For Current Students

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For Current Students

Biomedical Data Science Program Information

Nearly all of the DBDS program policies and procedures are described in the Student Handbook.

Student Handbook

Biomedical Data Science Student Handbook

The DBDS Student Handbook is a reference for DBDS program policies.

MS Word download: Student Handbook 2024

Curriculum

Curriculum

Note: the DBDS Curriculum has changed. Students starting after July 1, 2018 should use the new curriculum, which is described on the ExploreDegrees pages. Others should follow what is on this page.

Take a look at our current courses. 

Curriculum (admitted before July 1, 2018)

Computer Science, Statistics, Mathematics, and Engineering Electives

  • STATS 315A is recommended, not required.

Curriculum (admitted before Aug 1, 2016)

DBDS Core Courses (12 units)

  • Required: BIOMEDIN 212 and the three courses from the list below.
  • The core course websites (may need SUNet ID to view) are:
    • BIOMEDIN 202 Biomedical Data Science
    • BIOMEDIN 212 Introduction to Biomedical Data Science Research Methodology
    • BIOMEDIN 214 Representations and Algorithms for Computational Molecular Biology
    • BIOMEDIN 215 Data Driven Medicine
  • For PhD students who have received waivers (see below) for any of the core courses, then you should replace them with an equivalent number of units from another BIOMEDIN course, or CS/Stats/Math/Eng elective. You may, with permission, also replace waivered courses with DBDS 299 units.
  • HCP students: Note that BIOMEDIN 212 is a project class that has to-date been only available on campus, so you are currently exempt (replace with other course from DBDS, or from the CS/Stats/Math/Eng electives, as below). We have contemplated offering a remote version of 212, so please contact us if you are interested.
  • Core courses should be taken for grade, not pass/fail (all students).

Computer Science, Statistics, Mathematics, and Engineering Electives (24 units)

  • See our electives page for the full list.
  • If the course does appear on the list, then it is in principle acceptable, but realize that your entire elective list must be approved. This is to ensure some degree of coherence and to avoid excessive overlap of course material. It is therefore possible to submit an elective list, all of whose courses are listed, but which considered together would not be approved.
  • Note that CS 107 and CS 108 can count towards the CS/Stats/Math/Eng category. CS 106A, B cannot count for this category, but can be counted as Unrestricted Electives.
  • BIOMEDIN 224 and BIOMEDIN 258 are basically courses in biology and medicine, so don’t count as this category. All other BIOMEDIN courses can count in this category, if needed.
  • Up to 9 units can be 100-199 level; the rest must be 200 and above.
  • Up to 8 units can be taken pass/fail.

Social and Ethical Issues Electives (4 units)

  • To find all the approved courses in this category, type “dbds::ethics” into the search box in explorecourses, or click here.
  • Note that MED 255 is required for all MS and PhD students engaged in NIH-funded research at Stanford. It is not required but strongly recommended for coterm MS students not doing research. There is no distance education version of this class for HCP students.
  • HCP students: currently, only two classes satisfy this requirement and are offered through SCPD. These are: MS&E 256: Technology Assessment and Regulation of Medical Devices (Spring) and ME 208: Patent Law and Strategy for Innovators and Entrepreneurs, (Autumn). For HCP students who hold an MD degree or equivalent: this set of electives is waived on the basis of your medical school training, so replace with 4 more unrestricted units.

Unrestricted Electives (6 units)

  • Any graduate level class (200 and above) can be used for this category. Classes 100 and above can also count, subject to the restrictions listed below.

Biology/Medicine Electives (for PhD students only) (9 units)

  • In order to reach a total of 52 units of core curriculum, PhD students should take an additional 9 units; this should include 6 units of biology or medicine classes relevant to their research interests, 2 units of BIOMEDIN 290 Biomedical Data Science Teaching Methods and one additional unit of unrestricted elective.
  • All 6 units can be taken as Credit/No Credit.

More rules

  • All courses taken towards a graduate degree must be 100 level or above. At least 50% must be 200 level or above.
  • BIOMEDIN 201 can be taken up to three times for credit.
  • MS: total of 45 units required at Stanford.
  • PhD: total of 135 units, of which at least 27 units should come from DBDS, CS/Stats/Math/Engr electives, Biology/Medicine electives, or courses satisfying the Ethics requirement (including MED 255). They may not include research, teaching, journal club, or other classes can that only be taken pass/fail such as some seminars.
  • Coterms: see our Coterm page.
  • HCP MS students: see the SCPD Student Handbook.
  • Many more details on ExploreDegrees.

The core curriculum generally entails a minimum of 45 units of course work for MS students and 54 units of course work for PhD students, but can require substantially more or less depending upon the courses chosen and the previous training of the student. BIOMEDIN 299, 801, and 802 may be taken for satisfactory/no credit (S/NC).

