Learning Outcomes: 1. Create and modify SAS datasets using programming structures within the SAS Data Step (e.g. Do loops, If/Then/Else, Functions, and Arrays).
Audience: Graduate

2. Utilize various SAS Procedures to explore SAS datasets, to summarize information in SAS datasets, and to perform basic statistical analyses.
Audience: Graduate

3. Recognize common SAS program errors, identify strategies for debugging SAS programs, and implement general techniques to check and verify your coding.
Audience: Graduate

Learning Outcomes: 1. Understand building blocks and fundamentals that support core themes of Biostatistics in the application of biomedicine and public health
Audience: Both Grad & Undergrad

2. Conduct basic statistical analyses of biomedical data
Audience: Both Grad & Undergrad

3. Use R for statistical computing
Audience: Both Grad & Undergrad

4. Critique methods and evidence from others' studies
Audience: Graduate

5. Collaborate effectively with biostatisticians
Audience: Graduate

Learning Outcomes: 1. Develop skills to critically review clinical trials literature
Audience: Graduate

2. Formulate focused research questions, specific aims, and key outcomes
Audience: Graduate

3. Recognize the strengths and weaknesses of alternative clinical trials designs and design components
Audience: Graduate

4. Develop related technical skills, including basic sample size calculations and survival analysis
Audience: Graduate

5. Write a clinical trial protocol with all its core components
Audience: Graduate

Learning Outcomes: 1. Apply applicable legal and regulatory responsibilities, liabilities, and accountabilities for clinical trials and health services research studies according to IRBs, HIPAA, CGP, FDA, NIH, ClinicalTrials.gov, and other applicable entities
Audience: Graduate

2. Apply and implement best practices for human subjects’ protection
Audience: Graduate

3. Apply team science through working with multidisciplinary and interprofessional team
Audience: Graduate

4. Analyze requirements in conduct of a clinical trial or health services research study and implement the use of a protocol to standardize procedures
Audience: Graduate

5. Evaluate and select data collection processes and implement quality control and assurance processes
Audience: Graduate

6. Create a protocol and consent/assent processes and documents for a clinical trial or health services research study, as well as supportive components
Audience: Graduate

Learning Outcomes: 1. Know and recognize statistical and probability terminology, symbols, definitions, and formulas
Audience: Graduate

2. Explain the meaning, assumptions, and interrelationships of statistical and probability concepts and formulas
Audience: Graduate

3. Execute probability and statistical calculations from information provided
Audience: Graduate

4. State assumptions, conclusions and interpretation in terms of statistical and probability computations
Audience: Graduate

Learning Outcomes: 1. State the assumptions underlying linear, logistic, survival and Poisson regression models, recognize and address violations of those assumptions, and estimate and interpret regression models to answer epidemiologic and public health research questions.
Audience: Graduate

2. Critique uses of linear, logistic, survival and Poisson regression models in the epidemiologic and public health literature.
Audience: Graduate

3. Translate epidemiologic concepts into statistical modeling assumptions, and explain statistical modeling assumptions in epidemiologic terms.
Audience: Graduate

4. Recognize applications that require methods beyond their expertise, and identify resources to learn about more advanced techniques.
Audience: Graduate

Learning Outcomes: 1. Implement the key principles of ideas from probability, statistics and computer vision algorithms used in medical image analysis
Audience: Undergraduate

2. Recognize which image analysis problems will benefit from which modeling approach
Audience: Undergraduate

3. Apply algorithms about image analysis tasks and implement algorithms and pipelines using MATLAB
Audience: Undergraduate

4. Implement the building blocks taught in this course to independently learn and apply new, but related imaging analysis algorithms
Audience: Undergraduate

Learning Outcomes: 1. Utilize informatics-based tools in translational research including locate relevant informatics tools; select appropriate informatics tools; and use those tools in research for managing and analyzing biomedical and health information.
Audience: Graduate

2. Describe the essential functions of the major clinical systems ( e.g., the EHR-- (electronic health record and its feeder systems, such as radiology and lab) that are relevant to healthcare analytics and quality improvement and the challenges to using these data for research.
Audience: Graduate

3. Explain the role of health information technology standards in the interoperability of research, clinical, and administrative information systems and on secondary use of data for Clinical Translational Research or CTS.
Audience: Graduate

4. Describe the essential information generation, management, analysis, transformation, summarization, and visualization methods that apply to healthcare analytics and quality improvement, such as clinical data; imaging data; consumer and patient reported data; and population-level and environmental exposure data.
Audience: Graduate

