Curriculum

Cancer Biology graduate students must be registered in all quarters. Courses may be taken for Pass/Fail unless designated otherwise. All required courses MUST BE taken with a letter grade (except for CBIO 280, 299, and 399). If a student does not earn a grade of "B" or better in a required course, the course must be repeated. Students must complete the following courses (or their equivalents) by the end of their second year.

Required - Cancer Biology Graduate-Level

Course Number Course Name Units Quarter
BIOS 200 Foundations in Experimental Biology  5 A
CBIO 240 Molecular and Genetic Basis of Cancer   4 A
CBIO 242 Cellular and Clinical Aspects of Cancer 4 Sp
CBIO 245 Lecture Seminar Series in Cancer Biology Program (1st & 2nd year) 1 A, W, Sp
CBIO 280 Cancer Biology Journal Club (1st & 2nd year) 1 A, W, Sp
MED 255 Responsible Conduct in Research 1 A, W, Sp
CCRTP Comprehensive Cancer Research Training Program  0 A

Electives - (total of 10 units)

Computational/Systems Cancer Biology Track

Core Knowledge

Course Number Course Name Units Quarter
STATS 60 Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160) 5 All Qtrs
GENE 218 Computational Analysis of Biological Information: Introduction to Python for Biologists (MI 218, PATH 218) 2 Su

BIOS 205

Introduction to R

1

A, W, Sp

NENS 230 Analysis Techniques for the Biosciences Using MATLAB 2 A
CS 106A Programming Methodology (ENGR 70A) 3-5 All Qtrs
GENE 211 Genomics  3 W
CBIO 243 Principles of Cancer Systems Biology 3 Sp
BIOS 201 Next Generation Sequencing and Applications 2 W

Additional Courses

Course Number Course Name Units Quarter
CS 106B Programming Abstractions (ENGR 70B) 3-5 All Qtrs
STATS 116 Theory of Probability     3-5 A, Sp, Su
STATS 202 Human Genetics 3 A, Su
STATS 216 Introduction to Statistical Learning 3 WI
BIOMEDIN214  Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214) 3-4 A
IMMUNOL 207 Essential Methods in Computational and Systems Immunology 3 Sp
CS 161     Design and Analysis of Algorithms 3-5 A, W, Sp, Su
GENE 245     Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, STATS 345) 3 Sp
Self-paced online statistical learning http://online.stanford.edu/course/statistical-learning-self-paced none extended period of time. Content available at least Aug 2, 2017

Other Cancer Biology Related Graduate-Level

Course Number Course Name Units Quarter
BIO 214
Advanced Cell Biology 4 W
SBIO 241 Biological Macromolecules 3-5 Sp
CSB 210 Cell Signaling 4 W
BIOE 217 Translational Bioinformatics   4 Sp
IMMUNOL 201  Advanced Immunology I 3 W
DBIO 201 Development and Disease Mechanisms 2 A
MI 215  Principles of Biological Technologies 3 Sp
CBIO 275 Tumor Immunology (alternate years-next, offer 2017-18) 3 Sp

Additional graduate-level courses listed in Stanford University Explore Courses

Lab Rotations

A minimum of three one-quarter laboratory rotations will be required of all new students during the first year. Students must choose a thesis advisor prior to the end of summer quarter, first year, but not before the end of spring quarter. Two rotations must be done within the Cancer Biology Faculty participating labs. Students may rotate the third rotation with any faculty member outside the program.

You can find information about our participating faculty's research interests on the faculty directory page.

Qualifying Exam

Besides coursework, students are required to do a single qualifying exam based on the student’s thesis proposal in their second year. The goal of the qualifying Examination is to determine the student’s preparedness to pursue research on a thesis topic, explore whether potential problems have been considered, assess the student’s ability to think, and evaluate the student’s familiarity with relevant background information and alternative experimental approaches.