Waivers: The varying backgrounds of students are well recognized and no one is required to take courses in an area in which he or she has already been adequately trained; under such circumstances, students are permitted to skip courses or substitute more advanced work using a formal annual process administered by the DBDS executive committee, in which students demonstrate satisfaction of core curriculum prerequisites, and request permission to receive core curriculum credit for classes taken previously in areas of the core curriculum. Students design appropriate programs for their interests with the assistance and approval of their Biomedical Data Science academic adviser.

Also, see the curriculum requirements for the MS and PhD degrees listed on the DBDS page in exploredegrees.

Advice on choosing courses and useful links

CS/Stats/Math/Eng Electives

Electives

There are two categories of electives in our curriculum:

  1. Computer science, mathematics, statistics, and engineering electives. This page is a list of courses which can used for this category.
  2. Unrestricted electives. These can be any graduate-level courses at Stanford at or above the 100 level (subject to degree-specific limits).

Note that a course not on this list is not an allowable elective. If you want to add a course to this list, send an email to DBDS Student Services (dbds-studentservices@stanford.edu) for consideration. Your particular course plan still needs to be approved by your academic advisor. Not all subsets of the following list are acceptable, for example, in the case of significant overlap between courses. Also, to find a course on this list, you may need to look under its cross-listed course ID.