5. Identify and describe available methods for handling large-scale data for machine learning and medical language processing.
Audience: Graduate

6. Illustrate the nature of the contributions in consulting and/or collaborating with biomedical and health informaticians throughout the life cycle of clinical and translational research projects: use the terminology and principles of biomedical informatics; discriminate among the different subdomains of biomedical informatics; and enumerate the roles of biomedical informatics specialists.
Audience: Graduate

7. Identify how structure and organization of information in a domain can affect quality research foci through fundamental data categories and elements, terminologies and taxonomies, and ontologies.
Audience: Graduate

8. Identify, retrieve, and manage biomedical and health science knowledge through literature searches using advanced search techniques (MeSH, PubMed, Google Scholar, etc.).
Audience: Graduate

9. Discuss the fundamental principles and practices that address the ethical, legal, social, privacy, and security implications of bio- medical and health informatics.
Audience: Graduate

Learning Outcomes: 1. Explain the biology and significance of the most commonly measured molecules in molecular biology.
Audience: Undergraduate

2. Identify the primary computational problems associated with each type of biological data.
Audience: Undergraduate

3. Explain the major algorithms and approaches used to address the computational problems.
Audience: Undergraduate

4. Implement efficient algorithms for bioinformatic tasks through the use of the discussed approaches.
Audience: Undergraduate

5. Apply the discussed algorithms to novel but closely-related tasks.
Audience: Undergraduate

6. Understand the methods covered such that parts of the methods sections of published biological papers are interpretable.
Audience: Undergraduate

7. Begin to gain the qualifications of a bioinformatician.
Audience: Undergraduate

Learning Outcomes: 1. Recall and state the formal definitions of the mathematical objects and their properties in systems biology (e.g., reaction networks, reaction rate equations, mass-action kinetics models, detailed balanced and complex balanced systems, Lyapunov functions, etc.).
Audience: Both Grad & Undergrad

2. Use such definitions to argue that a mathematical object does or does not have the condition of being a particular type or having a particular property (e.g., reversible, weakly reversible, mass-action, detailed balanced, complex balanced, globally stable, oscillatory, persistent, permanent, etc.).
Audience: Both Grad & Undergrad

3. Recall and state the standard theorems of the field (e.g., the Horn-Jackson theorem, the deficiency zero theorem, theorems on characterization of mass-action systems, theorems on persistence and permanence, theorems on dynamical equivalence, etc.) and recall the arguments for these theorems and the underlying logic of their proofs.
Audience: Both Grad & Undergrad

4. Construct mathematical arguments related to the above definitions, properties, and theorems, including the construction of examples and counterexamples.
Audience: Both Grad & Undergrad

5. Convey arguments using English and appropriate mathematical terminology, notation and grammar.
Audience: Both Grad & Undergrad

6. Model real biological systems by means of systems of differential equations, and be able to use software (such as Matlab) for visualization of their dynamics. Example models could include: (i) Enzymes, substrates and saturating kinetics, (ii) Glycolytic oscillations, (iii) Cell cycle control, budding yeast cell cycle models, (iv) Activator-inhibitor and positive feedback systems.
Audience: Both Grad & Undergrad

7. Identify applications of course content in current areas of research.
Audience: Graduate

Learning Outcomes: 1. Recognize problems in human genetics that are appropriate for statistical modeling
Audience: Both Grad & Undergrad

2. Identify appropriate statistical procedures and computational algorithms for different tasks
Audience: Both Grad & Undergrad

3. Gain practical experience in applying a select set of statistical methods on real data and evaluate its outputs
Audience: Both Grad & Undergrad

4. Evaluate the strengths and weaknesses of different statistical and computational approaches designed for a specific biological problem
Audience: Graduate

Learning Outcomes: 1. Summarize key concepts of statistical methods in epidemiology study: study design, association, causation
Audience: Both Grad & Undergrad

2. Build parametric or semiparametric model for analyzing categorical data and survival data
Audience: Both Grad & Undergrad

3. Utilize model design tools for model performance assessment
Audience: Both Grad & Undergrad

4. Build semiparametric model for analyzing categorical data and survival data
Audience: Graduate

Learning Outcomes: 1. Summarize the objectives of clinical trials and key statistical design components
Audience: Both Grad & Undergrad

2. Design the clinical trials and investigate the operating characteristics of the design to implement clinical trials
Audience: Both Grad & Undergrad

3. Write the protocol section of statistical considerations and communicate the design of clinical trials to both statisticians and clinicians
Audience: Both Grad & Undergrad

