Participating departments in the IGERT program have all defined a Big Data PhD track, which articulates how the IGERT requirements map to the department’s requirements without creating any additional burden. In some departments, these Big Data tracks go beyond this mapping.

UPDATE: An official Advanced Data Science Option has been approved and qualifying coursework will now be noted on transcripts! This will replace the Big Data Tracks, which are not transcripted.

Links below will be updated as each department updates individual web pages. Contact persons remain the same.

Recommended Data Science Courses

AA 543 (3) Computational Fluid Dynamics
Examines numerical discretization of the inviscid compressible equations of fluid dynamics; finite-difference and finite-volume methods; time integration, iterative methods, explicit and implicit algorithms; consistency, stability, error analysis, and properties of numerical schemes; grid generation; and applications to the numerical solution of model equations and the 2D Euler equations.

Uri Shumlak
Current Listing

 

AA 544 (3) Turbulence Modeling and Simulation
Examines numerical discretization of the incompressible Navier-Stokes equation; projection method, introduction to turbulence; Reynolds Averaged Navier-Stokes equations; algebraic, one-equation, and two-equation turbulence models; large-eddy simulation; direct numerical simulation; and applications to the numerical solution of laminar and turbulent flows in simple geometries.

Antonio Ferrante
Current Listing

 

AA 545 (3) Computational Methods for Plasmas
Develops the governing equations for plasma models – particle, kinetics, and MHD. Applies the governing equation to plasma dynamics through the PIC method and integration of fluid evaluation equations. Examines numerical solution to equilibrium configurations, and linear stability by energy principle and variational method.
Antonio Ferrante
Current Listing

AMATH 500A (1-2) High-Performance Scientific Computing
This course will introduce aspects of scientific computing and computational science that go beyond the Matlab-based introduction of AMath 301 or 352 and will introduce other languages (primarily Fortran 90/95/2003 and Python), debugging strategies, parallel computing (at the multi-core and cluster level), visualization tools for large data sets, and concepts such as Validation and Verification (V&V), uncertainty quantification (UQ), reproducible research, and scientific software design.

Instructor
Current Listing

 

AMATH 574 (5) Conservation Laws and Finite Volume Methods
Theory of linear and nonlinear hyperbolic conservation laws modeling wave propagation in gases, fluids, and solids. Shock and rarefaction waves. Finite volume methods for numerical approximation of solutions; Godunov’s method and high-resolution TVD methods. Stability, convergence, and entropy conditions. Prerequisite: AMATH 586 or permission of instructor.

Randy LeVeque
Current Listing

 

AMATH 581 (5) Scientific Computing
Project-oriented computational approach to solving problems arising in the physical/engineering sciences, finance/economics, medical, social and biological sciences. Problems requiring use of advanced MATLAB routines and toolboxes. Covers graphical techniques for data presentation and communication of scientific results. Prerequisite: Proficiency in basic MATLAB or AMATH 301, or permission of instructor.

Eli Shlizerman
Current Listing

 

AMATH 582 (5) Computational Methods for Data Analysis
Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression. Prerequisite: either MATLAB and linear algebra or permission of instructor.

Jose Nathan Kutz
Current Listing

 

AMATH 583 (5) High-Performance Scientific Computing
This class will cover a selection of topics in high-performance computing (HPC), briefly introducing many of the issues that arise when solving large scale computational problems in science and engineering.

Ulrich Hetmaniuk
Current Listing

 

AMATH 584 (5) Applied Linear Algebra and Introductory Numerical Analysis
Numerical methods for solving linear systems of equations, linear least squares problems, matrix eigen value problems, nonlinear systems of equations, interpolation, quadrature, and initial value ordinary differential equations.

Anne Greenbaum
Current Listing

 

AMATH 585 (5) Numerical Analysis of Boundary Value Problems
Numerical methods for steady-state differential equations. Two-point boundary value problems and elliptic equations. Iterative methods for sparse symmetric and non-symmetric linear systems: conjugate-gradients, preconditioners. Prerequisite: AMATH 581 or MATH 584 which may be taken concurrently.

