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{"sheet":"Yale non-credit courses","index":0,"n_rows":9,"n_cols":7,"rows_kept":9,"cut":false,"hidden":false,"merged":[]}
["Organization","Key offerings","Level","Cost","URL","Schedule","Course examples"]
["Yale research data support","Interactive and self-service solutions","Beginner","Free","https://researchdata.yale.edu/home ","https://researchdata.yale.edu/training-calendar ","Interactive Solutions: \n- Workshops, consultations, and training\n- Research Data Support Services group\nSelf-Service Solutions: Units, Resources, and Tools Supporting Data\n- Create a data management plan with the DMPTool\n- Find a research data repository\n- Learn about research data management at your own pace\n- Identify data-related library resources\n- Contact specific centers, units, and staff\n- Get further assistance on Yale research"]
["Yale Center for Research Computing","R, HPC, Python, Git, Linux, Deep Learning, etc. Online videos and class room lectures available. ","Beginner-Intermediate","Free","https://research.computing.yale.edu/training ","https://research.computing.yale.edu/calendar2","- Introduction to HPC\n- Introduction to HPC on the Milgram Cluster\n- Scripting with Python\n- Writing Efficient A2 Code\n- Using Git for Source Code Control\n- Practical Introduction to Linux\n- Python MPI for Parallel Programming\n- GeoComputation Bootcamp: Introduction to GRASS GIS\n- XSDEDE Workshop:  Writing a Successful XSEDE Proposal\n- NVIDIA Workshop on Deep Learning\n- Vendor partner training: Introduction to Stata, Data Management, Introduction to Bayesian Statistics Using Stata, and Bayesian Multilevel/Longitudinal Modeling."]
["Center for Science and Social Science Information (CSSSI) ","Data visualization, R, Python, SPSS, etc. ","Beginner","Free","https://csssi.yale.edu/instruction","https://csssi.yale.edu/instruction/workshop-and-instruction-calendar ","- Finding & Using Images for Presentation and Publication Across the Sciences\n- Data Visualization in R with ggplot2\n- First Steps with Python\n- Creating Animated Visualizations in R\n- Getting Started with SPSS\n- Advanced Google Scholar in 30 Minutes"]
["Cushing Medical Library","Consultation, classes, workshops, guides on database/software and video tutorials on evidence-based practice and research impact with specific focus on Bioinformatics","Beginner","Free","https://library.medicine.yale.edu/bioinformatics","http://schedule.yale.edu/calendar/medical-workshops/?cid=5105&t=d&d=0000-00-00&ct%5B%5D=28023&cal%5B%5D=5105","- Analysis of microarray and next-generation sequencing (NGS) data using library-licensed bioinformatics tools\n- Enrichment (pathway and network) analysis of differentially regulated molecules.  \n- Finding, retrieving, and using public datasets for cross-referencing research results or hypothesis testing  \n- Complex literature searches. \n- Identification of appropriate bioinformatics software or databases to answer specific research questions\n- Development or narrowing hypothesis in silico by using existing public omics data and literature\n- Hands-on tutorials on bioinformatics software licensed by the Medical Library"]
["Geographic Information Systems at Yale University","Encompasses a range of spatial applications and services used by many disciplines such as calculating space, analyzing spatial relationships, patterns or trends, to cartography, and visualization","Beginner","Free","https://guides.library.yale.edu/gis","https://guides.library.yale.edu/GIS/gisworkshops","- Intro to GIS: ArcGIS​ Video (2:15 hrs) | Data\n​- Intro to GIS: QGIS Video (58:53 mins) | Data\n- Intermediate GIS: ArcGIS​ Video (2:44 hrs) | Data\n- Intermediate GIS: QGIS - under video post-production.\n- BASIS or GIS Education, Applications and Software \n- Penn State GEOG 485 GIS Programming and Automation (Python)"]
["Yale StatLab","Work with teachers, learners, and researchers through workshops, walk-in consultation, computing resources at Yale University to support data analysis.","Beginner","Free","http://statlab.yale.edu/","http://statlab.yale.edu/workshops/ ","- First and Second Steps with MATLAB\n- First and Second Steps with R\n- Collecting and Analyzing Survey Results\n- Analyzing Survey Responses in Qualtrics\n- First Steps with Stata\n- Machine learning in R"]
["Yale data workshops","resources for data science and data management in the natural and life sciences","Beginner","Free","https://guides.library.yale.edu/sciencedata","https://guides.library.yale.edu/c.php?g=296024&p=1975001","- Science Data Resources: From Astronomy to Bioinformatics\n- Data Visualization Literacy\n- Data Management Planning Basics\n- Intro to Research Data Management\n- Finding and Using Social Science Data and Statistics\n- Bioinformatic tools on \n----Text mining\n----Gene Prediction / Annotation\n----Expression Analysis\n----Gene Regulation\n----Variation\n----Pharmacogenomics"]
["Yale University Library Digital Humanities Lab (DHLab):","The DHLab is a part of Digital Scholarship Services within Yale University Library, a group which also includes the StatLab and support for Research Data.","Beginner","Free","http://dhlab.yale.edu/#","http://dhlab.yale.edu/resources/workshops.html","- Intro to Network Analysis with Gephi\n- Introduction to Data visualization with Tableau\n- Maps, Graphs, & Data Dashboards with Tableau Public\n- Introduction to Machine Learning\n- Introduction to Programming with Python"]

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["Course #","Dept./Organization","Level","Course Title","Instructor","Course description"]
["AMTH 361 \nSection 01, CRN 21652","Applied Math","Intermediate","Data Analysis","William Brinda","Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering."]