The exam consists of an F31 NRSA-style written grant proposal not to exceed 7 pages (excluding references) and an oral examination. The examining committee includes three faculty members from the Cancer Biology Program but does not include the student’s thesis advisor. The composition of this committee is chosen by the student and thesis advisor and must be submitted to and approved by the Program Directors prior to the end of autumn quarter, second year. One non-Cancer Biology faculty member may be substituted, if necessary, to provide specific scientific expertise relevant to the student’s proposal. The written and oral proposal should represent the student’s own efforts to identify a question of interest and to develop appropriate experimental approaches. Preliminary data generated by the student are NOT required. Students are strongly encouraged to develop a written Specific Aims section by the end of winter quarter, second year. The qualifying exam must be taken by April 1st, second year. If necessary, one retake will be permitted prior to the end of summer quarter, second year.

Annual Dissertation Committee Meetings

In year three each student is required to hold an annual dissertation committee meeting in order to assess progress towards the degree and for the committe to provide advice. 

In the fourth year and beyond, students are required to meet with their committees twice a year.

The Annual Dissertation Committee Meeting Form should be completed and signed by all of the committee members in attendance at each meeting and forms should  be returned to Cancer Biology Program Office.

Dissertation and Oral Examination

The major accomplishment of each successful Ph.D. student is the presentation of a written dissertation resulting from independent investigation that contributes to knowledge in the area of cancer biology.  A University Oral Examination is also required for the Ph.D. degree.  In the Cancer Biology Program, a public seminar (one hour) is presented by the Ph.D. candidate, followed by a closed-door oral examination.  The University Oral Examination Committee consists of at least four examiners and a chair from a different academic department than the student's advisor.  Note that the chair may be a faculty member in the Cancer Biology Program provided that he/she is not in the same academic department as the student's advisor.  All members of the Committee are normally members of the Academic Council, and the oral examination chair must be.  With the prior approval of the Program Director or School Dean, one of the examiners may be a person who is not a member of the Academic Council if that individual contributes expertise not readily available from the Stanford faculty. Official responsibility for selecting the oral examination chair rests with the Department.  Cancer Biology delegates this to the student and dissertation advisor.

Course Descriptions

BIO 214: Advanced Cell Biology (BIOC 224, MCP 221)
For Ph.D. students. Current research on cell structure, function, and dynamics. Topics include complex cell phenomena such as cell division, apoptosis, compartmentalization, transport and trafficking, motility and adhesion, and differentiation. Weekly reading of current papers from the primary literature. Preparation of an original research proposal. Prerequisite for advanced undergraduates: BIO 129A,B, and consent of instructor. Elective.
Terms: Aut | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Jonikas, M. (PI) ; Kopito, R. (PI) ; Pfeffer, S. (PI) ; Rohatgi, R. (PI) ; Theriot, J. (PI)

BIOE 217: Translational Bioinformatics (BIOMEDIN 217, CS 275)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics. Elective.
Terms: Win | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Gevaert, O. (PI) ; Mallick, P. (PI) ; Wall, D. (PI) ; Gloudemans, M

BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)
This Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units. Elective.
Terms: Aut | Units: 3-4 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Altman, R. (PI)

BIOS 200: Foundations in Experimental Biology
This course is divided into two 3-week cycles. During the first cycle, students will be developing a 2-page original research proposal, which may be used for NSF or other fellowship applications. In the second cycle, students will work in small teams and will be mentored by faculty to develop an original research project for oral presentation. Skills emphasized include: 1) reading for breadth and depth; 2) developing compelling, creative arguments; 3) communicating with the spoken and written word; 4) working in teams. Important features of the course include peer assessment, interactive joint classes, and substantial face-to-face discussion with faculty drawn from across the Biosciences programs. Shortened autumn quarter class; class meets during weeks 1 through 8 of the quarter. Required.
Terms: Aut | Units: 5 | Grading: Satisfactory/No Credit.
Instructors: Schneider, D. (PI) ; Sherlock, G. (PI)

BIOS 201: Next Generation Sequencing and Applications
Usher in the golden age of biological discovery with next generation sequencing (NGS) through its wide spectrum of applications. Modules include general introduction of Next Generation Sequencing (NGS) technologies, applications of these sequencing technologies, caveats and comparisons with previous approaches, analysis and interpretation of sequencing data, principles of tools and resources and practical ways to utilize them, and features and pitfalls. Prerequisite: background in molecular biology. Elective.
Terms: Win | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC.
Instructors: Greenleaf, W. (PI); Li, J. (PI); Montgomery, S. (PI); Urban, A. (PI) 

