Some links are to ZIP files containing workshop materials. Be sure to also check out our collection of articles on statistics and statistical software as well.
Spring 2024
- Parallel Computing in R (Jacob Goldstein-Greenwood)
- Fundamentals of Package Development in R (Jacob Goldstein-Greenwood)
- Power and Sample Size Analysis (Clay Ford)
- Bayesian Data Analysis, Part 1 (Clay Ford)
- Bayesian Data Analysis, Part 2 (Clay Ford)
- Intro to Python (Erich Purpur)
- Python Data Analysis and Visualization (Erich Purpur)
- Python Geospatial Data and Mapping (Erich Purpur)
- Intro to Version Control with Git + Github (Erich Purpur)
- Preparing Datasets for Publishing (Michael Lenard)
- Reproducible Analysis and Documentation with R and R Markdown/Quarto (Michael Lenard)
- Getting Started with R (Jenn Huck)
Fall 2023
- Getting Started with R (Jenn Huck)
- Data Wrangling Techniques in R: Beyond the Basics (Jacob Goldstein-Greenwood)
- Fundamentals of Package Development in R (Jacob Goldstein-Greenwood)
- Parallel Computing in R (Jacob Goldstein-Greenwood)
- Linear Modeling in R (Clay Ford)
- Linear Mixed-Effect Modeling in R (Clay Ford)
- Binary Logistic Regression in R (Clay Ford)
- Survival Analysis Modeling in R (Clay Ford)
- Intro to Python (Erich Purpur)
- Intro to Version Control w/ Git + Github (Erich Purpur)
- Python Data Analysis + Visualization (Erich Purpur)
- Intro to Regular Expressions (Erich Purpur)
- Python and APIs (Erich Purpur)
- Python Geospatial Data and Mapping (Erich Purpur)
- Python Web Dashboards w/ Streamlit (Erich Purpur)
- Introduction to the Command Line (Ricky Patterson)
- Intro to Overleaf for LaTeX (Ricky Patterson)
- Organizing Transparent and Reproducible Research (Jenn Huck)
- Data Visualization Using Tableau - Part 1 (Nancy Kechner)
- Data Visualization Using Tableau - Part 2 (Nancy Kechner)
- BibLaTeX for your Dissertation (Ricky Patterson)
- Overleaf/LaTeX for Tables and Figures (Ricky Patterson)
- Introduction to Qualitative Analysis Principles (Christine Slaughter)
- Intro to Qualitative Analysis Using Dedoose (Christine Slaughter)
- Reproducible Analysis and Documentation with RStudio and R Markdown/Quarto (Jenn Huck)
- Intro to Qualitative Analysis Using NVivo (Christine Slaughter)
Spring 2023
- Data Management for Humanists (Jenn Huck)
- Power and Sample Size Analysis in R (Clay Ford)
- Count Modeling in R (Clay Ford)
- Data Visualization in R with ggplot2 (Jacob Goldstein-Greenwood)
- Data Wrangling in R with dplyr and tidyr (Jacob Goldstein-Greenwood)
- Fundamentals of Package Development in R (Jacob Goldstein-Greenwood)
- Intro to Python (Erich Purpur)
- Intro to Version Control w/ Git + Github (Erich Purpur)
- Python Data Analysis + Visualization (Erich Purpur)
- Intro to Regular Expressions (Erich Purpur)
- Python and APIs (Erich Purpur)
- Python Geospatial Data and Mapping (Erich Purpur)
- Python Web Scraping (Erich Purpur)
- Python Sentiment Analysis and Natural Language Processing (Erich Purpur)
Fall 2022
- Getting Started with R (Jenn Huck)
- Data Wrangling in R with dplyr and tidyr (Jacob Goldstein-Greenwood)
- Linear Modeling in R (Clay Ford)
- Binary Logistic Regression in R (Clay Ford)
- Ordinal Logistic Regression in R (Clay Ford)
- Linear Mixed-Effect Modeling in R (Clay Ford)