["AMTH 663\nSection 01, CRN 22339","Applied Math","Intermediate","Deep Learning Theory and Applications","Smita Krishnaswamy","This course provides a principled and hands-on approach to deep learning with neural networks. Students master the principles and practices underlying neural networks, including modern methods of deep learning, and apply deep learning methods to real-world problems including image recognition, natural language processing, and biomedical applications. "]
["AMTH 745\nSection 01, CRN 20479","Applied Math","Advanced","Advanced Topics in Machine Learning and Data Mining","Smita Krishnaswamy\nGuy Wolf","An overview of advances in the past decade in machine learning and automatic data-mining approaches for dealing with the broad scope of modern data-analysis challenges, including deep learning, kernel methods, dictionary learning, and bag of words/features. The focus is on a broad scope of biomedical data-analysis tasks, such as single-cell RNA sequencing, single-cell signaling and proteomic analysis, health care assessment, and medical diagnosis and treatment recommendations."]
["BENG 249\nSection 01, CRN 22582","Biomedical Engineering","Beginner","Introduction to Biomedical Computation","Michael Mak","Computational and mathematical tools used in biomedical engineering for the simulation of biological systems and the analysis of biomedical data. Basics of computational programming in MATLAB; applications to modeling, design, and statistical and data analysis."]
["BENG 352\nSection 01, CRN 22586","Biomedical Engineering","Intermediate","Biomedical Signals and Images","James Duncan\nLawrence Staib","Principles and methods used to represent, model, and process signals and images arising from biomedical sources. Topics include continuous and discrete linear systems analysis, Fourier analysis and frequency response, metrics for signal similarity, and noise filtering. Biomedical examples range from one-dimensional electrical signals in nerves and muscles to two-dimensional images of organs and cells."]
["BENG 449\nSection 01, CRN 22595","Biomedical Engineering","Intermediate","Biomedical Data Analysis","Richard Carson","Study of biological and medical data analysis associated with applications of biomedical engineering. Provides basics of probability and statistics, as well as analytical approaches for determination of quantitative biological parameters from experimental data. Includes substantial programming in MATLAB."]
["BIS 505\nSection 01, CRN 23680","Biostatistics","Intermediate","Biostatistics in Public Health II","Daniel Zelterman","This continuation of EPH 505a covers multiple regression, analysis of variance, nonparametric tests, survival analysis, Poisson regression, and logistic regression. The course concludes with a review of commonly used statistical methods. R and SAS software is used for analysis of data."]