BIOS 205: Introduction to R
Autumn quarter enrollment limited to ADVANCE students; instructor consent required for enrollment. Topics include: basics of R (widely used, open-source programming and data analysis environment) programming language and data structures, reading/writing files, graphics tools for figure generation, basic statistical and regression operations, survey of relevant R library packages. Interactive format combining lectures and computer lab. Elective.
Terms: Aut, Win, Spr | Units: 1 | Grading: Medical Satisfactory/No Credit.
Instructors: Bagley, S. (PI)

CBIO 240: Molecular Genetic Basis of Cancer
Required for first-year Cancer Biology graduate students. Focus is on fundamental concepts in the molecular biology of cancer, including oncogenes, tumor suppressor genes, and cellular signaling pathways. Emphasis will be given to seminal discoveries and key experiments in the field of cancer molecular biology. Course consists of two 1 hour lectures and one 2 hour discussion per week. Enrollment of undergraduates requires consent of the course director. Lecture: Tue, Thu 8:30 AM - 10:20 AM. Discussion:  Fri 1:30 PM - 3:20 PM. Required.
Terms: Aut | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Giaccia, A. (PI) ; Graves, E. (PI)

CBIO 242: Cellular and Clinical Aspects of Cancer
Required for first-year Cancer Biology graduate students, and for first- and second-year medical students intending to complete the Cancer Biology Scholarly Concentration. Focus is on the cellular biology of cancer, including discussion of basic biology including tumor angiogenesis, metabolism, and immunology, as well as clinical oncology and cancer therapeutics. Emphasis will be given to seminal discoveries and key experiments in the field of cancer biology and oncology. Course consists of two 1 hour lectures and one 2 hour discussion per week. Enrollment of undergraduates requires consent of the course director.  Lecture: Tue, Thu 8:30 AM - 10:20 AM. Discussion:  Fri 1:30 PM - 3:20 PM. Required.
Terms: Spr | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Giaccia, A. (PI) ; Graves, E. (PI) ; Koong, A. (PI)  

CBIO 243: Principles of Cancer Systems Biology
Focus is on major principles of cancer systems biology research that integrates experimental and computational biology in order to systematically unravel the complexity of cancer. The opportunity to embark on cancer systems biology research has been enabled by the rapid emergence of numerous and increasingly accessible technologies that provide global DNA, RNA and protein expression profiles of cells under a variety of conditions following environmental, drug and genetic perturbations. Course addresses the challenge of how to analyze high-dimensional and highly-multiplexed data in order to synthesize biologically and clinically relevant insights and generate hypotheses for further functional testing. Aims to broaden student exposure to the experimental and computational skills needed to apply the emerging principles of systems biology to the study of cancer. Elective.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Plevritis, S. (PI)

CBIO 245: Lecture Seminar Series in Cancer Biology Program
Required for first-year and second-year Cancer Biology graduate students. Invited speakers share insights about state-of-the-art trends. Presents new concepts in the field of cancer biology. Science talks presented by students. Required.
Terms: Aut, Win, Spr | Units: 1 | Grading: Medical Satisfactory/No Credit.

CBIO 275: Tumor Immunology (IMMUNOL 275)
Tumor Immunology focuses on the mechanisms by which tumors can escape from and subvert the immune system and conversely on the ability of innate and adaptive arms of the immune system to recognize and eliminate tumors. Topics include: tumor antigens, tumor immunosurveillance and immunoediting, tumor immunotherapy (including CAR-T and checkpoint antibodies) and cancer vaccines. Tracks the historical development of our understanding of modulating tumor immune response and discusses their relative significance in the light of current reserach findings. Prerequisite: for undergraduates, human biology or biology core.  Elective.
Terms: alternate years, given next year (2017) | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)