- Fundamentals of Package Development in R (Jacob Goldstein-Greenwood)
- Intro to Shiny Apps (Christina Gancayco)
- Customizing Shiny Apps (Christina Gancayco)
- Intro to Python (Erich Purpur)
- Python Data Analysis + Visualization with Pandas and Matplotlib (Erich Purpur)
- Python and APIs (Erich Purpur)
- Geospatial Data and Mapping in Python (Erich Purpur)
- Python Web Scraping (Erich Purpur)
- Rivanna from the Command Line (Ahmad Sheikhzada & Gladys Andino)
- Intro to Multicore Processing (Jackie Huband)
- Parellel MatLab on Rivanna (Ed Hall)
- Intro to Data Visualization Using Tableau (Nancy Kechner)
- Creating Data Visualization Using Tableau (Intermediate)(Nancy Kechner)
- Intro to Qualitative Analysis Using Dedoose (Christine Slaughter)
- Intro to Qualitative Analysis Using NVivo (Christine Slaughter)
- Intro to Zotero (Maggie Nunley)
- Endnote vs. Zotero (Jenny Coffman & Maggie Nunley)
- Intro to Regular Expressions (Erich Purpur)
- Version Control with Git/GitHub (Erich Purpur)
- Organizing Transparent and Reproducible Research (Jenn Huck)
Spring 2022
- Intro to the Command Line (Ricky Patterson)
- Intro to Zotero (Maggie Nunley)
- Getting Oriented with the New SciFinder-n (Jenny Coffman)
- Intro to Endnote (Jenny Coffman)
- LaTeX for Figures and Tables (Ricky Patterson)
- BibLaTeX: Bibliographies in LaTeX (Ricky Patterson)
- Introduction to Regular Expressions (Erich Purpur)
- Intro to Python (Erich Purpur)
- Data Analysis and Visualization in Python with Pandas and Matplotlib (Erich Purpur)
- Scientific Image Processing with Python (Karsten Siller)
- Plotting with Matplotlib (Will Rosenow)
- Python and APIs (Erich Purpur)
- Buiding lists with .append() (Will Rosenow)
- Python and Web Scraping (Erich Purpur)
- Text Parsing/Regular Expressions (Will Rosenow)
- Error Handling with try and except (Will Rosenow)
- Introduction to Qualitative Analysis Using Dedoose (Christine Slaughter)
- Introduction to Qualitative Analysis Using NVIVO (Christine Slaughter)
- Getting Started with R (Jennifer Huck)
- Linear Modeling with R (Clay Ford)
- Mixed-Effect/Multilevel Modeling with R (Clay Ford)
- Bayesian Data Analysis, Part I (Clay Ford)
- Bayesian Data Analysis, Part II (Clay Ford)
- Plot annotations with ggplot2 (Marieke Jones)
- Mathematical Annotation in R (Clay Ford)
- Working with Dates in R (Clay Ford)
- Data Cleaning with tidyverse (Will Rosenow/Jacalyn Huband)
- across() function (Marieke Jones)
- purrr map() function (Marieke Jones)
- Rolling joins (Clay Ford)
- Reproducible Analysis and Documentation with R Studio and R Markdown (Jennifer Huck)
- Organize for Transparent and Reproducible Research (Jennifer Huck)
- Sharing Your Data for Transparent and Reproducible Research (Sherry Lake)
- Version Control with Git and GitHub (Erich Purpur)
- Introduction to Data Visualization: Using Tableau (Nancy Kechner)
- Exploring Data Visualizations Using Tableau (Nancy Kechner)
- Parallel/GPU Computing with Matlab (Ed Hall)
- Converting Jupyter Notebooks to run as a Batch Job (Jacalyn Huband)
- Optimization Techniques with Matlab (Ed Hall)
- Using Spark on Rivanna (Ruoshi Sun)
Fall 2021
- Organizing for Transparent/Reproducible Research (Jenn Huck)
- Version Control with GitHub (Erich Purpur)
- Reproducible Analysis and Documentation with R and RStudio (Jenn Huck)