APPPHYS 215: Numerical Methods for Physicists and Engineers
APPPHYS 223: Stochastic and Nonlinear Dynamics (BIO 223)
APPPHYS 223B: Nonlinear Dynamics: This Side of Chaos
APPPHYS 293: Theoretical Neuroscience
APPPHYS 315: Methods in Computational Biology
BIOC 223: Open Problems in Biology
BIODS 205: Bioinformatics for Stem Cell and Cancer Biology
BIODS 215: Topics in Biomedical Data Science: Large-scale inference
BIODS220: Artificial Intelligence in Healthcare (CS271, BIOMEDIN220)
BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236)
BIODS 239: Introduction to Analysis of RNA Sequence Data (BIOC 239)
BIODS 253: Software Engineering for Scientists
BIODS 260A,B,C: Workshop in Biostatistics (STATS 260A,B,C)
BIODS 271 (CS 277): Foundation Models for HealthcareBIOE 210: Systems Biology (BIOE 101)
BIOE 285: Computational Modeling in the Cardiovascular System (CME 285, ME 285)
BIOE 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, CME 279, CS 279)
BIOE 291: Principles and Practices of Optogenetics
BIOE 300B: Engineering Concepts Applied to Physiology
BIOE301E: Computational Protein Modeling Laboratory
BIOE331: Protein Engineering
BIOE 332: Large-Scale Neural Modeling
BIOE 334: Engineering Principles in Molecular Biology
BIOMEDIN 210: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270)
BIOMEDIN 217: Translational Bioinformatics (CS 275)
BIOMEDIN 219: Mathematical Models and Medical Decisions
BIOMEDIN 220: Artificial Intelligence in Healthcare (BIODS 220, CS 271)
BIOMEDIN 221: Machine Learning Approaches for Data Fusion in Biomedicine
BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)
BIOMEDIN 224 does NOT count towards this category
BIOMEDIN 226: Digital Health Practicum in a Health Care Delivery System
BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (EPI 261, STATS 261)
BIOMEDIN 245: Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345)
BIOMEDIN 248: Clinical Trial Design in the Age of Precision Medicine and Health (BIODS 248, BIODS 248P, STATS 248)
BIOMEDIN 248B: Causal Inference in Clinical Trials and Observational Study (II)
BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)
BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (BMP 260, CS 235, RAD 260)
BIOMEDIN 262: Computational Genomics (CS 262)
BIOMEDIN 273A: The Human Genome Source Code (CS 273A, DBIO 273A)
BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
BIODS 276: Advanced Topics in Computer Vision and Biomedicine (CS 286)
BIOMEDIN 279: Computational Biology: Structure and Organization of Biomolecules and Cells (CS 279, BIOPHYS 279, BIOE 279, CME 279)
BIOMEDIN 371: Computational Biology in Four Dimensions (CS 371, BIOPHYS 371, CME 371)
BIOMEDIN 374: Algorithms in Biology (CS 374)
BIOMEDIN 472: Data Sciencee and AI for COVID-19 (BIODS 472, CS 472)
BIOPHYS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, CME 279, CS 279)
BIOPHYS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, CME 371, CS 371)
BIOS 221: Modern Statistics for Modern Biology (STATS 366)
BIOS 234: Personalized Genomic Medicine
CBIO 243: Principles of Cancer Systems Biology
CHEM 263: Machine Learning for Chemical and Dynamical Data
CME 100: Vector Calculus for Engineers (ENGR 154)
CME 102: Ordinary Differential Equations for Engineers (ENGR 155A)
CME 103: Introduction to Matrix Methods (EE 103)
CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B)
CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)
CME 108: Introdution to Scientific Computing (MATH 114)
CME 200: Linear Algebra with Application to Engineering Computations (ME 300A)
CME 204: Partial Differential Equations in Engineering (ME 300B)
CME 211: Software Development for Scientists and Engineers
CME 213: Introduction to Parallel Computing using MPI, openMP, and CUDA
CME 214: Software Design in Modern Fortran for Scientists and Engineers (EARTHSCI 214)
CME 250: Introduction to Machine Learning
CME 250A: Machine Learning on Big Data
CME 251: Geometric and Topological Data Analysis (CS 233)
CME 285: Computational Modeling in the Cardiovascular System (BIOE 285, ME 285)
CME 263: Introduction to Linear Dynamical Systems (EE 263)
CME 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, BIOPHYS 279, CS 279)
CME 292: Advanced MATLAB for Scientific Computing
CME 302: Numerical Linear Algebra
CME 303: Partial Differential Equations of Applied Mathematics (MATH 220)
CME 309: Randomized Algorithms and Probabilistic Analysis (CS 265)
CME 323: Distributed Algorithms and Optimization
CME 330: Applied Mathematics in the Chemical and Biological Sciences (CHEMENG 300)
CME 334: Advanced Methods in Numerical Optimization (MS&E 312)
CME 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CS 371)
CME 500: Numerical Analysis and Computational and Mathematical Engineering Seminar
CME 510: Linear Algebra and Optimization Seminar
CS 103: Mathematical Foundations of Computing
CS 106A and 106B do NOT count towards this category
CS 107: Computer Organization and Systems
CS 108: Object-Oriented Systems Design
CS 109: Introduction to Probability for Computer Scientists
CS 111: Operating Systems Principles
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
CS 129: Applied Machine Learning
CS 131: Computer Vision: Foundations and Applications
CS 142: Web Applications
CS 143: Compilers
CS 144: Introduction to Computer Networking
CS 145: Introduction to Databases
CS 147: Introduction to Human-Computer Interaction Design