["BIS 557\nSection 01, CRN 13870","Biostatistics","Intermediate","Computational Statistics","Michael Kane","This is a course in the theory and practice of statistical computing. The goal of the course is to develop analytical and computational skills that will enable students to solve computational challenges in their own research. The course covers basic mathematical and statistical techniques that statisticians use when analyzing data and models for which there is no ready-made software. Every component of the course covers theoretical concepts, implementation details, and applications to real data or common statistical models that students will encounter in practice. "]
["BIS 567\nSection 01, CRN 13871","Biostatistics","Intermediate","Bayesian Statistics","Joshua Warren","Bayesian inference is a method of statistical inference in which prior beliefs for model parameters can be incorporated into an analysis and updated once data are observed. This course is designed to provide an introduction to basic aspects of Bayesian data analysis including conceptual and computational methods. Broad major topics include Bayes’s theorem, prior distributions, posterior distributions, predictive distributions, and Markov chain Monte Carlo sampling methods. "]
["BIS 621\nSection 01, CRN 13873","Biostatistics","Advanced","Regression Models for Public Health","Elizabeth Claus","The class explores advanced topics within these domains including the analysis of (1) blocked and nested study designs, (2) linear contrasts and multiple comparisons, (3) longitudinal data or repeated measures, (4) missing data, and (5) pragmatic clinical trials using propensity scores to reduce selection bias, etc. SAS software is used for analysis of data."]
["BIS 623\nSection 01, CRN 13874","Biostatistics","Advanced","Applied Regression Analysis","Elizabeth Claus","This course provides a focused examination of the theory and application behind linear regression. Topics include linear regression, estimation, hypothesis testing, regression diagnostics, analysis of variance, adjusting for covariates, transformations, missing data, and generalized linear models. R and SAS software is used for analysis of data."]
["BIS 625\nSection 01, CRN 23197","Biostatistics","Advanced","Categorical Data Analysis","Zuoheng Wang","This course presents methods for analyzing categorical data in public health, epidemiology, and medicine. Topics include discrete distributions, contingency tables, log-linear models, and logistic regression. Emphasis is on the theory and application of the methods and the interpretation of results by applying the techniques to a variety of data sets."]
["BIS 628\nSection 01, CRN 23198","Biostatistics","Advanced","Longitudinal and Multilevel Data Analysis","Haiqun Lin","The course teaches the common analytic techniques that can be used to analyze both longitudinal data and multilevel data with both continuous and discrete responses."]
["BIS 639\nSection 01, CRN 23200","Biostatistics","Advanced","Descriptive Analysis of Public Health Data","Theodore Holford","This course introduces methodology for extracting disease rates from public sources and using them to analyze temporal-spatial trends for disease. It also uses survey data on exposure to putative risk factors and results from analytical studies to quantify the extent that known etiology can account for disease trends."]
["BIS 679\nSection 01, CRN 13876","Biostatistics","Advanced","Advanced Statistical Programming in SAS and R","Denise Esserman","This class offers students the chance to build on basic SAS and R programming skills. Half of the term is spent working with SAS learning how to create arrays, format data, merge and subset data from multiple sources, transpose data, and write and work with macros. The second half of the term is spent working with R learning how to work with data, program functions, write simulation code using loops, and bootstrap."]
["BIS 692\nSection 01, CRN 23604","Biostatistics","Advanced","Statistical Methods in Computational Biology","Hongyu Zhao","Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics. We discuss statistical issues arising in analyzing population genetics data, gene expression microarray data, next-generation sequencing data, microbiome data, and network data. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and methods of classification and clustering; models include hidden Markov models, Bayesian networks, and graphical models."]
["CB&B 750\nSection 01, CRN 21882","Computational Biology and Bioinformatics","Intermediate","Core Topics in Biomedical Informatics","Kei-Hoi Cheung\nCynthia Brandt","introduction to common unifying themes that serve as the foundation for different areas of biomedical informatics, including clinical, neuro-, and genome informatics. The course is designed for students with significant computer experience and course work who plan to build databases and computational tools for use in biomedical research. "]
["CB&B 752\nSection 01, CRN 21244","Computational Biology and Bioinformatics","Intermediate","Biomedical Data Science: Mining and Modeling","Mark Gerstein","This course covers sequence alignment, large-scale processing, next-generation sequencing data, comparative genomics, phylogenetics, biological database design, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, normalization of microarray data, mining of functional genomics data sets, and machine-learning approaches to data integration."]
["CDE 534\nSection 01, CRN 23611","Epidemiology","Advanced","Applied Analytic Methods in Epidemiology","Mayur Desai","This computer lab-based course provides students with a comprehensive overview of data management and data analysis techniques."]