4. Build sequential and adaptive methods for clinical trials
Audience: Graduate

Learning Outcomes: 1. Extend the knowledge of regression analysis beyond ordinary linear models
Audience: Graduate

2. Describe the features of correlated data and their implications in drawing inference
Audience: Graduate

3. Construct proper linear and generalized linear models for longitudinal and clustered data
Audience: Graduate

4. Describe the assumptions needed for estimation and inference
Audience: Graduate

5. Implement the inference procedures to solve real-world problems using statistical packages such as SAS and R
Audience: Graduate

6. Use diagnostic tools to assess model fit
Audience: Graduate

7. Interpret and present the analytic results to answer substantive questions
Audience: Graduate

Learning Outcomes: 1. Apply, analyze, and evaluate advanced theories, concepts, and methods in Biostatistics in relation to the discipline of Epidemiology.
Audience: Graduate

Learning Outcomes: 1. Demonstrate understanding of biomedical informatics concepts, methods, and tools used in precision medicine and population health.
Audience: Graduate

2. Demonstrate understanding of real-world data (patient-generated, clinical, and genomic) and data standards used in biomedical research.
Audience: Graduate

3. Demonstrate understanding of FAIR Guiding Principles for scientific data management and stewardship.
Audience: Graduate

4. Demonstrate understanding of regulations for using protected health information (PHI) data in health research, and ability to recognize potential ethical and compliance issues.
Audience: Graduate

Learning Outcomes: 1. Apply concepts learned in coursework to real biomedical applications
Audience: Both Grad & Undergrad

2. Read and effectively analyze scientific literature
Audience: Both Grad & Undergrad

3. Develop critical, analytical, and independent thinking skills
Audience: Both Grad & Undergrad

4. Create literature reviews and publications
Audience: Graduate

Learning Outcomes: 1. Apply ray and wave based light propagation models
Audience: Graduate

2. Explain the process of image formation in conventional imaging systems using theory and computational models
Audience: Graduate

3. Select and combine the different components required in an imaging system to perform light manipulation, collection, and image reconstruction
Audience: Graduate

4. Apply the linear matrix and integral operators that model light propagation
Audience: Graduate

5. Apply the linear inverse algorithms that allow an imaging system to reconstruct properties of the scene from collected data
Audience: Graduate

6. Simulate different computational imaging systems and perform computation on simulated datasets
Audience: Graduate

7. Understand the most common computational imaging techniques and be able to use and adapt them for their own applications
Audience: Graduate

Learning Outcomes: 1. Analyze longitudinal data in a variety of settings and with a variety of outcome variables
Audience: Graduate

2. Apply statistical methods in fitting longitudinal data models for addressing scientific questions
Audience: Graduate

3. Perform longitudinal data analyses in statistical softwares such as R and/or SAS
Audience: Graduate

4. Interpret and communicate the scientific meanings of the results to both statisticians and non-statisticians (such as clinicians and scientists)
Audience: Graduate

Learning Outcomes: 1. Identify ethical issues and recognize challenges to integrity that arise during the course of planning and conducting research or reporting research results.
Audience: Graduate

2. Formulate a justified response to an ethical issue or integrity challenge, using ethical decision-making approaches and concepts.
Audience: Graduate

3. Identify where to find professional resources that provide guidance for resolving ethical and research integrity issues.
Audience: Graduate

4. Conduct improved discussions about ethical issues.
Audience: Graduate

Learning Outcomes: 1. Summarize the features of censored data and their implications in drawing inference
Audience: Graduate

2. Implement proper non- and semi-parametric methods for analysis of various types of data
Audience: Graduate

3. Recognize and check the assumptions needed for estimation and inference
Audience: Graduate

4. Implement the inference procedures to solve real-world problems using statistical packages such as R (or SAS)
Audience: Graduate

5. Interpret and present the analytic results in a clear and coherent way to answer substantive questions
Audience: Graduate

Learning Outcomes: 1. Recognize mathematical and computational aspects of modern image processing algorithms for image analysis and medical image processing
Audience: Graduate

2. Interpret papers in the mainstream medical image analysis venues
Audience: Graduate

3. Develop expertise in implementing algorithms described in research papers (or appropriate modifications) in a high level programming language on practical image processing tasks
Audience: Graduate

4. Discuss algorithmic developments and experimental findings in the form of a research report for a technical audience
Audience: Graduate

Learning Outcomes: 1. Implement the key concepts of biomedical image processing and analysis
Audience: Graduate

