Mail Code: 94305-4065
Email: mcs-inquiries@stanford.edu
Web Site: https://mcs.stanford.edu/
Courses offered by Mathematical and Computational Science program are listed under the subject code MCS on the Stanford Bulletin's ExploreCourses website.
This interdisciplinary undergraduate degree program in MCS is sponsored by Stanford's departments of Statistics, Mathematics, Computer Science, and Management Science & Engineering, providing students with a core of mathematics basic to all the mathematical sciences and an introduction to concepts and techniques of computation, optimal decision making, probabilistic modeling, and statistical inference.
Utilizing the faculty and courses of the departments listed above, this major prepares students for graduate study or employment in the mathematical and computational sciences or in those areas of applied mathematics which center around the use of computers and are concerned with the problems of the social and management sciences. A biology option is offered for students interested in applications of mathematics, statistics, and computer science to the biological sciences (bioinformatics, computational biology, statistical genetics, neurosciences); and in a similar spirit, an engineering and statistics option.
Undergraduate Mission Statement for Mathematical and Computational Science
The mission of the Mathematical and Computational Science Program is to provide students with a core of mathematics basic to all the mathematical sciences and an introduction to concepts and techniques of computation, optimal decision making, probabilistic modeling and statistical inference. The program is interdisciplinary in its focus, and students are required to complete course work in mathematics, computer science, statistics, and management science and engineering. A computational biology track is available for students interested in biomedical applications. The program prepares students for careers in academic, financial and government settings as well as for study in graduate or professional schools.
Learning Outcomes
The program expects undergraduate majors to be able to demonstrate the following learning outcomes. These learning outcomes are used in evaluating students and the department's undergraduate program. Students are expected to be able to demonstrate:
- understanding of principles and tools of statistics.
- command of optimization and its applications and the ability to analyze and interpret problems from various disciplines.
- an understanding of computer applications emphasizing modern software engineering principles.
- an understanding of multivariate calculus, linear algebra, and algebraic and geometric proofs.
Bachelor of Science in Mathematical and Computational Science
The Program in Mathematical and Computational Science (MCS) offers a Bachelor of Science in Mathematical and Computational Science. Eligible students may also pursue a Bachelor of Science with Honors. The department also offers a minor in Mathematical and Computational Science.
Suggested Preparation for the Major
Students ordinarily would have taken two of the required Math courses (MATH 51 Linear Algebra, Multivariable Calculus, and Modern Applications/MATH 52 Integral Calculus of Several Variables/MATH 53 Ordinary Differential Equations with Linear Algebra) and one of the required Statistics core courses (STATS 116 Theory of Probability, STATS 191 Introduction to Applied Statistics) before declaring MCS during their freshman or sophomore year.
How to Declare the Major
To declare the major, a student should first meet with an MCS peer advisor to create a proposed study plan and then with the MCS student services officer to discuss the major. Students ordinarily have taken two of the required MATH 50 series courses and a core Statistics course prior to declaration. Once the student has created a proposed study plan, they should connect with the MCS student services officer and declare the major through Axess. Students should have an overall grade point average (GPA) of 3.0 to declare.
Degree Requirements
- The student must have a grade point average (GPA) of 3.0 or better in all course work used to fulfill the major requirement.
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At least three quarters before graduation, majors must file with their advisor a plan for completing degree requirements.
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All courses used to fulfill major requirements must be taken for a letter grade with the exception of courses offered satisfactory/no credit only.
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Students who earn less than a 'C+' in STATS 116 Theory of Probability or STATS 200 Introduction to Statistical Inference must repeat the course.
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Only one MCS core course can be substituted by filing a petition with their advisor (with the exception of STATS 200 Introduction to Statistical Inference which cannot be substituted). The Course Substitution Form must be submitted the quarter prior to enrolling in the course.
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Course transfer credit is subject to department evaluation and to the Office of the Registrar's external credit evaluation. These courses may result in a replacement course for MCS required course or may establish placement in a higher-level course. Transfer requests must first be submitted to Student Services Center prior to being evaluated by your advisor. Submit the MCS Program Transfer Credit Form to the student services office.
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Students may take their three electives courses for credit (CR).
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Students may be granted a one-time exception to take a core course for credit (CR) with the exception of STATS 116 and STATS 200.
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The University requires students to complete at least one approved writing-intensive course in each of their majors. See the Hume Center for Writing and Speaking web site for a full description of the WIM requirement.
