cmu.edu/academics/courselist
36-304 Biostatistics
Fall: 9 units
TBD
36-309 Experimental Design for Behavioral and Social Sciences
Fall: 9 units
Statistical aspects of the design and analysis of planned experiments are studied in this course. A clear statement of the experimental factors will be emphasized. The design aspect will concentrate on choice of models, sample size and order of experimentation. The analysis phase will cover data collection and computation, especially analysis of variance and will stress the interpretation of results. In addition to a weekly lecture, students will attend a computer lab once a week.
Prerequisites: 36-207 or 36-217 or 36-220 or 36-247 or 36-201
Course Website: http://www.stat.cmu.edu/academics/courselist
36-314 Biostatistics
Fall: 9 units
Tbd
36-315 Statistical Graphics and Visualization
Spring: 9 units
Graphical displays of quantitative information take on many forms as they help us understand both data and models. This course will serve to introduce the student to the most common forms of graphical displays and their uses and misuses. Students will learn both how to create these displays and how to understand them. As time permits the course will consider some more advanced graphical methods such as computer-generated animations. Each student will be required to engage in a project using graphical methods to understand data collected from a real scientific or engineering experiment. In addition to two weekly lectures there will be lab sessions where the students learn to use software to aid in the production of appropriate graphical displays.
Prerequisites: 36-202 or 36-208 or 36-226 or 88-250 or 36-309 or 36-625 or 70-208 or 36-225 or 36-303
Course Website: http://www.stat.cmu.edu/academics/courselist
36-326 Mathematical Statistics (Honors)
Spring: 9 units
This course is a rigorous introduction to the mathematical theory of statistics. A good working knowledge of calculus and probability theory is required. Topics include maximum likelihood estimation, confidence intervals, hypothesis testing, Bayesian methods, and regression. A grade of C or better is required in order to advance to 36-401, 36-402 or any 36-46x course. Not open to students who have received credit for 36-625. Prerequisites: 15-359 or 21-325 or 36-217 or 36-225 with a grade of A AND advisor approval. Students interested in the course should add themselves to the waitlist pending review.
Prerequisites: 15-359 Min. grade A or 36-217 Min. grade A or 21-325 Min. grade A or 36-225 Min. grade A
36-350 Statistical Computing
Fall: 9 units
Statistical Computing: An introduction to computing targeted at statistics majors with minimal programming knowledge. The main topics are core ideas of programming (functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction), illustrated through key statistical topics (exploratory data analysis, basic optimization, linear models, graphics, and simulation). The class will be taught in the R language. No previous programming experience required. Pre-requisites: (36-202, 36-208, or 36-309), plus ("computing at Carnegie Mellon" or consent of instructor) and 36-225 co-requisite.
Prerequisites: 36-315 or 36-226 or 36-303 or 36-309 or 70-208 or 36-208 or 36-202
Course Website: http://www.stat.cmu.edu/academics/courselist
36-401 Modern Regression
Fall: 9 units
This course is an introduction to the real world of statistics and data analysis. We will explore real data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. A minimum grade of C in any one of the pre-requisites is required. A grade of C is required to move on to 36-402 or any 36-46x course.
Prerequisites: (36-226 Min. grade C or 36-326 Min. grade C or 36-625 Min. grade C) and (21-241 or 21-240)