- Sharing Your Data (Sherry Lake)
- Getting Started with R (Clay Ford)
- Getting Started with R (Jenn Huck)
- Data Wrangling Strategies with R (Clay Ford)
- Basic Statistics Refresher with R (Clay Ford)
- Power and Sample Size Analysis in R (Clay Ford)
- Introduction to Multicore Processing with R (Jacalyn Huband)
- Introduction to Python (Erich Purpur)
- Data Analysis and Visualization in Python with Matplotlib and Pandas (Erich Purpur)
- Python and APIs (Erich Purpur)
- Python Web Scraping (Erich Purpur)
- Sentiment Analysis with Python (Jacalyn Huband)
- Scientific Image Processing with Python (Karsten Siller)
- Using Rivanna from the Command Line (Gladys Andino)
- Introduction to Data Visualization Using Tableau (Nancy Kechner)
- Exploring Data Visualization Using Tableau (Nancy Kechner)
- Using Qualtrics to Create Useful Surveys (Nancy Kechner)
- Introduction to Qualitative Analysis Using Dedoose (Christine Slaughter)
- Introduction to Qualitative Analysis Using NVivo (Christine Slaughter)
- Introduction to Zotero (Maggie Nunley)
- Introduction to LaTeX and Overleaf (Ricky Patterson)
- An Introduction to ORCID (Winston Barham)
- Funding Discovery Tools (Ricky Patterson)
- Parallel Processing with Matlab (Ed Hall)
- Scientific Image Processing with Matlab (Ed Hall)
- Scientific Computing with Julia on Rivanna (Ed Hall)
Spring 2021
- Organizing for Transparent/Reproducible Research (Jenn Huck)
- Version Control with GitHub (Erich Purpur)
- Reproducible Analysis and Documentation with R and RStudio (Jenn Huck)
- Sharing Your Data (Sherry Lake)
- Introduction to R/RStudio (Jenn Huck)
- Data Wrangling with dplyr (Jenn Huck)
- Data Visualization with ggplot2 (Clay Ford)
- Introductory Statistics with R (Clay Ford)
- Parallelizing R (Jacalyn Huband)
- R with MPI (Jacalyn Huband)
- Introduction to Shiny (Christina Gancayco)
- Customizing Shiny Apps (Christina Gancayco)
- Introduction to Python (Erich Purpur)
- Data Visualization in Python with Matplotlib and Pandas (Erich Purpur)
- Parallel Matlab (Ed Hall)
- Optimizing Matlab Code (Ed Hall)
- Building Containers for Rivanna (Ruoshi Sun)
- Minimal Containers (Ruoshi Sun)
- Introduction to Data Visualization with Tableau (Nancy Kechner)
- Creating Data Visualizations Using Tableau (Nancy Kechner)
- Using Dedoose for Qualitative Research (Christine Slaughter)
- Using NVivo for Qualitative Data (Christine Slaughter)
- Introduction to Zotero (Maggie Nunley)
- Introduction to LaTeX and Overleaf (Ricky Patterson)
Fall 2020
- Data Preparation in R with dplyr (Jenn Huck)
- Data Visualization in R with ggplot2 (Clay Ford)
- Data Visualization in Python with Matplotlib and Pandas (Erich Purpur)
- Scientific Writing with LaTeX/Overleaf – Figures & Tables (Ricky Patterson)
- Using Zotero for Research (Maggie Nunley)
- Using Dedoose for Qualitative Data (Christine Slaughter)
Spring 2020
- Introduction to R/RStudio (Jenn Huck)
- Data Wrangling in R (Clay Ford)
- Data Visualization in R (Clay Ford)
- Introductory Statistics with R (Clay Ford)
- Linear Modeling with R (Clay Ford)
- Modeling Count Data with R (Clay Ford)
- Introduction to Python (Erich Purpur)
- Using APIs with Python (Erich Purpur)
- Data Visualization with Matplotlib (Erich Purpur)
- Web Scraping with Python (Erich Purpur)
- Funding Discovery Tools (Ricky Patterson)