Anne Greenbaum
Current Listing

 

AMATH 586 (5) Numerical Analysis of Time Dependent Problems
Numerical methods for time-dependent differential equations, including explicit and implicit methods for hyperbolic and parabolic equations. Stability, accuracy, and convergence theory. Spectral and pseudospectral methods. Prerequisite: AMATH 581 or AMATH 584.

Instructor
Current Listing

ASTR 427 (3) Numerical Methods of Astrophysics
This is a hands-on course to learn methods for numerically solving problems that arise in astrophysics. Some programming experience is required. An emphasis is placed on high performance. Topics include ordinary differential equations, root finding, optimization, Monte-Carlo methods, basic data structures and algorithms, and parallel techniques.

Instructor
Current Listing

 

ASTR 597B (3) Big Data in Astronomy: Introduction to Large Surveys
The goal of this course is to prepare you for research with large survey data, teach you how to think about such data sets, and give you an overview of what is or soon will be available. While focused on astronomical surveys, the course may be suitable for advanced undergraduates and non-majors interested in learning about working with large scientific data sets.

Mario Juric
Current Listing

 

ASTR 599 / AMATH 500 (1) Scientific Computing with Python
This is a Graduate seminar course offered jointly through the UW Astronomy & Applied Math departments. It is designed as a comprehensive introduction to scientific computing in Python, geared toward graduate students, postdocs, and researchers in scientific fields which depend on analysis of large datasets.

Jake Vanderplas
Current Listing

BIO 419/519 (4) Data Science for Biologists
The objective of this course is to provide students with foundational knowledge in mathematics and basic tools in computation to practice data science in broadly biologically focused fields. The course will focus on the basics of data wrangling, data analytics, statistics and visualization. The target audience is advanced undergrads and beginning graduate students, including students studying biology, neurobiology, microbiology, bioengineering, and others fields working with biologically relevant data.

Bing Brunton
Current Listing

BIOST 578A (2) Bioinformatics for Big Omics Data
Stat Meth Epi Studies

Li Hsu
Current Listing

 

BIOST 578B (tbd) Introduction to Biomedical Data Science
This is a new course and is still under development.

Instructor TBD
No Current Listing

 

BIOST 578C (tbd) Machine Learning for Biomedical Big Data
Will be offered as BIOST 546 starting Spring 2015. This course provides an introduction to statistical machine learning methods for analysis of Biomedical Big Data, including high dimensional regression and classification methods, variable selection techniques, high dimensional inference, clustering and dimension reduction methods. Prerequisites: Knowledge of statistical inference at the level of BIOST 511/512 and familiarity with computing or permission of the instructor.

Instructor TBD
No Current Listing (check back Spring 2015)

CSE 414 (4) Introduction to Database Systems
Introduces database management systems and writing applications that use such systems; data models, query languages, transactions, database tuning, data warehousing, and parallelism. Intended for non-majors. Not open for credit to students who have completed CSE 344. Prerequisite: minimum grade of 2.5 in CSE 143.

Hal Perkins
Current Listing

 

CSE 527 (4) Computational Biology
Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as network reconstruction and analysis, sequence analysis, regulatory analysis and genetic analysis. Techniques such as Bayesian networks, Gaussian graphical models, structure learning, expectation-maximization. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.

Su-In Lee
Current Listing

 

CSE 547 / STAT 592 (4) Machine Learning for Big Data
Machine Learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models. Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel (Map-reduce, GraphLab). Prerequisite: either STAT 535 or CSE 546.

Emily Fox
Current Listing

 

CSE 599A (4) Molecular Biology as a Computational Science
This is a course in molecular biology for computer science students interested in computational research in the Life Sciences, such as bioinformatics and bioengineering. The premise of the course is that cell biology can be described and analyzed in much the same way as complex software systems. Indeed, this is how Systems Biology studies gene programs.
The course assumes some exposure to object-oriented design, and makes use of python (although deep knowledge of python is not required). The course only requires a high school background in chemistry and biology.

Joseph Hellerstein
Current Listing

 

There are also several undergraduate courses open to non-majors that provide useful background. In particular:
CSE 373: Data Structures and Algorithms
CSE 374: Intermediate Programming Concepts and Tools
CSE 410: Computer Systems
CSE 415: Introduction to Artificial Intelligence
CSE 417: Algorithms and Computational Complexity

All courses are listed here.