Course Website: http://www.stat.cmu.edu/academics/courselist
36-402 Advanced Methods for Data Analysis
Spring: 9 units
This course introduces modern methods of data analysis, building on the theory and application of linear models from 36-401. Topics include nonlinear regression, nonparametric smoothing, density estimation, generalized linear and generalized additive models, simulation and predictive model-checking, cross-validation, bootstrap uncertainty estimation, multivariate methods including factor analysis and mixture models, and graphical models and causal inference. Students will analyze real-world data from a range of fields, coding small programs and writing reports. Prerequisites: 36-401
Prerequisite: 36-401 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-410 Introduction to Probability Modeling
Spring: 9 units
An introductory-level course in stochastic processes. Topics typically include Poisson processes, Markov chains, birth and death processes, random walks, recurrent events, and renewal theory. Examples are drawn from reliability theory, queuing theory, inventory theory, and various applications in the social and physical sciences.
Prerequisites: 36-625 or 36-217 or 36-225 or 21-325
Course Website: http://www.stat.cmu.edu/academics/courselist
36-428 Time Series
Spring: 6 units
The course is designed for graduate students and advanced undergraduate students. It will introduce the analysis and some of the theory of sequences of serially-dependent random variables (known as time series). Students should already have learned mathematical probability and statistics, including multivariate and conditional distributions, linear regression, calculus, matrix algebra, and the fundamentals of complex variables and functions. The focus will be on popular models for time series and the analysis of data that arise in applications.
Prerequisite: 36-401 Min. grade C
36-459 Statistical Models of the Brain
Spring: 12 units
This new course is intended for CNBC students, as an additional option for fulfilling the computational core course requirement, but it will also be open to Statistics and Machine Learning students. It should be of interest to anyone wishing to see the way statistical ideas play out within the brain sciences, and it will provide a series of case studies on the role of stochastic models in scientific investigation. Statistical ideas have been part of neurophysiology and the brainsciences since the first stochastic description of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago. Many contemporary theories of neural system behavior are built with statistical models. For example, integrate-and-fire neurons are usually assumed to be driven in part by stochastic noise; the role of spike timing involves the distinction between Poisson and non-Poisson neurons; and oscillations are characterized by decomposing variation into frequency-based components. In the visual system, V1 simple cells are often described using linear-nonlinear Poisson models; in the motor system, neural response may involve direction tuning; and CA1 hippocampal receptive field plasticity has been characterized using dynamic place models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities; and some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models incorporating random ``noise'' components are used as an effective vehicle for data analysis. In neuroscience, however, the models also help form a conceptual framework for understanding neural function. This course will examine some of the most important methods and claims that have come from applying statistical thinking
Prerequisite: 36-401 Min. grade C
36-461 Special Topics: Statistical Methods in Epidemiology
Intermittent: 9 units
Epidemiology is concerned with understanding factors that cause, prevent, and reduce diseases by studying associations between disease outcomes and their suspected determinants in human populations. Epidemiologic research requires an understanding of statistical methods and design. Epidemiologic data is typically discrete, i.e., data that arise whenever counts are made instead of measurements. In this course, methods for the analysis of categorical data are discussed with the purpose of learning how to apply them to data. The central statistical themes are building models, assessing fit and interpreting results. There is a special emphasis on generating and evaluating evidence from observational studies. Case studies and examples will be primarily from the public health sciences.
Prerequisite: 36-401 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-462 Special Topics: Data Mining
Intermittent: 9 units
Data mining is the science of discovering patterns and learning structure in large data sets. Covered topics include information retrieval, clustering, dimension reduction, regression, classification, and decision trees. Prerequisites: 36-401 (C or better).
Prerequisite: 36-401 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-463 Special Topics: Multilevel and Hierarchical Models
Intermittent: 9 units
Multilevel and hierarchical models are among the most broadly applied "sophisticated" statistical models, especially in the social and biological sciences. They apply to situations in which the data "cluster" naturally into groups of units that are more related to each other than they are the rest of the data. In the first part of the course we will review linear and generalized linear models. In the second part we will see how to generalize these to multilevel and hierarchical models and relate them to other areas of statistics, and in the third part of the course we will learn how Bayesian statistical methods can help us to build, estimate and diagnose problems with these models using a variety of data sets and examples.
Prerequisite: 36-401 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-464 Special Topics: Applied Multivariate Methods
Intermittent: 9 units
This course is an introduction to applied multivariate methods. Topics include a discussion of the multivariate normal distribution, the multivariate linear model, repeated measures designs and analysis, principle component and factor analysis. Emphasis is on the application and interpretation of these methods in practice. Students will use at least one statistical package. Prerequisites: 36-401 (C or better).
Prerequisite: 36-401 Min. grade C