CS 148: Introduction to Computer Graphics and Imaging
CS 149: Parallel Computing
CS 154: Introduction to Automata and Complexity Theory
CS 155: Computer and Network Security
CS 157: Logic and Automated Reasoning
CS 161: Design and Analysis of Algorithms
CS 164: Computing with Physical Objects: Algorithms for Shape and Motion
CS 166: Data Structures
CS 167: Readings in Algorithms
CS 193C: Client-Side Internet Technologies
CS 193P: iPhone and iPad Application Programming
CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning
CS 221: Artificial Intelligence: Principles and Techniques
CS 223A: Introduction to Robotics (ME 320)
CS 224D: Deep Learning for Natural Language Processing
CS 224M: Multi-Agent Systems
CS 224N: Natural Language Processing (LINGUIST 284)
CS 224S: Spoken Language Processing (LINGUIST 285)
CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)
CS 224W: Social and Information Network Analysis
CS 225A: Experimental Robotics
CS 226: Statistical Techniques in Robotics
CS 227B: General Game Playing
CS 228: Probabilistic Graphical Models: Principles and Techniques
CS 229: Machine Learning
CS 229A: Applied Machine Learning
CS 230: Deep Learning
CS 231A: Introduction to Computer Vision
CS 231B: The Cutting Edge of Computer Vision
CS 231N: Convolutional Neural Networks for Visual Recognition
CS 236: Deep Generative Models
CS 238: Decision Making under Uncertainty (AA 228)
CS 240: Advanced Topics in Operating Systems
CS 240E: Embedded Wireless Systems
CS 240H: Functional Systems in Haskell
CS 242: Programming Languages
CS 243: Program Analysis and Optimizations
CS 244: Advanced Topics in Networking
CS 244B: Distributed Systems
CS 244E: Networked Wireless Systems (EE 384E)
CS 246: Mining Massive Data Sets
CS 248: Interactive Computer Graphics
CS 248A: Computer Graphics: Rendering, Geometry, and Image ManipulationCS 249A: Object-Oriented Programming from a Modeling and Simulation Perspective
CS 249B: Large-scale Software Development
CS 254: Computational Complexity
CS 255: Introduction to Cryptography
CS 259: Security Analysis of Network Protocols
CS 261: Optimization and Algorithmic Paradigms
CS 262: Computational Genomics (BIOMEDIN 262)
CS 263: Algorithms for Modern Data Models (MS&E 317)
CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309)
CS 266: Parameterized Algorithms and Complexity
CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)
CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)
CS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A)
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
CS 275: Translational Bioinformatics (BIOMEDIN 217)
CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, BIOE 279, CME 279)
CS 295: Software Engineering
CS 309A: Cloud Computing
CS 316: Advanced Multi-Core Systems (EE 382E)
CS 319: Topics in Digital Systems
CS 328: Topics in Computer Vision
CS 329: Topics in Artificial Intelligence
CS 329A: Self Improving AI Agents
CS 329H – Machine Learning from Human Preferences
CS 331A: Advanced Reading in Computer Vision
CS 331B: 3D Representation and Recognition
CS 334A: Convex Optimization I (CME 364A, EE 364A)
CS 344: Topics in Computer Networks
CS 344E: Advanced Wireless Networks
CS 345: Advanced Topics in Database Systems
CS 347: Parallel and Distributed Data Management
CS 349C: Topics in Programming Systems: Readings in Distributed Systems
CS 361A: Advanced Algorithms
CS 361B: Advanced Algorithms
CS 362: Algorithmic Frontiers: Effective Algorithms for Large Data
CS 364A: Algorithmic Game Theory
CS 364B: Topics in Algorithmic Game Theory
CS 366: Graph Partitioning and Expanders
CS 367: Algebraic Graph Algorithms
CS 369N: Beyond Worst-Case Analysis
CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371)
CS 373: Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, GENE 245, STATS 345)
CS 374: Algorithms in Biology (BIOMEDIN 374)
CS 375: Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249)
CS 427: Hero’s Journey: AI and Game Theory in 3D Real-time Storytelling
CS 442: High Productivity and Performance with Domain-specific Languages in Scala
CS 447: Software Design Experiences
CS 448B: Data Visualization
CS 468: Topics in Geometric Algorithms: Differential Geometry for Computer Science
CS 522: Seminar in Artificial Intelligence in Healthcare
CS 545: Database and Information Management Seminar
CS 547: Human-Computer Interaction Seminar
CS 528: Machine Learning Systems Seminar
DBIO 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, CS 273A)EE 101A: Circuits I
EE 101B: Circuits II
EE 102A: Signal Processing and Linear Systems I
EE 102B: Signal Processing and Linear Systems II
EE 103: Introduction to Matrix Methods (CME 103)
EE 108A: Digital Systems I
EE 108B: Digital Systems II
EE 168: Introduction to Digital Image Processing
EE 169: Introduction to Bioimaging
EE 179: Analog and Digital Communication Systems
EE 248: Fundamentals of Noise Processes
EE 256: Numerical Electromagnetics
EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)
EE 261: The Fourier Transform and Its Applications
EE 263: Introduction to Linear Dynamical Systems (CME 263)
EE 264: Digital Signal Processing
EE 276:  Information Theory
EE 277: Reinforcement Learning: Behaviors and Applications (MS&E 237)
EE 278A: Probabilistic Systems Analysis (EE 178)
EE 278B: Introduction to Statistical Signal Processing
EE 282: Computer Systems Architecture
EE 284: Introduction to Computer Networks