2. Describe the key concepts of statistical inference procedures for single and multiple images
Audience: Graduate

3. Apply scalable computation in breaking bigger imaging problems into smaller computable problems
Audience: Graduate

4. Describe functional data analysis (FA), geometric data analysis (GDA) and topological data analysis (TDA) methods in analyzing biomedical images
Audience: Graduate

Learning Outcomes: 1. Demonstrate their understanding of basic theories, current approaches, key concepts, and common practices in the area of deep learning for computer vision.
Audience: Graduate

2. Recognize and distinguish among a variety of visual recognition problems in computer vision, including their problem formulations and evaluation metrics.
Audience: Graduate

3. Utilize and implement deep learning models to solve visual recognition problems.
Audience: Graduate

4. Design deep learning models for visual recognition problems, conduct experiments to evaluate the proposed model, and analyze and interpret the results.
Audience: Graduate

5. Communicate effectively through written reports, oral presentations, and discussions.
Audience: Graduate

Learning Outcomes: 1. Recognize the various types and sources of health data, and the key issues involved in processing health data including security, privacy, noise, and missingness
Audience: Graduate

2. Conduct observational studies using data that are centered around answering research questions
Audience: Graduate

3. Implement basic programming and data manipulation using R, and be familiar with a variety of techniques such as descriptive analysis, regression methods, prediction models, phenotyping, and natural language processing
Audience: Graduate

4. Apply common tools for managing, accessing and mapping health data, including data standards, controlled vocabularies, and ontologies
Audience: Graduate

Learning Outcomes: 1. Recognize problems in molecular biology that are appropriate for network modeling
Audience: Graduate

2. Identify appropriate network algorithms for different tasks
Audience: Graduate

3. Evaluate the strengths and weaknesses of different network algorithms designed for a specific biological problem
Audience: Graduate

4. Gain practical experience in applying a select set of network algorithms on real data and evaluate its outputs
Audience: Graduate

5. Understand the algorithmic and statistical concepts of different network-based approaches
Audience: Graduate

Learning Outcomes: 1. Select and implement appropriate algorithms and probabilistic models for analyzing genomes, RNA, proteins, and biological networks
Audience: Graduate

2. Write a manuscript describing a bioinformatics research study, including the motivation for the research, the scientific outcomes, and the detailed methods required to reproduce the study
Audience: Graduate

3. Read a bioinformatics research paper to identify the key computational techniques and assess the evidence supporting the paper's claims
Audience: Graduate

4. Identify emerging biological data types and data processing (e.g., single cell biology) and how the data can contribute to their research
Audience: Graduate

Learning Outcomes: 1. Explain the breadth of research in biomedical data science.
Audience: Graduate

2. Describe the approaches, resources and environment available to conduct interdisciplinary research at UW.
Audience: Graduate

3. Describe the high-level goals of research programs of individual faculty trainers in the Biomedical Data Science program.
Audience: Graduate

Learning Outcomes: 1. Design, implement, and evaluate a graduate level independent research project that applies or extends these theories, concepts, and methods
Audience: Graduate

Learning Outcomes: 1. Derive the nuisance tangent space, its complement, and the corresponding efficient influence function in semiparametric models
Audience: Graduate

2. Apply a variety of semiparametric methods and models in applications ranging from biomedical studies to social sciences
Audience: Graduate

3. Perform machine learning algorithms for estimating nuisance functions in software such as R and/or Python
Audience: Graduate

Learning Outcomes: 1. Understand the statistical and computational background underlying many state-of-the-art techniques for the pre-processing and analysis of high-throughput genomics datasets
Audience: Graduate

2. Identify the appropriateness and limitations of such methods in a variety of settings.
Audience: Graduate

3. Discuss scientific problems and identify the statistical and computational aspects embedded in the processing and analysis of genomics datasets.
Audience: Graduate

4. Become proficient in select software packages commonly used in analysis of next-generation sequencing data.
Audience: Graduate

Learning Outcomes: 1. Critically evaluate quantitative approaches in the scientific literature
Audience: Graduate

2. Articulate the biological context of a research question and the scientific relevance of analysis results
Audience: Graduate

3. Identify and articulate the strengths and weaknesses of different study designs and analysis methods, including potential biases in research data sets
Audience: Graduate

Learning Outcomes: 1. Critically evaluate quantitative approaches in the scientific literature
Audience: Graduate

2. Articulate the biological context of a research question and the scientific relevance of analysis results
Audience: Graduate

3. Identify and articulate the strengths and weaknesses of different study designs and analysis methods, including potential biases in research data sets
Audience: Graduate

Learning Outcomes: 1. Describe the review processes for journal articles and research grant proposals
Audience: Graduate