- Introduction to the Command Line/Unix (Ricky Patterson)
- Writing Data Management Plans with the DMPTool (Bill Corey)
- Introduction to Git/GitHub (Ricky Patterson)
- Getting Started with LaTeX and Overleaf (Ricky Patterson)
- Introduction to Zotero (Maggie Nunley)
- Advanced Zotero (Jeremy Garritano)
- Qualitative Data Analysis (Christine Slaughter)
- Introduction to Data Visualization and Tableau (Nancy Kechner)
- Data Visualization with Tableau II (Nancy Kechner)
Fall 2019
- Introduction to R (Clay Ford)
- Data Wrangling in R (Clay Ford)
- Data Visualization in R (Clay Ford)
- Bayesian Data Analysis in R I (Clay Ford)
- Bayesian Data Analysis in R II (Clay Ford)
- Introduction to R (Jenn Huck)
- Census Data Basics (Jenn Huck)
- Working with tidycensus in R (Jenn Huck)
- Introduction to Python (Erich Purpur)
- Acquiring API Data in Python (Erich Purpur)
- Data Visualization in Python (Erich Purpur)
- Introduction to the Command Line/Unix Shell (Ricky Patterson)
- Introduction to Git, GitHub, and GitLab (Ricky Patterson)
- Research Data Management Practices (Bill Corey)
- Using LaTeX and Overleaf (Ricky Patterson)
- Making the Most of Funding Discovery Tools (Ricky Patterson)
- Introduction to QGIS (Erich Purpur, additional materials)
- Qualitative Data Analysis and Introduction to Dedoose (Christine Slaughter)
- LibraData and Dataverse: UVA’s Data Sharing Repository (Sherry Lake)
- Basics of Data Visualization and Tableau (Nancy Kechner)
- Data Visualization and Tableau II (Nancy Kechner)
- Building Surveys in Qualtrics (Nancy Kechner)
- Working with Data in Excel (Nancy Kechner)
- Introduction to Zotero for Citation Management (Maggie Nunley)
- Using Advanced Features of Zotero (Jeremy Garritano)
- MS Word for Theses/Long Documents (Christine Slaughter)
Spring 2019
- Introduction to R (Jenn Huck)
- Python: Introduction to Python (Erich Purpur)
- Data Preparation/Tidy Data in R (Michele Claibourn)
- Introduction to the Command Line (Aycan Katitas)
- Qualitative Data Analysis + Intro to Dedoose (Christine Slaughter)
- Intro to QGIS (Erich Purpur)
- (Exploratory) Data Visualization in R (Clay Ford)
- Introduction to Data Visualization with Tableau Part 1 (Nancy Kechner)
- Introduction to Data Visualization with Tableau Part 2 (Nancy Kechner)
- Python: Introduction to Python (Pete Alonzi)
- Census Basics (Jenn Huck)
- Visualizing Models, Communicating Results in R (Clay Ford)
- Python: Data Preparation in Python (Pete Alonzi)
- Interactive Web Apps in R with shiny (Clay Ford)
- Introduction to Git and GitHub (Ricky Patterson)
- Python: Machine Learning in Python (Pete Alonzi)
- Research Data Management Fundamentals (Bill Corey)
- Funding Discovery Tools (Ricky Patterson)
- Advanced Zotero (Jeremy Garritano)
- Introduction to LaTeX/Overleaf (Ricky Patterson)
- MS Word for Theses and Long Documents (Christine Slaughter)
- Introduction to Zotero (Maggie Nunley)
- Data in Excel (Nancy Kechner)
- Qualtrics for Survey Research (Nancy Kechner)
Fall 2018
- Introduction to R (Jenn Huck)
- Data Wrangling with R, Part I (Clay Ford)
- Data Wrangling with R, Part II (Clay Ford)
- Data Wrangling with R, Part III (Clay Ford)
- Visualization in R with ggplot2 (Clay Ford)
- Getting Started with Bayesian Data Analysis in R (Clay Ford)
