This workshop offers a brief yet comprehensive overview of essential mathematics for data science. It covers foundational statistics and probability, crucial for model understanding, and basic hypothesis testing techniques. It also introduces linear algebra concepts like vectors and matrices, alongside fundamental calculus for derivatives and integrals.
Register to attend this workshop at the bottom of this page. Zoom link will be emailed after filling out the registration form. Python is a popular language in academia and industry for developing software and data science applications. Compared to the other generic programming languages like C, C++, or Java, learning Python is relatively easy. This … Read More
Shiny is an R package that enables the creation of interactive websites for data visualization. This session provides a brief overview of the Shiny framework and how to edit and publish Shiny sites in RStudio (with shinyapps.io). Familiarity with R/RStudio is assumed.
This workshop is tailored for beginners in machine learning. It focuses on supervised learning algorithms that are a cornerstone of machine learning, where the algorithm learns from labeled training data, helping to predict outcomes for unforeseen data. Classification and Regression will be introduced. Participants will learn about key algorithms like Linear Regression and Decision Trees, … Read More
Maintenance is usually scheduled for weekdays because it often involves coordination with university facilities teams or external vendors. There may also be times when Amarel must be unavailable due to externally-scheduled data center facilities, power, or telecommunications maintenance. When this happens, we will do our best to avoid adding to a month’s downtime with our … Read More
Sharing your data and code is the essential step in maximizing the impact and usefulness of your research. This workshop first reviews repositories for data publication such as Dataverse, ICPSR, OSF, Zenodo, and more. Then we turn to a detailed discussion of building R packages. R Packages are an excellent way to distribute collections of … Read More
This workshop is designed to introduce the concepts of unsupervised learning, a branch of machine learning where algorithms infer patterns from unlabelled data. The course covers clustering methods like K-means and DBSCAN, used to identify inherent groupings in data. It also explores dimensionality reduction techniques such as PCA, which simplify complex data sets while preserving … Read More
The RStudio environment enables the easy creation of documents in various formats (HTML, DOC, PDF) using Rmarkdown, while knitr allows the incorporation of executable R code to produce the tables and figures in those documents. This session introduces these concepts and other packages and practices supporting reproducibility with the R environment.
This workshop offers an introduction to the fundamentals of deep learning, a highly influential branch of artificial intelligence. This session focuses on the core concepts of neural networks, including feedforward neural networks, the simplest type of artificial neural network architecture. The course also covers convolutional neural networks (CNNs), essential for image and video recognition, and … Read More