Course Website: http://www.stat.cmu.edu/academics/courselist
36-468 Special Topics
Intermittent: 9 units
TDB
36-490 Undergraduate Research
Spring: 9 units
This course is designed to give undergraduate students experience using statistics in real research problems. Small groups of students will be matched with clients and do supervised research for a semester. Students will gain skills in approaching a research problem, critical thinking, statistical analysis, scientific writing, and conveying and defending their results to an audience. Eligible students will receive information about the application processes for this course early in the fall.
Prerequisite: 36-401
Course Website: http://www.stat.cmu.edu/academics/courselist
36-492 Topic Detection and Document Clustering
Intermittent: 6 units
Imagine if someone read all your email. Everything you sent, everything you received. What would they find? Do you have repeating topics? How do the topics change over time? The Enron Corporation was an energy, commodities, and services company in Houston, Texas that went spectacularly bankrupt in 2001 after it was revealed that it was engaging in systematic, planned accounting fraud. At its peak, it employed over 20,000 people with revenues over $100 billion. Its downfall was related to deregulation of California's energy commodity trading and a series of rolling power blackouts over months. For example, Enron traders encouraged the removal of power during the energy crisis by suggesting plant shutdowns. The resulting increase in the price for power made them a fortune. After Enron's collapse, journalists used the Freedom of Information Act to release the emails sent/received by the employees of Enron. Subsequently, the emails were analyzed to see who knew what and when. Every news article, email, letter, blog, tweet, etc can be thought of as an observation. We characterize these documents by their length, what words they use and how often, and possibly extra information like the time, the recipient, etc. Topic detection and document clustering methods are statistical and machine learning tools that extract and identify related documents, possibly over time. These methods need to be flexible enough to handle both very small and very large clusters of documents, topics that change in importance, and topics that appear and disappear. This class will emphasize application of methods and real-world data analysis. Class time will be split into lecture and "lab". (Bring your laptop.) Occasional homeworks and final project, but mostly we'll focus on the downfall of Enron as our overarching case study.
Prerequisite: 36-401
36-494 Astrostatistics
Intermittent: 6 units
Since a young age, many of us have pondered the vastness and beauty of the Universe as we gazed up at the night sky. Planets, moons, stars, galaxies, and beyond have fascinated humanity for centuries. It turns out it also provides a plethora of interesting and complex statistical problems. In this course, problems in astronomy, cosmology, and astrophysics are going provide motivation for learning about some advanced statistical methodology. Possible topics include computational statistics, topological data analysis, nonparametric regression, spatial statistics, and statistical learning. While exploring newer statistical methodology, we will get to sample a variety of problems that appeal to astrostatisticians Statistical problems related to exoplanets (planets orbiting stars outside our Solar System), the large-scale structure of the Universe (the "Cosmic Web''), dark matter (over 80% of the matter in the Universe is thought to be invisible), Type Ia supernova (a dying star eats its companion star until explodes), cosmic microwave background (a.k.a. "baby pictures of the Universe'') are some possibilities. This course will be suitable for advanced undergraduate statistics majors through Ph.D. level statistics students, and astronomy Ph.D. students with some background in statistics.
Prerequisite: 36-401 Min. grade C
36-625 Probability and Mathematical Statistics I
Fall: 12 units
This course is a rigorous introduction to the mathematical theory of probability, and it provides the necessary background for the study of mathematical statistics and probability modeling. A good working knowledge of calculus is required. Topics include combinatorial analysis, conditional probability, generating functions, sampling distributions, law of large numbers, and the central limit theorem. Undergraduate students studying Computer Science, or considering graduate work in Statistics or Operations Research, must receive permission from their advisor and from the instructor. Prerequisite: 21-122 and 21-241 and (21-256 or 21-259).
Prerequisites: 21-123 or 21-256 or 21-118 or 21-122