["CPSC 366\nSection 01, CRN 23225","Computer Science","Intermediate","Intensive Algorithms","Yang Cai","Mathematically sophisticated treatment of the design and analysis of algorithms and the theory of NP completeness. "]
["CPSC 446\nSection 01, CRN 23228","Computer Science","Intermediate","Data and Information Visualization","Holly Rushmeier","This course provides an introduction to the concepts needed to build new visualization systems, rather than to use existing visualization software. Major topics are abstracting visualization tasks, using visual channels, spatial arrangements of data, navigation in visualization systems, using multiple views, and filtering and aggregating data"]
["E&EB 713\nSection 01, CRN 21984","Ecology and Evolutionary Biology","Intermediate","Concepts and Methods in Global Biodiversity Change Research in the Age of Big Data","Walter Jetz","The course combines in-depth discussion of recent empirical work and hands-on examples of biodiversity change analysis workflows. On the technical front, we explore the use of remote sensing though Google Earth Engine (GEE) and work through R-based scripted examples of species distribution and community change modeling. "]
["EHS 562\nSection 01, CRN 23624","Environmental Health Sciences ","Intermediate","Applications in Systems Biology in Public Health","Caroline Johnson","This course focuses on understanding the different types of -omic tools that can be used within epidemiology, including those listed, as well as newer approaches such as exposome and microbiome analysis. "]
["EMD 538\nSection 01, CRN 13951","Epidemiology of Microbial Diseases","Intermediate","Quantitative Methods for Infectious Disease Epidemiology","Virginia Pitzer","This course provides an overview of statistical and analytical methods that apply specifically to infectious diseases. "]
["F&ES 603\nSection 01, CRN 24659","Forestry & Environmental Studies","Intermediate","Environmental Data Visualization and Communication","Jennifer Marlon\nSimon Queenborough","This course teaches how to effectively explore and communicate insights from data using visualization as a powerful means of enhancing our cognitive abilities to learn from data"]
["F&ES 611\nSection 01, CRN 12598","Forestry & Environmental Studies","Intermediate","Data Science for Social Research: An Introduction","Justin Farrell","Students are introduced to various techniques and software for collecting, cleaning, and analyzing data at large scales, especially text data (e.g., machine learning, topic modeling, location extraction, semantic networks). "]
["F&ES 720\nSection 01, CRN 12754","Forestry & Environmental Studies","Intermediate","Introduction to R","Simon Queenborough","This seminar provides an overview and introduction to the statistical software R for the analysis and graphical presentation of natural and social science data. "]
["F&ES 753\nSection 01, CRN 12760","Forestry & Environmental Studies","Intermediate","Regression Modeling of Ecological and Environmental Data","Timothy Gregoire","This course in applied statistics assists scientific researchers in the analysis and interpretation of observational and field data. "]
["F&ES 758\nSection 01, CRN 23304","Forestry & Environmental Studies","Intermediate","Multivariate Data Analysis in the Environmental Sciences","Jonathan Reuning-Scherer","Topics include multivariate analysis of variance (MANOVA), principal components analysis, cluster analysis (hierarchical clustering, k-means), canonical correlation, multidimensional scaling ordination methods, discriminate analysis, factor analysis, and structural equations modeling. "]
["F&ES 781\nSection 01, CRN 23310","Forestry & Environmental Studies","Advanced","Applied Spatial Statistics","Timothy Gregoire","An introduction to spatial statistical techniques with computer applications. Topics include modeling spatially correlated data, quantifying spatial association and autocorrelation, interpolation methods, variograms, kriging, and spatial point patterns. "]
["INP 558\nSection 01, CRN 21550","Interdepartmental Neuroscience Program","Intermediate","Computational Methods in Human Neuroscience","Nicholas Turk-Browne","This course provides training on how to use computational science for the advanced analysis of brain imaging data, primarily from functional magnetic resonance imaging (fMRI). "]
["MGT 510\nSection 01, CRN 22939","Management","Intermediate","Data Analysis&Causal Inference","Robert Jensen","Introduction to data analytics and causal inference."]
["MGT 672\nSection 01, CRN 21026","Management","Intermediate","Practicum in Data Analysis Using Stata","Justin Thomas","This course provides students with practical hands-on instruction in the analysis of survey data using the statistical package Stata. It serves as a bridge between the theory of statistics/econometrics and the practice of social science research. "]
["S&DS 100\nSection 01, CRN 21642","Statistics & Data Science","Beginner","Introductory Statistics","William Brinda","An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems."]