Course Requirements
| Units | ||
|---|---|---|
| Mathematics (MATH) | 28 | |
| Single-variable calculus or AP credit. 1 | ||
| MATH 19 | Calculus | 3 |
| MATH 20 | Calculus | 3 |
| MATH 21 | Calculus | 4 |
| Students may choose one of the following sequences: | 15 | |
| Multivariable Calculus and Linear Algebra | ||
| Linear Algebra, Multivariable Calculus, and Modern Applications | ||
| Integral Calculus of Several Variables | ||
| Ordinary Differential Equations with Linear Algebra | ||
| Modern Mathematics: Continuous Methods (a proof-oriented sequence) | ||
| Modern Mathematics: Continuous Methods | ||
| Modern Mathematics: Continuous Methods | ||
| Modern Mathematics: Continuous Methods | ||
| Modern Mathematics: Discrete Methods (a proof-oriented sequence) | ||
| Modern Mathematics: Discrete Methods | ||
| Modern Mathematics: Discrete Methods | ||
| Modern Mathematics: Discrete Methods | ||
| Select one of the following: | 3 | |
| Applied Matrix Theory | ||
| Linear Algebra and Matrix Theory | ||
| Computer Science (CS) | 22-25 | |
| CS 103 | Mathematical Foundations of Computing | 5 |
| CS 106A | Programming Methodology | 5 |
| and either | ||
| CS 106B | Programming Abstractions | 5 |
| or CS 106X | Programming Abstractions | |
| Select two of the following: | 7-10 | |
| Introduction to Scientific Computing | ||
| Computer Organization and Systems | ||
| Introduction to the Theory of Computation | ||
| Design and Analysis of Algorithms | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Management Science and Engineering (MS&E) | 7-11 | |
| MS&E 211X | Introduction to Optimization (Accelerated) | 3-4 |
| MS&E 221 | Stochastic Modeling | 3 |
| Or select three of the following: | 9-11 | |
| Introduction to Optimization | ||
| Introduction to Stochastic Modeling | ||
| Introduction to Optimization | ||
| Introduction to Optimization Theory | ||
| Stochastic Modeling | ||
| Introduction to Stochastic Control with Applications | ||
| Statistics (STATS) | 10-11 | |
| STATS 116 | Theory of Probability | 3-4 |
| or MATH 151 | Introduction to Probability Theory | |
| STATS 200 | Introduction to Statistical Inference | 4 |
| Select one of the following: | 3 | |
| Introduction to Applied Statistics | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Writing in the Major (WIM) | 3-5 | |
| Choose one from the MCS-designated WIM courses to fulfill the Writing in the Major requirement: | ||
| Applied Group Theory | ||
| Applied Number Theory and Field Theory | ||
| Groups and Rings | ||
| Fundamental Concepts of Analysis | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Modern Statistics for Modern Biology | ||
| WIM courses offered by other majors may be used in cases of specific concentrations (e.g. biology, decision theory). Advisor approval required. | ||
| Mathematical and Computational Science Approved Electives | 9 | |
| Choose three courses in Mathematical and Computational Science 100-level or above, at least 3 units each from two different departments. | ||
| Choose three electives: | ||
| Advanced Topics in Econometrics | ||
| Introduction to Financial Economics | ||
| Game Theory and Economic Applications | ||
| Experimental Economics | ||
| The Fourier Transform and Its Applications | ||
| Introduction to Linear Dynamical Systems | ||
| Introduction to Statistical Signal Processing | ||
| Computer Systems Architecture | ||
| Convex Optimization I | ||
| Convex Optimization II | ||
| Probabilistic Analysis | ||
| Simulation | ||
| Fundamentals of Data Science: Prediction, Inference, Causality | ||
| Introduction to Stochastic Control with Applications | ||
| Topics in Social Data | ||
| Applied Matrix Theory | ||
| Functions of a Complex Variable | ||
| Graph Theory | ||
| Introduction to Combinatorics and Its Applications | ||
| Linear Algebra and Matrix Theory | ||
| Introduction to Scientific Computing | ||
| Functions of a Real Variable | ||
| Complex Analysis | ||
| Partial Differential Equations | ||
| Stochastic Processes | ||
| Basic Probability and Stochastic Processes with Engineering Applications | ||
| Discrete Probabilistic Methods | ||
| Fundamental Concepts of Analysis | ||
| Lebesgue Integration and Fourier Analysis | ||
| Metalogic | ||