CBIO 280: Cancer Biology Journal Club
Required of first- and second-year graduate students in Cancer Biology. Recent papers in the literature presented by graduate students. When possible, discussion relates to and precedes cancer-related seminars at Stanford. Attendance at the relevant seminar required. Required.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Medical Satisfactory/No Credit
Instructors: Morrison, A. (PI)

CS 106A: Programming Abstractions (ENGR 70A)
Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. No prior programming experience required. Summer quarter enrollment is limited. Elective.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR | Grading: Letter or Credit/No Credit
Instructors: Cain, J. (PI) ; Piech, C. (PI) ; Roberts, E. (PI) ;  Sahami, M. (PI) ; Stepp, M. (PI) ; Troccoli, N. (PI) 

CS 106B: Programming Abstractions (ENGR 70B)
Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent. Summer quarter enrollment is limited.. Elective.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | Grading: Letter or Credit/No Credit. 
Instructors: Gregg, C. (PI) ; Piech, C. (PI) ;  Schwarz, K. (PI); 

CS 161: Design and Analysis of Algorithms
Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116. Elective.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR | Grading: Letter or Credit/No Credit
Instructors: Charikar, M. (PI) ; Eng, D. (PI) ; Su, J. (PI) ; Valiant, G. (PI) ; Wootters, M. (PI)

CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 217)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.  Elective.
Terms: Win | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC). 
Instructors: Dumontier, M. (PI) ; Gevaert, O. (PI) ; Wall, D. (PI)

CSB 210: Cell Signaling
The molecular mechanisms through which cells receive and respond to external signals. Emphasis is on principles of cell signaling, the systems-level properties of signal transduction modules, and experimental strategies through which cell signaling pathways are being studied. Prerequisite: working knowledge of biochemistry and genetics. Elective.
Terms: Win | Units: 4 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Meyer, T. (PI) ; Teruel, M. (PI)

DBIO 201: Development and Disease Mechanisms
Mechanisms that direct human development from conception to birth. Conserved molecular and cellular pathways regulate tissue and organ development; errors in these pathways result in congenital anomalies and human diseases. Topics: molecules regulating development, cell induction, developmental gene regulation, cell migration, programmed cell death, pattern formation, stem cells, cell lineage, and development of major organ systems. Emphasis on links between development and clinically significant topics including infertility, assisted reproductive technologies, contraception, prenatal diagnosis, teratogenesis, inherited birth defects, fetal therapy, adolescence, cancer, and aging. Elective.
Terms: Aut | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Kim, S. (PI) ; Kingsley, D. (PI)

GENE 211: Genomics
The goal of this course is to explore how different experimental strategies are applied to a variety of biological questions. By experimental strategy, we refer to both the general method and the logic with which the method is applied. An underlying theme of the course is that each strategy we discuss can be applied to problems that cut across different disciplines, for example immunology, cancer biology, or embryology. Genome evolution, organization, and function; technical, computational, and experimental approaches; hands-on experience with representative computational tools used in genome science; and a work knowledge of the scripting language Python. Elective.
Terms: Win | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Cherry, J. (PI) ; Sherlock, G. (PI)

GENE 218: Computational Analysis of Biological Information: Introduction to Python for Biologists (MI 218, PATH 218)
Computational tools for processing, interpretation, communication, and archiving of biological information. Emphasis is on sequence and digital microscopy/image analysis. Intended for biological and clinical trainees without substantial programming experience.
Terms: Sum | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC). Elective

GENE 245: Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, STATS 345)
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC). Elective.
Instructors: Kundaje, A. (PI) ; Pritchard, J. (PI) ; Tang, H. (PI)

IMMUNOL 201: Advanced Immunology I (MI 211)
For graduate students, medical students and undergraduates. Topics include the innate and adaptive immune systems; genetics and function of immune cells and molecules; lymphocyte activation and regulation of immune responses. Recommended: undergraduate course in immunology.  Elective.
Terms: Win | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Chien, Y. (PI)