- Text Analysis with R, Part I (Michele Claibourn)
- Text Analysis with R, Part II (Michele Claibourn)
- Text Analysis with R, Part III (Michele Claibourn)
- Introduction to QGIS (Erich Purpur)
- Unix: Introduction to the Command Line (Ricky Patterson)
- Census Basics (Jenn Huck)
- Principles of Data Visualization and Tableau, Part I (Nancy Kechner)
- Data Visualization Using Tableau, Part II (Nancy Kechner)
- Data in Excel (Nancy Kechner)
- Introduction to Git/GitHub (Pete Alonzi)
- Introduction to Python (Erich Purpur)
- Introduction to LaTeX & Overleaf (Ricky Patterson)
- Funding Discovery Tools (Ricky Patterson)
- Publish or Perish! Research Metrics for Academics (Erich Purpur)
- UVaCollab for Research Collaborations Part I (Bill Corey)
- UVaCollab for Research Collaboration Part II (Bill Corey)
- Data Storage Best Practices (Bill Corey)
- Introduction to Python (Pete Alonzi)
- Introduction to Apache Spark and PySpark (Pete Alonzi)
- Introduction to Dedoose (Christine Slaughter)
- Qualitative Data Analysis (Christine Slaughter)
- Introduction to EndNote (Jeremy Garritano)
Spring 2018
- Introduction to Unix (Ricky Patterson)
- Data Sharing and Archiving for Engineering (Bill Corey, Erich Purpur)
- Introduction to Stata (Clay Ford)
- Introduction to R (Clay Ford)
- Working with Data in Excel (Nancy Kechner)
- Exploratory Factor Analysis (Clay Ford)
- Help! I need to use the Census (Jenn Huck)
- LaTeX & ShareLaTeX (Ricky Patterson)
- Linear Mixed Effects Modeling in R (Clay Ford)
- Confirmatory Factor Analysis (Clay Ford)
- Funding Discovery Tools (Ricky Patterson)
- Research Management and Reproducible Practices with the Open Science Framework (Ricky Patterson, Sherry Lake)
- Introduction to Python (Pete Alonzi)
- Text Analysis in R: Quanteda (Michele Claibourn)
- Introduction to Git/GitHub (Pete Alonzi)
- Introduction to Qualtrics (Nancy Kechner)
- Data Visualization with Tableau (Nancy Kechner)
- Introduction to QGIS (Erich Purpur)
- Introduction to Dedoose (Nancy Kechner)
Fall 2017
- Introduction to Python (Pete Alonzi)
- Introduction to Unix (Ricky Patterson)
- Introduction to GitHub (Pete Alonzi)
- Data in Excel (Nancy Kechner)
- Introduction to R (Clay Ford)
- LaTeX & ShareLaTeX (Ricky Patterson)
- Introduction to Dedoose (Nancy Kechner)
- Interactive Visualization with Python/Bokeh (Pete Alonzi)
- Visualization in R/ggplot2 (Clay Ford)
- Power and Sample Size Analysis (Clay Ford)
- Websites with GitHub and Jekyll (Doug Chestnut)
- Introduction to ORCID (Ricky Patterson)
- Machine learning with Python-scikit (Pete Alonzi)
- Funding Discovery Tools (Ricky Patterson)
- Visualization with Tableau (Nancy Kechner)
Spring 2017
- Intro to R (Clay Ford)
- Intro to Python (Pete Alonzi)
- Web Scraping in R with rvest, workshop (Clay Ford)
- Web Scraping in R with rvest, Endangered Data Week presentation (Clay Ford)
- Introduction to Unix (Ricky Patterson)
- Web Scraping in Python (Eric Rochester)
- Sentiment Analysis in R (Michele Claibourn)
- Character Manipulation in R (Clay Ford)
- Topic Modeling in R (Michele Claibourn)
- Text Classification in Python (Pete Alonzi)
- Survival Analysis in Stata (Alex Jakubow)
- Text Classification in R (Michele Claibourn)
- Introduction to R Markdown (Clay Ford)
- Introduction to ShareLaTeX for collaborative LaTeX (Ricky Patterson)