| Mathematics of Sports | ||
| Data Science 101 | ||
| Data Mining and Analysis | ||
| Applied Multivariate Analysis | ||
| Introduction to Time Series Analysis | ||
| Bootstrap, Cross-Validation, and Sample Re-use | ||
| Statistical Models in Biology | ||
| Introduction to Statistical Learning | ||
| Introduction to Stochastic Processes I | ||
| Introduction to Stochastic Processes II | ||
| Stochastic Processes | ||
| Statistical Methods in Finance | ||
| A Course in Bayesian Statistics | ||
| For Computer Science (CS), electives can include courses not taken as units under the CS list above and the following: | ||
| Introduction to Numerical Methods for Engineering | ||
| Software Development for Scientists and Engineers | ||
| Numerical Linear Algebra | ||
| Object-Oriented Systems Design | ||
| Principles of Computer Systems | ||
| Operating Systems and Systems Programming | ||
| Compilers | ||
| Computational Logic | ||
| Design and Analysis of Algorithms | ||
| Software Project | ||
| Artificial Intelligence: Principles and Techniques | ||
| Introduction to Robotics | ||
| Experimental Robotics | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Program Analysis and Optimizations | ||
| Mining Massive Data Sets | ||
| Interactive Computer Graphics | ||
| Electives that are not offered this year, but may be offered in subsequent years, are eligible for credit toward the major. | ||
| With the advisor's approval, courses other than those listed or offered by the sponsoring departments may be used to fulfill part of the elective requirement. Courses must provide skills relevant to the MCS degree and do not overlap courses in the student's program. Depending on student’s interests, these may be in fields such as, biology, economics, electrical engineering, industrial engineering, and medicine, are otherwise relevant to a mathematical sciences major. | ||
| Total Units | 76-89 | |
| 1 | Students who scored a 5 on both the Calculus AB and BC advanced placement exams (total of 10 units) can be waived out of MATH 19 Calculus, MATH 20 Calculus, MATH 21 Calculus; See also the Registrar's Advanced Placement web site (AP or IB exams). Students who place out of MATH 19, 20, and 21 are required to take additional Math classes as discussed with MCS student services and the student's faculty advisor. |
Mathematical and Computational Science Tracks
MCS program has designed three tracks to allow majors to pursue their interests in fields where applied mathematics and statistical analysis is utilized. Declared MCS majors are not required to choose a track. These tracks are not declared in Axess and are not printed on the transcript or diploma.
Biology Track
Students in the Biology track take the introductory courses for the Mathematics and Computational Science major with the following allowable substitutions as electives.
| Units | ||
|---|---|---|
| STATS/BIO 141 | Biostatistics 1 | 5 |
| Allowable Elective Course Substitutions: | ||
| Take three courses from Foundational Biology Core: | 10 | |
| Genetics | ||
| Biochemistry & Molecular Biology | ||
| Physiology | ||
| Evolution | ||
| Cell Biology | ||
| Or take two courses from the Biology core and one of the following: | 3-4 | |
| Advance Molecular Biology: Epigenetics and Proteostasis | ||
BIO 133 | (no longer offered) | |
| Conservation Biology: A Latin American Perspective | ||
| Theoretical Population Genetics (offered alternate years) | ||
| Molecular and Cellular Immunology | ||
| Honors students select the following three courses: | 1-4 | |
| Modern Statistics for Modern Biology | ||
| Fundamentals of Molecular Evolution | ||
| Genes and Disease (no longer offered) | ||
| The following courses are no longer offered, but may be used by students who completed them in fulfillment of this requirement: BIO102, 160A & 160B | ||
| 1 | STATS 141: Biostatistics (BIO 141) can replace STATS 191 Introduction to Applied Statistics or STATS 203 Introduction to Regression Models and Analysis of Variance from the major's Statistics core requirement. |
Engineering Track
Students in the Engineering track take the introductory courses for the Mathematics and Computational Sciences major with the following allowable substitutions.
