Workshops – Archived

CCS presents a Lunch and Learn session Modernizing Service Deployment with Ansible and Docker at UM Libraries on Wednesday, October 10, 2018, 12:00-1:00 PM. Register Now The University of Miami Libraries recently launched a new main website (https://library.miami.edu) and archival description and access system (https://atom.library.miami.edu). Charles Brown-Roberts, Web and Application Developer, will discuss developing and deploying...

Registration Closed |  Please Read Instructions Topics covered in this Workshop: The Unix shell Version control with git Programming with R Since the goal of this workshop is to bring you up to speed with data analysis, it would be incomplete without instruction on a shell as well as a version control system. The shell is important in building reproducible data analysis pipelines where your R scripts may be just one component, Git is...

Register Now This Bash, Python, and git workshop is capped at 35 participants, and is on a first-come/first-served basis. If you are looking to make yourself competitive in the job market If you want to take a personal project to the next level If you are interested in learning programming for data analysis Then, this workshop is for you. Our trained instructors will lead you through a comprehensive, hands-on overview of an...

Register Now This Bash, Python, and git workshop is capped at 24 participants, and is on a first-come/ first-served basis. If you are looking to make yourself competitive in the job market If you want to take a personal project to the next level If you are interested in learning programming for data analysis Then, this workshop is for you. Our trained instructors will lead you through a comprehensive, hands-on overview of an...

Register Now Open to UM Faculty/Staff/Students This workshop introduces participants to techniques for cleaning and transforming data with OpenRefine and Microsoft Excel. Sometimes datasets are created by multiple individuals, and/or created without a clear set of guidelines. The resulting datasets may be difficult to analyze because they lack standardization (i.e., New York City may be listed as “NYC,” “New York, NY”, “New York...