Please note registration for each workshop will close at 11a.m. the day of the workshop.

REGISTER HERE

Introduction to R with Cameron Riopelle

Wednesday, February 10th, 1-3 p.m.

Designed for new R and R Studio users. It provides an introduction to the R software program, including its programming language, software-environment, importing data, descriptive statistics, transforming variables, selecting and splitting data, exploratory tests, and visualization.

Intermediate R with Cameron Riopelle

Monday, February 15th, 1-3 p.m.

Designed for intermediate R and R Studio users. It covers common statistical methods in R such as means comparisons, ANOVA, linear regression, and basic visualization.

R for Data Visualization with Cameron Riopelle

Wednesday, February 17th, 1-3 p.m.

Introduction to using the software program R for visualization. Prior experience with R required--this workshop assumes knowledge of the R language and environment.

Research Data Management Workshop with Tim Norris

Wednesday, February 24th, 1-3 p.m.

This is an introduction to topics in research data management designed to foster skills and encourage data management best practices for efficiency, compliance and security in the research environment. This is a discipline agnostic seminar. Specific learning goals include the identification of best practices for: file naming conventions, file system organization, data security, data privacy, backup strategies, data sharing, data documentation, and data publication. These topics introduce practical behaviors to ease the digital research process.

Introduction to SPSS with Cameron Riopelle

Tuesday, March 9th, 1-3 p.m.

Designed for new SPSS users. It provides an introduction to the SPSS software program, including its software environment, importing data, descriptive statistics, transforming variables, selecting and splitting data, and visualization.

Intermediate SPSS with Cameron Riopelle

Wednesday, March 17th, 1-3 p.m.

Designed for intermediate SPSS users. It covers common statistical methods in SPSS such as means comparisons, ANOVA, linear regression, and logistic regression models.

Tableau for Data Visualization with Cameron Riopelle

Wednesday, March 24th, 1-3 p.m.

Participants will learn the basics of data visualization using the software program Tableau, including connecting to data, visualizing univariate and bivariate data, visualization design, building dashboards, and online sharing.

Introduction to NVivo with Cameron Riopelle

Wednesday, April 7th, 1-3 p.m.

This workshop is an introduction to using the software program NVivo to conduct qualitative research, including the NVivo environment, coding, analysis, visualization, and generating reports.

Introduction to ArcGIS Online with Jorge Quintela

Tuesday, April 20th 10th, 1-3 p.m.

This workshop will introduce you to ArcGIS Online, the ESRI’s cloud-based mapping and analysis platform. You will learn how to create interactive maps, how to add, manage and share content, and how to perform basic spatial analysis procedures with your data. You will also learn how to create and share basic web apps. Participants should register in advance to receive the required ArcGIS Online credentials before the session starts (only UM email accounts will be used to provide access to the software).

Introduction to ArcGIS Business Analyst Web App with Jorge Quintela

Tuesday, April 27th, 1-3 p.m.

In this introductory workshop you will learn how to access a wide variety of demographic, consumer spending, and business data aggregated at different geographic levels, and how to generate presentation-ready reports, infographics and maps. A basic understanding of ArcGIS Online is required. Participants should register in advance to receive the required ArcGIS Online credentials before the session starts (only UM email accounts will be used to provide access to the software).

Introduction to Python for Data Analysis with Cameron Riopelle

Wednesday, April 28th, 1-3 p.m.

This workshop is an introduction to the programming language Python using the Jupyter environment. The basics of using Python for data analytis are covered, including using Jupyter, importing data, using popular libraries such as pandas and NumPy, data visualization, and saving/exporting.