| Units | ||
|---|---|---|
| With consent of an MCS advisor, MATH 51, MATH 52, MATH 53 series may be substituted for CME 100, CME 102, CME 104. Depending on the exact material taught in relevant years, an additional math course may be necessary 1 | 15 | |
| Vector Calculus for Engineers | ||
| Ordinary Differential Equations for Engineers | ||
| Linear Algebra and Partial Differential Equations for Engineers | ||
| STATS 116 may be replaced by: | 3-5 | |
| Statistical Methods in Engineering and the Physical Sciences | ||
| STATS 191/STATS 203 may be replaced by: | 3-4 | |
| Data Mining and Analysis | ||
| Allowable Elective Course Substitutions: | 9 | |
| Select one of the following: | 3-4 | |
| Functions of a Complex Variable | ||
| Introduction to Combinatorics and Its Applications | ||
| Complex Analysis | ||
| Metalogic | ||
| Select two of the following: | 3-5 | |
| Dynamics | ||
| Introduction to Chemical Engineering | ||
ENGR 25B | ||
ENGR 40 | (no longer offered) | |
| Introduction to Materials Science, Nanotechnology Emphasis | ||
| Feedback Control Design | ||
| 1 | Only MCS majors pursuing the engineering track may petition their advisor to substitute the required Math series for CME courses listed above. |
Statistics Track
Students in the Statistics track take the introductory courses for the Mathematics and Computational Sciences major with the following additional courses - (87 units total)
Required:
| Units | ||
|---|---|---|
| Additional Courses for the Statistics Track: | 9 | |
| Introduction to Stochastic Processes I | ||
| Advanced CS, such as: | 3 | |
| Mining Massive Data Sets | ||
| Advanced MS&E, such as: | 3 | |
| Probabilistic Analysis | ||
| or | ||
| Simulation | ||
| Allowable Elective Course Substitutions: | 9 | |
| Select three of the following: | ||
| Data Mining and Analysis | ||
| Applied Multivariate Analysis | ||
| Introduction to Time Series Analysis | ||
| Bootstrap, Cross-Validation, and Sample Re-use | ||
| Introduction to Statistical Learning | ||
| Stochastic Processes | ||
| A Course in Bayesian Statistics | ||
Honors Program
The honors program is designed to encourage a more intensive study of mathematical sciences than the B.S. program. Students interested in honors should consult with their faculty advisor as soon as possible to allow more opportunities in course planning and concentration area. The honors program allows for a capstone experience, building upon the student’s current academic knowledge and strengthening their understanding in a specific field of study/concentration. Honors work may be concentrated in fields such as biological sciences and medicine, environment, physics, sports analytics, investment science, AI/machine learning, etc.
Students are required to submit an MCS Honors Proposal Form describing the concentration for honors work, including the courses they intend to use, by the final study list deadline two quarters prior to the expected degree conferral quarter. The honors final report is due no later than the last day of classes of the quarter the student expects to graduate. More information can be found on the MCS Honors Website.
In addition to meeting all requirements for the B.S., the student must:
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Maintain a GPA of at least 3.5 in all major coursework.
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Students should complete 15 units of graduate level coursework. Included in these 15 units can be any of the following:
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Related research from a 199 course
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Participation for credit in a small group seminar
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Directed reading
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Complete a final report which should:
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Include their name, degree and the title of their work.
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Be typed with 12pt font, single-spaced, minimum 1 page (no longer than 2 pages) with a one-inch margin at the top and bottom of each page.
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Explain a theme between the student’s coursework, their interests, and how they relate to MCS.
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Describe how each course selected added to the student's knowledge and understanding in the chosen area of concentration.
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The student's work must demonstrate in-depth learning of a topic or shared idea in the breadth of the MCS major (examples are on MCS webpage), and all students are held to Stanford’s Honor Code.
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| Units | ||
|---|---|---|
| Suggested electives for students pursuing honors: | ||
| CME 206 | Introduction to Numerical Methods for Engineering | 3 |
| CS/STATS 229 | Machine Learning | 3-4 |
| CS 248 | Interactive Computer Graphics | 3-4 |
| EE 364A | Convex Optimization I | 3 |
| MATH 171 | Fundamental Concepts of Analysis | 3 |
| MATH 172 | Lebesgue Integration and Fourier Analysis | 3 |
| MATH 205A | Real Analysis | 3 |
| STATS 202 | Data Mining and Analysis | 3 |
| STATS 216 | Introduction to Statistical Learning | 3 |
| STATS 217 | Introduction to Stochastic Processes I | 3 |
Minor in Mathematical and Computational Science
The minor in Mathematical and Computational Science is intended to provide an experience of the four constituent areas: Mathematics, Computer Science, Management Science and Engineering, and Statistics. The minor consists of nine courses for a minimum of 32 units. A grade point average (GPA) of 2.75 is required for courses fulfilling the minor. All courses for the minor must be taken for a letter grade, if offered.
