Led by: Dustin JohnsonDescription: Unsupervised learning involves determining a hidden structure (typically groupings or clusters) from unlabelled data. Without labels, we don't have a measure of accuracy to determine which model to trust more. In this workshop, we will demonstrate how more minds work better than one and let a consensus of experts decide the ideal clustering. Brought to you by Applied Quantitative Methods.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/176
Led by: Emre ErhanDescription: Machine learning classifiers are a powerful tool for determining to which category novel data belongs given some training data. This workshop explores the basics of using the scikit-learn Python library with some toy cancer datasets.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/170
Led by: Bruno GrandeDescription: You may have heard of the term literate programming. It's actually been around since 1984 when it was coined by Donald Knuth. Essentially, the idea of literate programming is the interweaving of code and documentation. R Markdown goes one step further and incorporates the output of your code such as figures and tables, allowing you to develop a narrative for your analysis. You can now generate fully reproducible high-quality reports of your analyses and easily share them with others. This workshop will introduce how to get started with incorporating R Markdown into your workflow. For a quick demo of what you can achieve in R Markdown, check out this quick 1-minute video.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/169
Led by: Marie-Hélène BurleDescription: Emacs is a powerful, extensible and highly customizable text editor. It can do anything that can be done with plain text: write and run code, literate programming, version control, writing your papers and thesis, organizing your agenda and notes, emails, etc. The idea behind this workshop is to get you started on your emacs journey so that you can more easy look for information and build up the knowledge yourself.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/174
Led by: Nafiseh SedaghaDescription: It is said that cleaning/cleansing data takes 80% of data analysis process. Data cleaning must be repeated for every new data in every project. Typically, data sets obtained from a real world problems violate the standards of clean data in different ways and analyzing data without cleaning is impossible. In the process of cleaning data, we try to remove every possible problem in data and organize the values in a standard manner.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/172
Led by: Vanessa GuerraDescription: Scared of the command line? Overcome your feat and attend this introductory workshop on the Bash shell! You will learn how to navigate your file system and automate tasks to make your life easier.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/171Register here: http://www.lib.sfu.ca/about/branches-depts/rc/32591
Led by: Jessica WalshIMPORTANT: This workshop will be in room 3008 of the library (not the usual room 7010).Description: Learn the benefits of using Git to track your changes, improve workflow and share code in collaborative projects. This will be designed for beginners, as we will go over the basics of Git and Github. Basic knowledge of Bash and R Studio will be helpful.Required preparation: https://github.com/sciprog-sfu/sciprog-sfu.github.io/issues/173