### Past Workshops

See the current workshop calendar:

Workshop Calendar

**Descriptions**

**Introduction to SPSS**

SPSS is a flexible and user-friendly statistical software package known for its graphics, quick assessment tools and easy programming language. SPSS also works directly with Excel files. Widely used in all of the social sciences, SPSS offers add-ons which enable qualitative analysis, missing values analysis, and Survey design. This class offers a very basic introduction to the application GUI and coding mechanisms. Basic statistical understanding is expected but not necessary.

**Introduction to Stata **

Stata is a popular integrated statistical program used by academic researchers across campus, especially in economics, political science, and EPH. If you are a total to moderate rookie with Stata (i.e. have never used or only ever used "regress" for class) and want to learn more about importing, merging, and cleaning your data, this class is for you. We will cover the basics: getting around the program, do files, graphics and table generation.

**Introduction to R **

R is a free, open source development language for statistical computing and graphics. Because of its price and large development community, R is quickly becoming the statistical application of choice at Yale. R has add-ons for GIS, graphing, advanced statistics, econometrics, image analysis and more. This class offers an extremely basic introduction to the programming language and resources available. Basic statistical understanding is expected.

**Intermediate & Advanced R **

This class assumes basic knowledge of R and statistics. Topics include the various data types in R, reading in data, graphing, matrix manipulations and using and writing your own statistical functions.

**Parallel R using foreach**

This workshop well provide an overview of Parallel programming in R. Learn how to use the foreach package, a popular parallel programming package for R that allows you to execute your R script faster using multiple cores on your laptop and multiple nodes on an HPC cluster.

**Introduction to GIS: Mapping and ArcGIS Software**

This is an introduction to the basic concepts of creating, managing and analyzing explicitly spatial data within a Geographic Information Systems (GIS) framework. Included is a step-by-step, "hands on" introduction to using spatial data within ESRI's ArcGIS software. Topics will include: Spatial Data Models, Spatial Relationships, The ArcMap User Interface, Thematic Mapping Using Symbology, and Simple Analysis Using Complex Selection Methods.

**Intermediate GIS & ****Spatial Analysis**

This workshop will introduce students to a variety of tools for spatial analysis. Students will use the ArcGIS software suite to load, manipulate, analyze, and visualize data. We will primarily focus on strategies for working with pixelated raster data but will touch on vector analysis as well. Topics may include: data formats; coordinate systems and projections; symbology and 3D visualization of raster data; creating new datasets and surface models; spatially-explicit regression and map algebra; and image classification. Examples of GIS analysis using R and Google EarthEngine will be presented as time allows. Prerequisites: Prior exposure to ArcGIS is helpful but not required.

**Introduction to Qualtrics**

Qualtrics is an easy to use but very sophisticated online survey tool that is now available to students, staff and faculty at Yale. This workshop will introduce you to some of the more advanced design considerations and features of the software, including conditional branching, scoring, embedded data, implementation of longitudinal designs, and integration of Qualtrics with crowdsourcing tools like Amazon Mechanical Turk.

**Research Data Management **

This workshop will introduce researchers (from postdocs to undergrads) to the fundamentals of research data management. You’ll learn about the data life cycle: creating, processing, analyzing, preserving, giving access to, and re-using data. We’ll discuss how to identify the current best practices in your field and any funder or publisher mandates that you’ll need to be aware of. Topics will include metadata standards, data documentation, data preservation, and how to access Yale’s many resources for data management help. In addition, we’ll discuss data management guidelines for NIH, NSF, and NEH grants.

**Introduction to Programming with Python **

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. In this workshop session, we'll introduce you to basic Python programming with some examples of simple data analysis and GIS. No programming experience or statistical training required.

**Introduction to LaTeX/BibTeX **

LaTeX is a document preparation system for typesetting technical or scientific documents but it can be used for almost any form of publishing. In this workshop, students will learn how to install LaTeX, work with TeX editors, generate basic documents (e.g., papers and Beamer presentations), manage bibliographies, and collaborate with others using ShareLaTeX.

**Data Visualization and Tableau**

This workshop will familiarize you with key issues in data visualization. You’ll learn about the principles of creating effective visualizations and some common pitfalls that result in confusing or misleading ones. We’ll introduce popular tools, discuss their differences, and point you towards resources (at Yale and beyond) for learning to use them. We’ll also explore a portfolio of science, social science, and digital humanities data visualizations to help you imagine how you might communicate your data and findings through visualizations.

**Overview of Regression and Data Analysis**

In this seminar, we will introduce ANOVA and the interpretation of a basic linear regression. We will cover considerations made for time-series and repeated measures data. Generalized linear models (GLM) will be introduced to illustrate their use with count data, binary data, and survival data.