Degree Requirements
| Units | ||
|---|---|---|
| Mathematics (MATH) | 3-5 | |
| Select one of the following: | ||
| Linear Algebra, Multivariable Calculus, and Modern Applications | ||
| Applied Matrix Theory | ||
| Computer Science (CS) | 10 | |
| Select two of the followning: | ||
| CS 106A | Programming Methodology | 5 |
| and either | ||
| CS 106B | Programming Abstractions | 5 |
| or CS 106X | Programming Abstractions | |
| Management Science and Engineering (MS&E) | 3-4 | |
| Select one of the following: | ||
| Introduction to Optimization | ||
| Stochastic Modeling | ||
| Statistics (STATS) | 7 | |
| Select two of the following: | ||
| STATS 116 | Theory of Probability | 4 |
| and either | ||
| STATS 191 | Introduction to Applied Statistics | 3-4 |
| or STATS 200 | Introduction to Statistical Inference | |
| Electives | 9 | |
| The minor requires three courses, two of which must be in different departments. | ||
| Select three of the following: | ||
| Introduction to Scientific Computing | ||
| Mathematical Foundations of Computing | ||
| Computer Organization and Systems | ||
| Introduction to the Theory of Computation | ||
| Design and Analysis of Algorithms | ||
| Game Theory and Economic Applications | ||
| The Fourier Transform and Its Applications | ||
| Introduction to Optimization | ||
| Mathematical Programming and Combinatorial Optimization | ||
| Stochastic Modeling | ||
| Introduction to Stochastic Control with Applications | ||
| Applied Matrix Theory | ||
| Functions of a Complex Variable | ||
| Introduction to Combinatorics and Its Applications | ||
| Applied Group Theory | ||
| Applied Number Theory and Field Theory | ||
| Functions of a Real Variable | ||
| Partial Differential Equations | ||
| Fundamental Concepts of Analysis | ||
| Metalogic | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Introduction to Stochastic Processes I | ||
| Other upper-division courses appropriate to the program major may be substituted with consent of MCS program director. Undergraduate majors in the constituent programs may not count courses in their own departments. | ||
| Total Units | 32-34 | |
COVID-19 Policies
On July 30, the Academic Senate adopted grading policies effective for all undergraduate and graduate programs, excepting the professional Graduate School of Business, School of Law, and the School of Medicine M.D. Program. For a complete list of those and other academic policies relating to the pandemic, see the "COVID-19 and Academic Continuity" section of this bulletin.
The Senate decided that all undergraduate and graduate courses offered for a letter grade must also offer students the option of taking the course for a “credit” or “no credit” grade and recommended that deans, departments, and programs consider adopting local policies to count courses taken for a “credit” or “satisfactory” grade toward the fulfillment of degree-program requirements and/or alter program requirements as appropriate.
Undergraduate Degree Requirements
Grading
The MCS program counts all courses taken in academic year 2020-21 with a grade of 'CR' (credit) or 'S' (satisfactory) towards satisfaction of undergraduate degree requirements and minor that otherwise require a letter grade.
Faculty
Director: Professor Guenther Walther
Associate Director: Professor Chiara Sabatti
Faculty Advisers: Assistant Professor John Duchi, Professor Bradley Efron, Associate Professor David Rogosa, Assistant Professor Johan Ugander, Assistant Professor Scott Linderman
Steering Committee: Takeshi Amemiya (Economics, emeritus), Emmanuel Candès (Mathematics, Statistics), Brian Conrad (Mathematics), Richard Cottle (Management Science and Engineering, emeritus), John Duchi (Electrical Engineering & Statistics), Darrel Duffie (Economics & GSB), Bradley Efron (Statistics), Peter Glynn (Management Science and Engineering), Ramesh Johari (Management Science and Engineering), Percy Liang (Computer Science & Statistics), Parviz Moin (Mechanical Engineering), George Papanicolaou (Mathematics), David Rogosa (Education & Statistics), Chiara Sabatti (Biomedical Data Science & Statistics), David Siegmund (Statistics), Jonathan Taylor (Statistics), Brian White (Mathematics)
Courses
MCS 198. Practical Training. 1 Unit.
For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience. Students may enroll in Summer Quarters only and for a total of three times. Students must first notify their MCS adviser before enrolling in their course section, and must submit a one-page written final report summarizing the knowledge/experience gained upon completion of the internship in order to receive credit.