EE 292M: Parallel Processors Beyond Multi-Core Processing
EE 361: Principles of Cooperation in Wireless Networks
EE 364A: Convex Optimization I (CME 364A, CS 334A)
EE 364B: Convex Optimization II (CME 364B)
EE 365: Stochastic Control
EE 368: Digital Image Processing (CS 232)
EE 369A: Medical Imaging Systems I
EE 369B: Medical Imaging Systems II
EE 369C: Medical Image Reconstruction
EE 376A: Information Theory (STATS 376A)
EE 376B: Network Information Theory (STATS 376B)
EE 376C: Universal Schemes in Information Theory
EE 378A: Statistical Signal Processing
EE 378B: Inference, Estimation, and Information Processing
EE 379: Digital Communication
EE 382C: Interconnection Networks
EE 382E: Advanced Multi-Core Systems (CS 316)
EE 384A: Internet Routing Protocols and Standards
EE 384M: Network Science
EE 384S: Performance Engineering of Computer Systems & Networks
EE 387: Algebraic Error Control Codes
EE 398A: Image and Video Compression
EE 464: Semidefinite Optimization and Algebraic Techniques
EPI 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis
EPI 224: Genetic Epidemiology (GENE 230)
EPI 225: Introduction to Epidemiologic and Clinical Research Methods
EPI 226: Intermediate Epidemiologic and Clinical Research Methods
EPI 239: Applications of Causal Inference Methods (EDUC 260 A, STATS 209B)
EPI 251: Design and Conduct of Clinical Trials
EPI 258: Introduction to Probability and Statistics for Clinical Research
EPI 259 : Introduction to Probability and Statistics for Epidemiology (HUMBIO 89X)
EPI  262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (STATS 262)
ENGR 154: Vector Calculus for Engineers (CME 100)
ENGR 155A: Ordinary Differential Equations for Engineers (CME 102)
ENGR 155B: Linear Algebra and Partial Differential Equations for Engineers (CME 104)
ENGR 155C: Introduction to Probability and Statistics for Engineers (CME 106)
ENGR 205: Introduction to Control Design Techniques
ENGR 206: Control System Design
ENGR 207A: Linear Control Systems I
ENGR 207B: Linear Control Systems II
ENGR 209A: Analysis and Control of Nonlinear Systems
GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B)
HRP 252: Outcomes Analysis (BIOMEDIN 251, MED 252)
HRP 255: Decoding Academia: Power, Hierarchies, and Transforming Institutions
HRP 263: Advanced Decision Science Methods and Modeling in Health
IMMUNOL 206A: Systems and Computational Immunology
IMMUNOL 206B: Directed Projects in Systems and Computational Immunology
IMMUNOL 207: Essential Methods in Computational and Systems Immunology
IMMUNOL 208: Advanced Computational and Systems Immunology
MATH 104: Applied Matrix Theory
MATH 106: Functions of a Complex Variable
MATH 107: Graph Theory
MATH 108: Introduction to Combinatorics and Its Applications
MATH 109: Applied Group Theory
MATH 110: Applied Number Theory and Field Theory
MATH 111: Computational Commutative Algebra
MATH 113: Linear Algebra and Matrix Theory
MATH 115: Functions of a Real Variable
MATH 116: Complex Analysis
MATH 118: Mathematics of Computation
MATH 120: Groups and Rings
MATH 121: Galois Theory
MATH 122: Modules and Group Representations
MATH 131P: Partial Differential Equations I
MATH 132: Partial Differential Equations II
MATH 136: Stochastic Processes (STATS 219)
MATH 143: Differential Geometry
MATH 144: Riemannian Geometry
MATH 145: Algebraic Geometry
MATH 147: Differential Topology
MATH 148: Algebraic Topology
MATH 149: Applied Algebraic Topology
MATH 151: Introduction to Probability Theory
MATH 152: Elementary Theory of Numbers
MATH 154: Algebraic Number Theory
MATH 155: Analytic Number Theory
MATH 159: Discrete Probabilistic Methods
MATH 161: Set Theory
MATH 171: Fundamental Concepts of Analysis
MATH 172: Lebesgue Integration and Fourier Analysis
MATH 173: Theory of Partial Differential Equations
MATH 175: Elementary Functional Analysis
MATH 193: Polya Problem Solving Seminar
MATH 205A: Real Analysis
MATH 205B: Real Analysis
MATH 210A: Modern Algebra I
MATH 210B: Modern Algebra II
MATH 210C: Lie Theory
MATH 215A: Complex Analysis, Geometry, and Topology
MATH 215B: Complex Analysis, Geometry, and Topology
MATH 215C: Complex Analysis, Geometry, and Topology
MATH 216A: Introduction to Algebraic Geometry
MATH 216B: Introduction to Algebraic Geometry
MATH 216C: Introduction to Algebraic Geometry
MATH 217A: Differential Geometry
MATH 220: Partial Differential Equations of Applied Mathematics (CME 303)
MATH 221A: Mathematical Methods of Imaging (CME 321A)
MATH 221B: Mathematical Methods of Imaging (CME 321B)
MATH 222: Computational Methods for Fronts, Interfaces, and Waves
MATH 224: Topics in Mathematical Biology
MATH 226: Numerical Solution of Partial Differential Equations (CME 306)
MATH 227: Partial Differential Equations and Diffusion Processes
MATH 228: Stochastic Methods in Engineering (CME 308)
MATH 230A: Theory of Probability (STATS 310A)
MATH 230B: Theory of Probability (STATS 310B)
MATH 230C: Theory of Probability (STATS 310C)
MATH 232: Topics in Probability: Percolation Theory
MATH 233: Probabilistic Methods in Analysis
MATH 236: Introduction to Stochastic Differential Equations
MATH 239: Computation and Simulation in Finance
MATH 245A: Topics in Algebraic Geometry: Moduli Theory
MATH 245B: Topics in Algebraic Geometry: Intersection Theory
MATH 245C: Topics in Algebraic Geometry: Alterations
MATH 247: Topics in Group Theory
MATH 248: Ergodic Theory and Szemeredi’s Theorem
MATH 248A: Algebraic Number Theory