36-626 Probability and Mathematical Statistics II
Intermittent: 12 units
An introduction to the mathematical theory of statistical inference. Topics include likelihood functions, estimation, confidence intervals, hypothesis testing, Bayesian inference, regression, and the analysis of variance. Not open to students who have received credit for 36-226. Students studying Computer Science should carefully consider taking this course instead of 36-220 or 36-226 after consultation with their advisor. Prerequisite: 36-625.
Prerequisite: 36-625
36-665 Special Topics
Intermittent: 9 units
TDB
36-668 Tbd
Intermittent: 9 units
TBD
36-692 Topic Detection and Document Clustering
Intermittent: 6 units
Imagine if someone read all your email. Everything you sent, everything you received. What would they find? Do you have repeating topics? How do the topics change over time? The Enron Corporation was an energy, commodities, and services company in Houston, Texas that went spectacularly bankrupt in 2001 after it was revealed that it was engaging in systematic, planned accounting fraud. At its peak, it employed over 20,000 people with revenues over $100 billion. Its downfall was related to deregulation of California's energy commodity trading and a series of rolling power blackouts over months. For example, Enron traders encouraged the removal of power during the energy crisis by suggesting plant shutdowns. The resulting increase in the price for power made them a fortune. After Enron's collapse, journalists used the Freedom of Information Act to release the emails sent/received by the employees of Enron. Subsequently, the emails were analyzed to see who knew what and when. Every news article, email, letter, blog, tweet, etc can be thought of as an observation. We characterize these documents by their length, what words they use and how often, and possibly extra information like the time, the recipient, etc. Topic detection and document clustering methods are statistical and machine learning tools that extract and identify related documents, possibly over time. These methods need to be flexible enough to handle both very small and very large clusters of documents, topics that change in importance, and topics that appear and disappear. This class will emphasize application of methods and real-world data analysis. Class time will be split into lecture and "lab". (Bring your laptop.) Occasional homeworks and final project, but mostly we'll focus on the downfall of Enron as our overarching case study.
36-700 Probability and Mathematical Statistics
Fall: 12 units
This is a one-semester course covering the basics of statistics. We will first provide a quick introduction to probability theory, and then cover fundamental topics in mathematical statistics such as point estimation, hypothesis testing, asymptotic theory, and Bayesian inference. If time permits, we will also cover more advanced and useful topics including nonparametric inference, regression and classification. Prerequisites: one- and two-variable calculus and matrix algebra.
36-705 Intermediate Statistics
Fall: 12 units
This course covers the fundamentals of theoretical statistics. Topics include: probability inequalities, point and interval estimation, minimax theory, hypothesis testing, data reduction, convergence concepts, Bayesian inference, nonparametric statistics, bootstrap resampling, VC dimension, prediction and model selection.
36-721 Statistical Graphics and Visualization
Intermittent: 6 units
Graphical displays of quantitative information take on many forms to help us understand both data and models. This course will serve to introduce the student to the most common forms of graphical displays and their uses and misuses. Students will learn both how to create these displays and how to understand them. The class will also cover some principles of visual perception and estimation. We will start with univariate and bivariate data, looking at some commonly used graphs and, after discussing their advantages/disadvantages, then turning to more sophisticated tools. We will then explore some three-dimensional tools, group structure/clustering, and projections of higher dimensional data. As time permits, the course will consider some more advanced graphical models such as statistical maps, networks, and the usage of icons.
36-746 Statistical Methods for Neuroscience and Psychology
Intermittent: 12 units