- Introduction to Dedoose (Nancy Kechner)
- Intro to Git/Github (Pete Alonzi)
Fall 2016
- Intro to R (Clay Ford)
- Intro to Stata (Michele Claibourn)
- Linear Modeling with R (Clay Ford)
- Introductory Categorical Data Analysis in R (Clay Ford)
- Intro to LaTeX (Ricky Patterson)
- Visualization in R with ggplot2 (Clay Ford)
- Intro to Python, Part 1 (Pete Alonzi)
- Creating Tables and Figures with LaTeX (Ricky Patterson)
- Intro to Python, Part 2 (Pete Alonzi)
- Power and Sample Size Analysis (Clay Ford)
- Visualizing Model Effects (Clay Ford)
- Visualization in Python (Pete Alonzi)
- Creating and Managing Bibliographies in LaTeX (Ricky Patterson)
- Funding Discovery Workshop (Ricky Patterson)
Spring 2016
- Intro to R (Bommae Kim)
- Topic Modeling in R (Michele Claibourn)
- Intro to LateX (Chelsea Goforth)
- Intro to Machine Learning, with Support Vector Machines (Bommae Kim)
- Matching Methods for Causal Inference (Michele Claibourn)
- Intro to Social Network Analysis (Yun Tai)
- Intro to Machine Learning, Classification and Regression Trees (Clay Ford)
- Power and Sample Size Analysis (Clay Ford)
- Exploratory Factor Analysis (using Stata) (Chelsea Goforth)
- Geospatial and Census Data in R (Yun Tai)
- Intro to Python (Pete Alonzi)
- Visualization in Python with matplotlib (Pete Alonzi)
- Using Python with Web APIs (Pete Alonzi)
- Version Control with Git (Pete Alonzi)
Fall 2015
- Intro to SAS (Clay Ford)
- Intro to R (Yun Tai)
- Intro to Stata (Chelsea Goforth)
- Getting Started with SPSS Syntax (Michele Claibourn)
- Visualization in R with ggplot2 (Clay Ford)
- Survey Data Analysis in Stata (Chelsea Goforth)
- Web Scraping, Twitter Data, and Text Data (Yun Tai)
- Linear Mixed-Effect Modeling in R (Clay Ford)
- Visualization in Python with matplotlib (Pete Alonzi)
- Multiple Imputation for Missing Data (Michele Claibourn)
Spring 2015
- Selecting a Statistical Model (Caitlin Steiner)
- Intro to the UNIX Command Line (Pete Alonzi)
- Intro to Graphics in Stata (Chelsea Goforth)
- Data Analysis with SPSS for Thesis Writers (Michael Hull)
- Typesetting a Document in LaTeX (Pete Alonzi)
- Classification and Regression Trees (Clay Ford)
- Social Network Analysis for Beginners (Yun Tai)
- Intro to ggplot2 (Caitlin Steiner)
- Logit and its GLM Friends (using Stata) (Chelsea Goforth)
- Principal Component Analysis and Exploratory Factor Analysis (Michael Hull)
- Version Control with Git (Pete Alonzi)
- Creating a Data Management Plan (Sherry Lake)
- Introduction to Database Design (Sherry Lake)
- Building Databases and Querying with MySQL (Sherry Lake)
Fall 2014
- Introduction to Stata (Clay Ford)
- Introduction to Stata (Michele Claibourn)
- Introduction to SPSS (Siny Tsang)
- Introduction to R (Caitlin Steiner)
- Introduction to R (Clay Ford)
- Linear Modeling in R (Clay Ford)
- Writing Articles in LaTeX (Caitlin Steiner)
- Matching Methods for Causal Inference (Michele Claibourn)
- Resampling Methods (Clay Ford)
- Creating Presentations in LaTeX (Caitlin Steiner)
- Introduction to Structural Equation Modeling (SEM) (Siny Tsang)
- Getting Started with R Graphics (Clay Ford)
- Introduction to Database Design (Sherry Lake)
- Building and Using MS Access Databases (Sherry Lake)
- Creating a Data Management Plan (Bill Corey)