MATH 249A: Topics in number theory
MATH 249B: Topics in Number Theory
MATH 249C: Topics in Number Theory
MATH 252: Algebraic Groups
MATH 254: Geometric Methods in the Theory of Ordinary Differential Equations
MATH 256A: Partial Differential Equations
MATH 256B: Partial Differential Equations
MATH 257A: Symplectic Geometry and Topology
MATH 258: Topics in Geometric Analysis
MATH 259: mirror symmetry
MATH 261A: Functional Analysis
MATH 263A: Lie Groups and Lie Algebras
MATH 264: Infinite Dimensional Lie Algebra
MATH 266: Computational Signal Processing and Wavelets
MATH 269: Topics in symplectic geometry
MATH 270: Geometry and Topology of Complex Manifolds
MATH 271: The H-Principle
MATH 272: Topics in Partial Differential Equations
MATH 280: Evolution Equations in Differential Geometry
MATH 282A: Low Dimensional Topology
MATH 282B: Homotopy Theory
MATH 284: Topics in Geometric Topology
MATH 286: Topics in Differential Geometry
MATH 287: Introduction to optimal transportation
MATH 295: Computation and Algorithms in Mathematics
MATH 301: Advanced Topics in Convex Optimization
MATH 310: Algorithms
MATH 384: Seminar in Geometry
MATH 385: Seminar in Topology
MATH 388: Seminar in Probability and Stochastic Processes
MATH 389: Seminar in Mathematical Biology
MATH 394: Classics in Analysis
MATH 395: Classics in Geometry and Topology
MGTECON 634: Machine Learning and Causal Inference
ME 285: Computational Modeling in the Cardiovascular System (BIOE 285, CME 285)
ME 261: Dynamic Systems, Vibrations and Control (ME 161)
ME 300B: Partial Differential Equations in Engineering (CME 204)
MED 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, STATS 211)
MED 252: Outcomes Analysis (BIOMEDIN 251, HRP 252)
MS&E 120: Probabilistic Analysis
MS&E 211: Linear and Nonlinear Optimization
MS&E 220: Probabilistic Analysis
MS&E 223: Simulation
MS&E 226: “Small” Data
MS&E 228: Applied Causal Inference with Machine Learning and AI
MS&E 252: Decision Analysis I: Foundations of Decision Analysis
MS&E 263: Healthcare Operations Management
MS&E 310: Linear Programming
MS&E 312: Advanced Methods in Numerical Optimization (CME 334)
MS&E 328: Foundations of Causal Machine Learning
MS&E 335: Queueing and Scheduling in Processing Networks
MS&E 355: Influence Diagrams and Probabilistics Networks
MS&E 454: Decision Analysis Seminar
MS&E 463: Healthcare Systems Design
NBIO 228: Mathematical Tools for Neuroscience
NENS 230: Analysis Techniques for the Biosciences Using MATLAB
PSYCH 248: Advanced fMRI modeling and analysis
PSYCH 248A: fMRI Analysis Bootcamp
PSYCH 253: Advanced Statistical Modeling
STATS 110: Statistical Methods in Engineering and the Physical Sciences
STATS 166: Computational Algorithms for Statistical Genetics (GENE 245, STATS 345)
STATS 191: Introduction to Applied Statistics
STATS 200: Introduction to Statistical Inference
STATS 202: Data Mining and Analysis
STATS 203: Introduction to Regression Models and Analysis of Variance
STATS 205: Introduction to Nonparametric Statistics
STATS 206: Applied Multivariate Analysis
STATS 207: Introduction to Time Series Analysis
STATS 208: Introduction to the Bootstrap
STATS 209: STATS 209: Introduction to Causal Inference
STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, MED 206)
STATS 214: Machine Learning Theory
STATS 215: Statistical Models in Biology
STATS 216: Introduction to Statistical Learning
STATS 216V: Introduction to Statistical Learning
STATS 217: Introduction to Stochastic Processes
STATS 218: Introduction to Stochastic Processes
STATS 219: Stochastic Processes (MATH 136)
STATS 253: Spatial Statistics (STATS 352)
STATS 260A: Workshop in Biostatistics (BIODS 260A)
STATS 260B: Workshop in Biostatistics (BIODS 260B)
STATS 260C: Workshop in Biostatistics (BIODS 260C)
STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261)
STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)
STATS 270: A Course in Bayesian Statistics (STATS 370)
STATS 285: Massive Computational Experiments, Painlessly
STATS 290: Paradigms for Computing with Data
STATS 300A: Theory of Statistics
STATS 300B: Theory of Statistics
STATS 300C: Theory of Statistics
STATS 305A: Applied Statistics I
STATS 305B: Applied Statistics II
STATS 305C: Applied Statistics III
STATS 306A: Methods for Applied Statistics
STATS 306B: Methods for Applied Statistics: Unsupervised Learning
STATS 310A: Theory of Probability (MATH 230A)
STATS 310B: Theory of Probability (MATH 230B)
STATS 310C: Theory of Probability (MATH 230C)
STATS 314: Advanced Statistical Methods
STATS 315A: Modern Applied Statistics: Learning
STATS 317: Stochastic Processes
STATS 318: Modern Markov Chains
STATS 319: Literature of Statistics
STATS 320: Heterogeneous Data with Kernels
STATS 321: Modern Applied Statistics: Transposable Data
STATS 322: Function Estimation in White Noise
STATS 325: Multivariate Analysis and Random Matrices in Statistics
STATS 330: An Introduction to Compressed Sensing (CME 362)
STATS 351A: An Introduction to Random Matrix Theory (MATH 231A)
STATS 352: Spatial Statistics (STATS 253)
STATS 355: Observational Studies (HRP 255)
STATS 362: Monte Carlo
STATS 366: Modern Statistics for Modern Biology (BIOS 221)
STATS 370: A Course in Bayesian Statistics (STATS 270)
STATS 374: Large Deviations Theory (MATH 234)
STATS 375: Inference in Graphical Models
STATS 376A: Information Theory (EE 376A)
STATS 396: Research Workshop in Computational Biology