This course provides a survey of basic statistical methods, emphasizing motivation from underlying principles and interpretation in the context of neuroscience and psychology. Though 36-746 assumes only passing familiarity with school-level statistics, it moves faster than typical university-level first courses. Vectors and matrices will be used frequently, as will basic calculus. Topics include Probability, Random Variables, and Important Distributions (binomial, Poisson, and normal distributions; the Law of Large Numbers and the Central Limit Theorem); Estimation and Uncertainty (standard errors and confidence intervals; the bootstrap); Principles of Estimation (mean squared error; maximum likelihood); Models, Hypotheses, and Statistical Significance (goodness-of-fit, p-values; power); General methods for testing hypotheses (permutation, bootstrap, and likelihood ratio tests); Linear Regression (simple linear regression and multiple linear regression); Analysis of Variance (one-way and two-way designs; multiple comparisons); Generalized Linear and Nonlinear Regression (logistic and Poisson regression; generalized linear models); and Nonparametric regression (smoothing scatterplots; smoothing histograms).
36-762 Data Privacy
Fall: 6 units
Protection of individual data is a growing problem due to the large amount of sensitive and personal data being collected, stored, analyzed, and shared across multiple domains and stakeholders. Researchers are facing new policies and technical requirements imposed by funding agencies on accessing and sharing of the research data. This course will introduce students to (1) key principles associated with the concepts of confidentiality and privacy protection, and (2) techniques for data sharing that support useful statistical inference while minimizing the disclosure of sensitive personal information. Methodologies to be considered will include tools for disclosure limitation used by government statistical agencies and those associated with the approach known as differential privacy which provides a formal privacy guaranteed. Students will explore specific techniques using special tools in R.
36-765 Writing in Statistics
Intermittent: 3 units
TBD
36-777 Topics in Modern Multivariate Analysis I
Intermittent: 6 units
This is the first part of a semester-long course on modern multivariate analysis.  In this MINI we will cover basic concepts about random vectors, multivariate Gaussian, and inference tools such as mean and covariance estimation and testing, multivariate analysis of variance, discriminant analysis, principal components analysis, and, if time permits, canonical correlation analysis, clustering analysis. Relevant matrix algebra results will be emphasized as a useful tool.
36-779 Topics in Modern Multivariate Analysis II
Intermittent: 6 units
This is the second part of a semester-long course on modern multivariate analysis.  In this MINI we will introduce recent research results focusing on high dimensional multivariate analysis.  Topics include high dimensional mean and covariance testing, kernel based methods, structured high dimensional subspace estimation (sparse PCA, functional data), and network data.
36-791 Central Limit Theorem in High-Dimensions
Intermittent: 6 units
TBD
36-792 Topic Detection and Document Clustering
Intermittent: 6 units
Imagine if someone read all your email. Everything you sent, everything you received. What would they find? Do you have repeating topics? How do the topics change over time? The Enron Corporation was an energy, commodities, and services company in Houston, Texas that went spectacularly bankrupt in 2001 after it was revealed that it was engaging in systematic, planned accounting fraud. At its peak, it employed over 20,000 people with revenues over $100 billion. Its downfall was related to deregulation of California's energy commodity trading and a series of rolling power blackouts over months. For example, Enron traders encouraged the removal of power during the energy crisis by suggesting plant shutdowns. The resulting increase in the price for power made them a fortune. After Enron's collapse, journalists used the Freedom of Information Act to release the emails sent/received by the employees of Enron. Subsequently, the emails were analyzed to see who knew what and when. Every news article, email, letter, blog, tweet, etc can be thought of as an observation. We characterize these documents by their length, what words they use and how often, and possibly extra information like the time, the recipient, etc. Topic detection and document clustering methods are statistical and machine learning tools that extract and identify related documents, possibly over time. These methods need to be flexible enough to handle both very small and very large clusters of documents, topics that change in importance, and topics that appear and disappear. This class will emphasize application of methods and real-world data analysis. Class time will be split into lecture and "lab". (Bring your laptop.) Occasional homeworks and final project, but mostly we'll focus on the downfall of Enron as our overarching case study.