GENOME 540 (4) Intro to Computational Molecular Biology
Algorithmic and probabilistic methods for analysis of DNA and protein analysis. Students must be able to write computer programs for data analysis. Prior coursework in biology and probability highly desirable. Prerequisite: permission of instructor.

Phil Green
Current Listing

 

GENOME 541 (4) Intro to Computational Molecular Biology
Provides a survey of topics within the field of computational molecular biology. Prerequisite: GENOME 540 or permission of instructor.

Bill Noble
Current Listing

HCDE 511 (4) Information Visualization
The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Vision and perception. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces.

Instructor
Current Listing

 

HCDE 517 (4) Usability Studies
Discusses the human-computer interface (HCI) as the communicative aspect of a computer system. Analyzes usability issues in HCI design, explores design-phase methods of predictability, and introduces evaluative methods of usability testing.

Instructor
Current Listing

MATH 514 (3) Networks and Combinatorial Optimization
Mathematical foundations of combinatorial and network optimization with an emphasis on structure and algorithms with proofs. Topics include combinatorial and geometric methods for optimization of network flows, matching, traveling salesmen problem, cuts, and stable sets on graphs. Special emphasis on connections to linear and integer programming, duality theory, total unimodularity, and matroids. Prerequisite: either MATH 308 or AMATH 352 any additional 400-level mathematics course. Offered: jointly with AMATH 514.

Thomas Rothvoss
Current Listing

 

MATH 515 (3) Networks and Combinatorial Optimization
Maximization and minimization of functions of finitely many variables subject to constraints. Basic problem types and examples of applications; linear, convex, smooth, and nonsmooth programming. Optimality conditions. Saddlepoints and dual problems. Penalties, decomposition. Overview of computational approaches. Prerequisite: linear algebra and advanced calculus. Offered: jointly with AMATH 515/IND E 515.

Instructor
Current Listing

 

MATH 516 (3) Networks and Combinatorial Optimization
Methods of solving optimization problems in finitely many variables, with or without constraints. Steepest descent, quasi- Newton methods. Quadratic programming and complementarity. Exact penalty methods, multiplier methods. Sequential quadratic programming. Cutting planes and nonsmooth optimization. Prerequisite: MATH 515. Offered: jointly with AMATH 516.

Instructor
Current Listing

MEBI 531 (3) Life and Death Computing
MEBI 531 addresses the complex software design issues that come up in biomedical and health informatics, programming for safety critical applications in medicine and health care, as well as in the application of computing to unlock the secrets of life. Examples from biology, medicine and health motivate software engineering topics such as: use of abstraction layers, design of tightly coupled but modular and extensible software, formal models and safety in real time control of medical equipment, design of network application protocols, integration of diverse biomedical data sources.

Instructor
Current Listing

 

MEBI 550 (3) Knowledge Representation and Biomedical Applications
MEBI 550 deals with the principles of knowledge representation and reasoning, with application to biology, medicine and health. Many of the examples will use the Common Lisp programming language, but prior knowledge of Lisp is not assumed. Other programming and knowledge representation languages will also be introduced, such as Prolog.

Ira Kalet
Current Listing

 

BIME 591C (1) BHI Research Colloquium
“Cancer, stochastic models and mathematical biology”
This section will focus on topics related to the Clinical Target Volume research project and to related topics more broadly in the area of mathematical biology. The topics will be largely dependent on the interests of the participants, but will include discussion of symbolic and functional programming (Lisp and ML), topics from abstract mathematics leading up to category theory, basics of stochastic modeling and Markov chains, anatomy, cancer (basic ideas, metastasis, staging, surgical and radiation therapy), and other applications of abstract mathematics in biology and medicine.