MED 255: The Responsible Conduct of Research
Forum. How to identify and approach ethical dilemmas that commonly arise in biomedical research. Issues in the practice of research such as in publication and interpretation of data, and issues raised by academic/industry ties. Contemporary debates at the interface of biomedical science and society regarding research on stem cells, bioweapons, genetic testing, human subjects, and vertebrate animals. Completion fulfills NIH/ADAMHA requirement for instruction in the ethical conduct of research. Prerequisite: research experience recommended. Required.
Terms: Aut, Win, Spr | Units: 1 | Grading: Medical Satisfactory/No Credit.
Instructors: Karkazis, K. (PI) ; Monsen, M. (PI)

MI 215: Principles of Biological Technologies (IMMUNOL 215)
The principles underlying novel as well as commonly utilized techniques to answer biological questions. Lectures and primary literature critiques on topics such as fluorescence microscopy, including applications such as FRET and single-cell analysis; human and murine genetic analysis; FACS; proteomics and analysis of noncoding RNAs. Class participation is emphasized. Prerequisite: biochemistry. Required of first-year graduate students in Microbiology and Immunology and the Immunology program. Elective.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Sarnow, P. (PI)

NENS 230: Analysis Techniques for the Biosciences Using MATLAB
Data analysis and visualization techniques commonly encountered in biosciences research. Fundamentals of the MATLAB computing environment, programming and debugging, data import/export, data structures, plotting, image analysis, introduction to statistical tools. Examples and assignments draw from a range of topics applicable to bioscience research: frequency analysis, genetic data mining, ion channel kinetics, neural spike rasters and spike-triggered averages, cell counting in fluorescence images, regression, PCA, and stochastic simulation. Assignments are practical in nature and demonstrate how to implement specific analyses that a biosciences student is likely to encounter. Assumes no previous programming experience. Elective.
Terms: Aut | Units: 2 | Grading: Medical Satisfactory/No Credit
Instructors: Albarran, E. (PI) ; Sorokin, J. (PI)

PATH 218: Computational Analysis of Biological Information: Introduction to Python for Biologists (GENE 218, MI 218)
Computational tools for processing, interpretation, communication, and archiving of biological information. Emphasis is on sequence and digital microscopy/image analysis. Intended for biological and clinical trainees without substantial programming experience. Elective
Terms: Sum | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Fire, A. (PI)

SBIO 241: Biological Macromolecules (BIOC 241, BIOE 241, BIOPHYS 241)
The physical and chemical basis of macromolecular function. Topics include: forces that stabilize macromolecular structure and their complexes; thermodynamics and statistical mechanics of macromolecular folding, binding, and allostery; diffusional processes; kinetics of enzymatic processes; the relationship of these principles to practical application in experimental design and interpretation. The class emphasizes interactive learning, and is divided equally among lectures, in-class group problem solving, and discussion of current and classical literature. Enrollment limited to 50. Prerequisites: Background in biochemistry and physical chemistry recommended but material available for those with deficiency in these areas; undergraduates with consent of instructor only. Elective.
Terms: Spr | Units: 3-5 | Grading: Medical Option (Med-Ltr-CR/NC).
Instructors: Bryant, Z. (PI) ; Das, R. (PI) ; Ferrell, J. (PI) ; Harbury, P. (PI) ; Huang, P. (PI)

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit
Instructors: Baiocchi, M. (PI) ; DiCiccio, C. (PI) ; LaRocque, K. (PI) ; Xia, L. (PI). Elective.

STATS 116: Theory of Probability 
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent. Elective.
Terms: Aut, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit. 
Instructors: Donoho, D. (PI) ; Hwang, J. (PI) ; Tay, J. (PI) ; Wang, R. (PI)

STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Elective.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit.
Instructors: Bacallado, S. (PI) ; Patel, R. (PI)

STATS 216: Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g., Stats 60), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105). Elective.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tibshirani, R. (PI)


CBIO 299: Directed Reading in Cancer Biology
Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

CBIO 399: Graduate Research
Students undertake investigations sponsored by individual faculty members. Cancer Biology Ph.D. students must register as soon as they begin dissertation-related research work.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

CBIO 801: TGR Project
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

CBIO 802: TGR Dissertation
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit | Grading: TGR

Additional graduate-level courses listed in Stanford University Explore Courses


PhD Program