Faculty

DAVID CHOI, Assistant Professor of Statistics and Information Systems – Ph.D., Stanford University; Carnegie Mellon, 2004–.

ALEXANDRA CHOULDECHOVA, Assistant Professor of Statistics and Public Policy – Ph.D. , Stanford University; Carnegie Mellon, 2014–.

MAX G'SELL, Assistant Professor – Ph.D., Stanford University ; Carnegie Mellon, 2014–.

CHRISTOPHER R. GENOVESE, Department Head and Professor of Statistics – Ph.D., University of California, Berkeley; Carnegie Mellon, 1994–.

JOEL B. GREENHOUSE, Professor of Statistics – Ph.D., University of Michigan; Carnegie Mellon, 1982–.

AMELIA HAVILAND, Anna Loomis McCandless Professorship of Statistics and Public Policy – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003–.

JIASHUN JIN, Professor of Statistics – Ph.D., Stanford University; Carnegie Mellon, 2007–.

BRIAN JUNKER, Associate Dean and Professor of Statistics – Ph.D., University of Illinois; Carnegie Mellon, 1990–.

ROBERT E. KASS, Professor of Statistics – Ph.D., University of Chicago; Carnegie Mellon, 1981–.

EDWARD KENNEDY, Assistant Professor – Ph.D., University of Pennsylvania; Carnegie Mellon, 2016–.

ANN LEE, Associate Professor – Ph.D., Brown University; Carnegie Mellon, 2005–.

JOHN P. LEHOCZKY, Thomas Lord Professor of Statistics – Ph.D., Stanford University; Carnegie Mellon, 1969–.

JING LEI, Assistant Professor – Ph.D., University of California, Berkeley; Carnegie Mellon, 2011–.

DANIEL NAGIN, Teresa and H. John Heinz III Professor of Public Policy – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1976–.

MATEY NEYKOV, Assistant Professor – Ph.D., Harvard University; Carnegie Mellon, 2017–.

NYNKE NIEZINK, Assistant Professor – Ph.D., University of Groningen; Carnegie Mellon, 2017–.

REBECCA NUGENT, Associate Department Head, Teaching Professor – Ph.D., University of Washington; Carnegie Mellon, 2006–.

ALESSANDRO RINALDO, Associate Professor – Ph.D., Carnegie Mellon; Carnegie Mellon, 2005–.

KATHRYN ROEDER, Professor of Statistics – Ph.D., Pennsylvania State University; Carnegie Mellon, 1994–.

CHAD M. SCHAFER, Associate Professor – Ph.D., University of California, Berkeley; Carnegie Mellon, 2004–.

MARK J. SCHERVISH, Professor of Statistics – Ph.D., University of Illinois; Carnegie Mellon, 1979–.

TEDDY SEIDENFELD, Herbert A. Simon Professor of Philosophy and Statistics – Ph.D., Columbia University; Carnegie Mellon, 1985–.

COSMA SHALIZI, Associate Professor – Ph.D., University of Wisconsin, Madison; Carnegie Mellon, 2005–.

DALENE STANGL, Teaching Professor – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2017–.

RYAN TIBSHIRANI, Associate Professor – Ph.D., Stanford University; Carnegie Mellon, 2011–.

VALERIE VENTURA, Associate Professor – Ph.D., University of Oxford; Carnegie Mellon, 1997–.

LARRY WASSERMAN, Professor of Statistics – Ph.D., University of Toronto; Carnegie Mellon, 1988–.

Emeriti Faculty

GEORGE T. DUNCAN, Professor of Statistics and Public Policy – Ph.D., University of Minnesota; Carnegie Mellon, 1974–.

WILLIAM F. EDDY, John C. Warner Professor of Statistics – Ph.D, Yale University; Carnegie Mellon, 1976–.

JOSEPH B. KADANE, Leonard J. Savage Professor of Statistics and Social Sciences – Ph.D., Stanford University; Carnegie Mellon, 1969–.

Adjunct Faculty

ANTHONY BROCKWELL, – Ph.D., Melbourne University; Carnegie Mellon, 1999–.

BERNIE DEVLIN, – Ph.D., Pennsylvania State University; Carnegie Mellon, 1994–.

APRIL GALYARDT, – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2017–.

ROSS O'CONNELL, .

SAM VENTURA, – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2015–.

Special Faculty

OLGA CHILINA, Lecturer – MS, University of Toronto; Carnegie Mellon, 2006–.

PETER FREEMAN, Research Associate – Ph.D., University of Chicago; Carnegie Mellon, 2004–.

HOWARD SELTMAN, Senior Research Statistician – Ph.D., Carnegie Mellon; M.D., Medical College of Pennsylvania; Carnegie Mellon, 1999–.

ISABELLA VERDINELLI, Professor in Residence – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1991–.

GORDON WEINBERG, Lecturer – M.A. Mathematics, University of Pittsburgh; Carnegie Mellon, 2004–.