2. Write clear, well-formed journal articles and research grant proposals
Audience: Graduate

3. Provide a clear oral presentation of their research work
Audience: Graduate

4. Demonstrate understanding of research on unconscious bias (for example, in reviewing papers/grants, in writing/evaluating recommendation letters, and in hiring/promotion) and assumptions and strategies to overcome them
Audience: Graduate

Learning Outcomes: 1. Prepare a web-based profile of their research efforts and products
Audience: Graduate

2. Describe good practices for research scientists’ participation in social media
Audience: Graduate

3. Define sexual harassment and describe practices for handling sexual harassment
Audience: Graduate

4. Describe strategies for forming and managing scientific collaborations
Audience: Graduate

5. Construct a personal website for networking and self-promotion
Audience: Graduate

6. Identify employment opportunities at universities and in industry and demonstrate understanding of strategies for applying and interviewing for such positions
Audience: Graduate

7. Describe and compare different teaching practices and methods for motivating students
Audience: Graduate

Learning Outcomes: 1. Develop critical, analytical, and independent thinking skills to support the development of independent research
Audience: Graduate

Learning Outcomes: 1. Utilize informatics-based tools in translational research including: locate relevant informatics tools; select appropriate informatics tools; and use those tools in research for managing and analyzing biomedical and health information. Describe the essential functions of the major clinical systems (e.g., the EHR and its feeder systems, such as radiology and lab) that are relevant to CTS and the challenges to using these data for research.
Audience: Graduate

2. Describe the essential functions of major research computer systems (e.g., clinical trials management systems, biospecimen management systems, research grant and finance management systems, and research services tracking systems) that are relevant to CTS.
Audience: Graduate

3. Compare and contrast the organizational roles and principal responsibilities essential for access, management, and governance of data related to CTS.
Audience: Graduate

4. Explain the role of health information technology standards in the interoperability of research, clinical, and administrative information systems and on secondary use of data for CTS.
Audience: Graduate

5. Describe the essential information generation, management, analysis, transformation, summarization, and visualization methods that apply to CTS data, such as: genomic, proteomic and other "-omics" data; clinical data; imaging data; consumer and patient-reported data; and population-level and environmental exposure data.
Audience: Graduate

6. Illustrate the nature of the contributions in consulting and/or collaborating with biomedical and health informaticians throughout the life cycle of clinical and translational research projects: use the terminology and principles of biomedical informatics; discriminate among the different subdomains of biomedical informatics; and enumerate the roles of biomedical informatics specialists.
Audience: Graduate

7. Identify how structure and organization of information in a domain can impact researchers' translational research foci through fundamental data categories and elements, terminologies and taxonomies, and ontologies.
Audience: Graduate

8. Identify, retrieve, and manage biomedical and health science knowledge through literature searches using advanced search techniques (MeSH, PubMed, Google Scholar, etc.).
Audience: Graduate

9. Discuss the fundamental principles and practices that address the ethical, legal, social, privacy, and security implications of bio- medical and health informatics.
Audience: Graduate

Learning Outcomes: 1. Discuss how methods from computer science, statistics, information science and engineering are applied to problems in biology, medicine and population health
Audience: Graduate

2. Recognize and be able to define applications in translational bioinformatics, clinical informatics and public health informatics
Audience: Graduate

Learning Outcomes: 1. Describe main concepts and challenges in health informatics.
Audience: Graduate

2. Identify the different aspects of electronic health records.
Audience: Graduate

3. Recognize medical safety issues related to chart maintenance and poor systems.
Audience: Graduate

4. Instruct patients in proper use of a personal health record (PHR).
Audience: Graduate

5. Compare and contrast the concept of learning health systems that is patient-centered, population-based, and promotes learning from data.
Audience: Graduate

6. Define population-based care and the informatics underlying it.
Audience: Graduate

7. Recognize different types of clinical decision support.
Audience: Graduate

8. Describe the area of quality measurement and improvement.
Audience: Graduate

9. Formulate how the area of quality measurement and improvement applies to clinical practice.
Audience: Graduate

10. Recognize the types and limitations of different types of quality measures.
Audience: Graduate

11. Formulate a clinical question as an answerable one, and then be able to select the appropriate resource and make optional use of it.
Audience: Graduate

12. Recognize growing role of genomics and personalized medicine in care.
Audience: Graduate

13. Describe and manage ethical issues in privacy and security.
Audience: Graduate

Learning Outcomes: 1. Develop skills to design, implement and apply computational and statistical frameworks to address diverse biomedical questions
Audience: Graduate