- Creating & Querying SQL & MySQL Databases (Sherry Lake)
- Data Documentation and Metadata (Sherry Lake, Anne Gaynor)
- Best Practices for Data Management (Ricky Patterson, Andrea Denton)
- Creating a DMP (Sherry Lake)
- Collaboration Management Tools (Bill Corey)
- Decennial Census: Finding and Accessing Data (Summer Durrant)
- Data Preservation (Bill Corey, Kara McClurken)
- Preserving and Sharing Data: Best Practices & Requirements for Selecting a Data Sharing Repository (Bill Corey)
Spring 2014
- LaTeX, Part 1 and Part 2 (Caitlin Steiner)
- Graphics in Stata (Brenton Peterson)
- Introduction to R (Siny Tsang)
- Getting Started with R Graphics (Clay Ford)
- Regression Discontinuity (in Stata) (Brenton Peterson)
- Text Mining with R (Clay Ford)
- Cluster Analysis (in R) (Michele Claibourn)
- Spatial Analysis (in R), Part 1 and Part 2 (Adam Slez, Sociology)
- For MICR 8380: Introduction to R, Part 1 (Michele Claibourn) and Part 2 (Clay Ford)
- For CS 6014: Computation as a Research Tool: Data Processing in R (Clay Ford) and Multiple Imputation in R (Michele Claibourn)
- Introduction to Designing and Building Databases (Sherry Lake, Nancy Kechner)
- Documentation and Metadata (Anne Gaynor, Sherry Lake)
- Data Management for Graduate Students I: Why Should You Care about Managing Your Research? (Sherry Lake, Bill Corey, Purdom Lindblad)
- Data Management for Graduate Students II: Data Management for Humanists (Bill Corey, Sherry Lake, Purdom Lindblad)
- Finding and Acquiring Data (Bill Corey, Summer Durrant)
- Managing Collaborations (Bill Corey)
- Planning for Data Management (Ricky Patterson, Andrea Denton)
Fall 2013
- Introduction to SPSS (Michele Claibourn)
- Introduction to Stata (Michele Claibourn)
- Introduction to R (Clay Ford)
- Duration/Survival/Hazard Models (in Stata) (Michele Claibourn)
- Missing Data and Multiple Imputation (in Stata) (Michele Claibourn)
- Versioning (Sherry Lake, Bill Corey)
- Data Wrangling and Interoperability (Ricky Patterson, Andrea Denton)
- Choosing between data sharing repositories for Engineering (Sherry Lake)
- Choosing between data sharing repositories for the Humanities (Bill Corey)
- Choosing between data sharing repositories for the Life Sciences (Andrea Denton)
- Choosing between data sharing repositories for Social Sciences (Bill Corey)
- Best Practices for Collecting Data (Bill Corey, Andrea Denton)
- Data Management: Documentation and Metadata for Engineering and Physical Sciences (Ivey Glendon, Jeremy Bartczak)
- Introduction to Databases for Managing Research Data (Sherry Lake, Bill Corey)
- Workflow Systems for Life Sciences and Social Sciences (Bill Corey, Andrea Denton)
- Workflow Systems for Engineering and Physical Sciences (Andrew Sallans)
Summer 2013
- Multilevel Models I: Introductions, implementation, interpretation (in R and Stata) (Michele Claibourn)
- Multilevel Models II: Model assessment, estimation, and generalizations (in R and Stata) (Michele Claibourn)
- Matching Methods I: Logic, limitations, and algorithms for matching on covariates (in R) (Michele Claibourn)
- Matching Methods II: Propensity score approaches (in R) (Michele Claibourn)
- Data Management: Documentation and Metadata (Sherry Lake, Bill Corey, Jeremy Bartczak)
- Gaining an Advantage by Sharing Your Research Data (Andrew Sallans, Bill Corey)