Funding

Funding Sources for DBDS PhD Students

All DBDS PhD students are admitted with a funding plan in place. However, we require that enrolled students apply to internal and external funding for which they are eligible. This is typically done in one of the first three years of graduate study. Below are listed the funding sources that would be available to most students. You are encouraged to seek out others as well. Note that for some of these sources having an MS will make you ineligible, or the MS time will count as years of graduate study.

Coterminal MS advice

Checklist for coterminal students

The rules are a bit complicated. Here’s a summary:

  • See the Coterminal Masters Degree page, VPGE Coterminal Degrees page, Stanford University Procedures for Coterminal Students, and DBDS curriculum description in exploredegrees.
  • When asking questions about your particular circumstances, please make sure that you have listed all the courses towards your MS on a flowsheet, and make that available to us, as nearly all questions involve some aspect of that.
  • You are required to have at least 180 units (undergraduate) and 45 units (graduate).
  • No units may be counted towards both undergraduate and DBDS MS degree.
  • 45 units are required for the DBDS MS.
  • All courses counted towards the MS must be DBDS-related courses at or above the 100 level (as approved by advisor and DBDS program).
  • At least 23 units must be courses at or above the 200 level.
  • At least 27 units must be taken for a grade (unless, through exceptional circumstances such as the 2020 crisis, this is imposible, in that case, consult DBDS Student Services).
  • DBDS core courses may not be taken pass/fail. Exceptions: BIOMEDIN 200 (no longer offered), 201, 205, 206, 207, 290, 299, 390, 801, 802, and MED 255, or DBDS courses that are offered only on a S/NC basis (including during the 2020 crisis).
  • A complete list of elective courses that can count for CS/Stats/Math/Eng is listed here. Up to eight units of CS/Stats/Math/Eng can be taken pass/fail.
  • You can apply to DBDS upon completion of 120 units, but no later than the quarter prior to the expected completion of the undergraduate degree.
  • Coterminal students are permitted to count coursework taken in the three quarters immediately prior to their first graduate quarter toward their graduate degree (Summer quarter is not included in the count).
  • The Coterminal Course Transfer eForm is used for transferring courses from the undergraduate career to the graduate career, and vice versa. This must be done before the undergraduate degree is conferred.
  • You may take any course you want at Stanford while in the program, including electives not at all related to DBDS, as long as you also have an approved DBDS-related 45-unit program of study that satisfies the requirements for the DBDS degree.
  • A common question is how to deal with courses when you have counted a class required for the MS towards your undergraduate degree, typically because you took the class more than three quarters before starting the MS. Generally, you list the course you took and mark it as UG, and take something else for the DBDS MS units.
    • For a “Core Biomedical Data Science” course, you can take another course in the same category, any course listed under BIOMEDIN or BIODS, or any course in the CS/Stats/Math/Eng electives list.
    • For a “CS/Math/Stats/Eng” course, you can take another course in the same category.
    • For a “Social/Ethics” course, you can take another course in the same category, or any other course appropriate for graduate degree credit.
    • Example: You took CS181W as an undergraduate. This class satisfies the DBDS MS Social and Ethical requirement; instead of taking graduate courses towards this distributional requirement, you now take an additional 4 units of DBDS-related electives. Enter CS181W on your flowsheet, and put “Y” in the “UG?” column. Note that it then contributes no units to the DBDS MS. Then list a course (or courses) for four units under DBDS-related electives.
    • Example: You took BIOMEDIN 217 as an undergraduate. This class is part of the DBDS core. List the course in the DBDS core part of the flowsheet, and enter “Y” in the “UG?” column. Then list another approved course (or courses) for four units under DBDS-related electives.
    • See the example spreadsheet.
    • In general, for choosing the courses that go under your (now expanded) set of units under DBDS-related electives, pick graduate courses with some thematic relationship to Biomedical Data Science. Feel free to discuss with the DBDS Program staff if you are unsure whether a course will be approved as DBDS-related. Courses such as BIOMEDIN 299 are fine.
  • Students must finish the master’s degree within three years of their first co-terminal quarter. Note that taking a leave of absence does NOT extend your time to degree. If you want to take more than three years, you will need to petition the registrar.