["S&DS 220\nSection 01, CRN 21643","Statistics & Data Science","Beginner","Introductory Statistics, Intensive","Xiaofei Wang","Introduction to statistical reasoning for students with particular interest in data science and computing. Using the R language, topics include exploratory data analysis, probability, hypothesis testing, confidence intervals, regression, statistical modeling, and simulation. "]
["S&DS 523\nSection 01, CRN 22334","Statistics & Data Science","Beginner","YData: An Introduction to Data Science","Jessica Cisewski\nJohn Lafferty","This course aims to dramatically enhance students’ knowledge and capabilities in fundamental ideas and skills in data science, especially computational and programming skills and inferential thinking. "]
["S&DS 530\nSection 02, CRN 22134","Statistics & Data Science","Intermediate","Data Exploration and Analysis","Jonathan Reuning-Scherer","Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used."]
["S&DS 565\nSection 01, CRN 22139","Statistics & Data Science","Intermediate","Applied Data Mining and Machine Learning","Derek Feng","Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. "]
["S&DS 571\nSection 01, CRN 23944","Statistics & Data Science","Intermediate","YData: Text Data Science: An Introduction","John Lafferty","This course is an introduction to computational and inferential methods that use text. The focus is on simple but often powerful text-processing techniques that do not require linguistic analyses, to gain familiarity with working with text data. "]

{"sheet":"Non-Yale courses","index":2,"n_rows":8,"n_cols":6,"rows_kept":8,"cut":false,"hidden":false,"merged":[]}
["Organization","Key offerings","Level","Cost","URL","Course examples"]
["Carpentries (In-person)","Software, data and library carpentry workshops","Beginner-intermediate","Workshop Administration Fee (Government and non-profit institutions): $2500\nTravel Expenses for Instructors: $2000 (estimate) ","https://carpentries.org/workshops/   ","- Software Carpentry workshops are domain-agnostic, and teach the Unix shell, coding in R or Python, and version control using Git.\n- Data Carpentry workshops are domain-specific, and focus on teaching skills for working with data effectively and reproducibly."]
["Code Camp (online)","Thousands of coding lessons to help improve programming skills","Beginner-Advanced","Free","https://learn.freecodecamp.org/ ","- Responsive Web Design Certification (300 hours)\n- Javascript Algorithms And Data Structures Certification (300 hours)\n- Front End Libraries Certification (300 hours)\n- Data Visualization Certification (300 hours)\n- Apis And Microservices Certification (300 hours)\n- Information Security And Quality Assurance Certification (300 hours)\n- Coding Interview Prep (Thousands of hours of challenges)"]
["Coursera (online)","Provides universal access to online education, partnering with top universities and organizations, including Yale, Stanford, JHU, etc.","Beginner-intermediate","Free","https://www.coursera.org/courses ","- Big data, genes, and medicine, \n- Big data science with the BD2K-LINCS Data Coordination and Integration Center (DCIC) "]
["DataOne (online and in-person)","Teaches data stewardship, including outlining best practices for data management, providing educational materials for use by those that support researchers, and linking to tools and resources available from DataONE and its partners.","Beginner","Free","https://www.dataone.org/education","- Data Management Modules \n- Webinars: Make Data Count; The Challenges of Reproducibility In Data Scarce Fields; Reproducible Research: Better science in less time\n- Screencast tutorials on DataONE tools and services\n- Data Stories\n- Education EVAluation: create a survey instrument for the evaluation of education and training resources and activities"]
["Esip Data Management Training","The Data Management Training (DMT) Clearinghouse is a registry for online learning resources focusing on research data management.  ","Beginner-intermediate","Free","http://dmtclearinghouse.esipfed.org/ ","- Local data management\n- Creating metadata\n- USGS Data Management Training Modules—Metadata for Research Data\n- Data Management Plans\n- Providing access to data"]
["Lynda (online)","LinkedIn Online Learning on software, creative, and business skills","Beginner-intermediate","Free through Yale portal","https://your.yale.edu/yale-link/lyndacom-online-software-training ","- The Data Science of Healthcare, Medicine, and Public Health, with Barton Poulson\n- Designing Big Data Healthcare Studies"]
["Rstudio ","Tutorials, articles, and examples exist to help you learn R and its extensions. ","Beginner-advanced","Free on open source products","https://www.rstudio.com/online-learning/   ","- R Programming\n- R Markdown\n- Cleaning data in R \n- Data Manipulation in R \n- Joining Data in R\n- Data Visualization in R "]