Ira J. Kalet
Current Listing

SOC 590 (3) Special Topics in Sociology
“Big Data and Population Processes”
In this course, we will study how traditional methods used in social sciences can help us make sense of new data sources, and how these new data sources may require new approaches and research design. There will be a mix of lectures, student-led discussions, and hands-on computational activities (e.g., how to access and analyze data from social media platforms like Twitter and Facebook, how to approach large data sets, etc.).
We will discuss a number of substantive topics related to the emergence of (big) data-driven discovery in social sciences, with emphasis on population processes. By the end of the course, students will be familiar with relevant literature at the intersection of demographic research and computational social science. The main goals of the course are i) to develop critical thinking about the emergent field of big data analysis ii) to learn some of the methods, approaches and tools of big data analysis iii) to identify research questions in your own area of interest that could be addressed with innovative data sources and to devise an appropriate research plan.

Emilio Zagheni
Current Listing

 

SOCW1 590B (3) Interdisciplinary Research Career Development: Roadmaps & Practical Strategies
Graduate seminar creating a forum for students spanning multiple disciplines to learn about national trends increasing need for interdisciplinary and transdisciplinary readiness in research careers as well as translations between research and real world application; focus on tools and strategies to increase one’s capacities and readiness for inter/transdisciplinary research oriented careers; engage collaboratively with peers from other disciplines in these aims; and hone your interdisciplinary career roadmaps for graduate training and beyond.

*Course undergoing final approval; contact instructor for information; emailing about interest in course is useful.

Paula Nurius
Current Listing

STAT 302 (3) Statistical Software and Its Applications
Introduction to data structures and basics of implementing procedures in statistical computing packages, selected from but not limited to R, SAS, STATA, MATLAB, SPSS, and Minitab. Provides a foundation in computation components of data analysis.

Friedrich-Wilhelm Scholz
Current Listing

 

STAT 391 (4) Probability and Statistics for Computer Science
Fundamentals of probability and statistics from the perspective of the computer scientist. Random variables, distributions and densities, conditional probability, independence. Maximum likelihood, density estimation, Markov chains, classification. Applications in computer science.

Instructor
Current Listing

 

STAT 403 (4) Introduction to Resampling Inference
Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases.

Instructor
Current Listing

 

STAT 592 / CSE 547 (4) Machine Learning for Big Data
Machine Learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models. Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel (Map-reduce, GraphLab). Prerequisite: either STAT 535 or CSE 546.

Emily Fox
Current Listing

Certificate in Cloud Computing
Gain an in-depth understanding of cloud computing models, applications, platforms, infrastructures and technologies. Get hands-on experience in developing scalable, efficient systems for the cloud and building scalable applications. Work on projects using frameworks like Hadoop and MapReduce, which enable massive scalability for processing and analyzing large data sets. Understand the platforms of key cloud vendors as well as the decision-making process for adopting a cloud migration strategy.

More Information

 

Certificate in Data Science
Develop the computer science, mathematics and analytical skills in the context of practical application needed to enter the field of data science. Discover how to use data science techniques to analyze and extract meaning from extremely large data sets, or “big data.” Become familiar with modern database systems, data models, and query interfaces. Learn how to use statistics, machine learning, text retrieval and natural language processing to analyze data and interpret results. Practice using these tools and techniques on data sets of increasing complexity and scale.

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Course # TBD (X) Software Engineering for Quantitative Biologists
Biology has transitioned from a descriptive enterprise to a quantitative science. This transition has been driven by a combination of low cost sequencing technology and radical reductions in the cost of computing. Indeed, modern biology relies heavily on computational artifacts, both code and data, to produce scientific results (e.g., genomic data sets and computational tools for biochemical pathways.

Joe Hellerstein
Current Listing

 

ENGR 401 (1) PEERS Seminar: Leadership Development to Promote Equity in Engineering Relationships
The seminar will enlist engineering students’ energy, creativity, social conscience, and on-the-ground perspectives in improving the diversity environment in the UW College of Engineering. Students will explore topics such as diversity in science and engineering, impact of unconscious bias, community engagement, leadership, etc. The seminar will culminate in a student-developed short presentation which will be the core of presentations in the College of Engineering PEERs initiative. Students who successfully complete the seminar can apply for quarter-long internship opportunities as PEER Leaders.

This course is appropriate for both undergrads and grads.

Joyce Yen, Program/Research Manager, UW ADVANCE Center for Institutional Change
Sapna Cheryan, Assistant Professor, Psychology

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