HCP MS advice

Checklist for HCP MS students

This document is some advice about how to navigate our curriculum and how to choose courses. There are differences between the HCP MS requirements and those listed for the other MS and PhD degrees; those differences are highlighted here.

  • Start by reviewing the curriculum at DBDS curriculum description in ExploreDegrees, the SCPD website, and the SCPD HCP Program Handbook. You should also review the DBDS Student Handbook.
  • Core courses: Although many classes necessary for the degree are available online, some requirements may be fulfilled through implementation of an alternative plan to be approved by the program. DBDS 212 requires special arrangements with the instructor, so we can include you in the class remotely if possible. Note that core courses should be taken for a grade, not pass/no credit (unless not offered for a grade).
  • If Stats 200 is not offered remotely, then in that case you can consider taking: MS&E 226 or EPI 259 and EPI 261 (both).
  • Computer science, statistics, mathematics, and engineering electives (18 units). Courses that can count for CS/Stats/Math/Eng is listed on the Electives page. Up to 6 units of CS/Stats/Math/Eng can be taken pass/fail. If you are following the new curriculum (admitted after Aug 1, 2016), note that CS 161 and STATS 200 are required. These two courses, or some of their prerequisites, may not be available through SCPD. You should submit a course plan with next best alternatives: these may include similar courses at Stanford, or courses at other institutions (note that outside courses would not count towards the required 45 units).
  • Social and Ethical issues (4 units): Type “dbds::ethics” into the search box in explorecourses. Note that although MED 255 is listed as required, it is required only for MS and PhD students engaged in NIH-funded research at Stanford. This typically does not apply to HCP students.
  • Unrestricted electives (14 units): They need to be at or above the 100 level.
  • How to put the courses in order. Use one of the course flowsheets. First, check ExploreCourses and the SCPD website for which quarter the courses are offered. Also, check the listed prerequisites for each class, and also the course websites for additional information about what’s expected so you can estimate how difficult each class will be for you. Typically, SCPD students take one course per quarter. Note that SCPD imposes an upper limit of three courses per quarter. Also, use the DBDS Student Wiki for advice on courses.
  • The following courses are only available P/F: BIOMEDIN 201, 205, 206, 207, 290, 299, 801, 802, and MED 255.
  • 45 units are required for the DBDS MS.
  • All courses counted towards the MS must be at or above the 100 level.
  • You can include up to 18 units that you took Non-Degree Option before entering the MS program.
  • At least 23 units must be courses at or above the 200 level.
  • At least 27 units must taken for a grade.
  • Requests to transfer from part-time (HCP) to full-time (Academic MS) are reviewed by the DBDS Exec on a case-by-case basis. Final decisions are at the DBDS Exec’s discretion. Please note the following limitations (for students enrolling in the HCP program starting Fall 2020):
  1. Students must complete a minimum of two (2) quarters in the part-time program excluding summer quarter or enrollment as a non-degree option student, before requesting to transfer to full-time. Therefore, the soonest the transfer can be discussed and approved is during the first DBDS Exec meeting of the third quarter of the student in the HCP program
  2. Students must complete a minimum of 10 units of letter-graded courses that meet requirements for the DBDS MS degree before commencing their first full-time (Academic MS) quarter
  3. GPA will be considered as part of the request.
  4. Students can make a maximum of two (2) transfers during the program (e.g. transfer from part-time to full-time and back to part-time).
  5. Students should consider the availability of courses online before requesting to switch from full-time to part-time, especially if this may interfere with their ability to satisfy the requirements of the degree.

Presentations

Advice on Talks and Presentations

Bagley, DBDS Journal Club Template Video

https://vimeo.com/51589652

Forms

Forms for DBDS Students

Course Flowsheets by Degree Program

Obtain signatures and submit all Milestone forms to the DBDS Student Services Officer, unless otherwise stated.

TGR (Terminal Graduate Residency)

  • TGR form – TGR allows students to register at a reduced tuition rate while working on a dissertation, thesis, or department project. Must have completed 135 total units to qualify. Submit to the Office of the University Registrar.

Graduation

  • Graduate Quarter Petition – To be submitted to the Office of the University Registrar prior to the quarter of intended graduation.
  • Commencement Walk Through Petition – To be submitted to DBDS Student Services Officer, if student has not completed degree requirements, but would like to participate in June commencement. Walkthrough candidates who submit their forms by May 1 will be in listed in the June Commencement Program.

Travel

Other Forms

DBDS Student Wiki

Biomedical Data Science Student Wiki

The DBDS Student Wiki