SQL is a standard language designed to query and extract data from tables stored in a database. Python, on the other hand, has well-known libraries specially designed for data analysis and manipulation. This course will introduce attendees to the basics of SQL, relational database management systems, and options for integrating these with Python for data...
This short class will illustrate several powerful tools and features that can enhance one’s experience with Jupyter notebooks. The interactive demonstrations will include a brief overview of plotting tools and magics, followed by demonstrations of how to make interactive widgets for interactive visualizations and dashboards and how to generate analysis pipelines and reports. Basic familiarity...
Please REGISTER in advance here. Workshop will be conducted in Zoom in PST time. Invites are sent out the day before the workshop. In this hands on workshop, you will be provided with a Jupyter Notebook workspace to import census data, explore, clean, and prepare the data for analysis using python. You will also learn...
Please REGISTER in advance here. Workshop will be conducted in Zoom in PST time. Invites are sent out the day before the workshop. The misuse of null hypothesis significance testing (NHST) and p-value thresholds, together with selective reporting of statistically significant results, have produced an inundation of overstated conclusions and unreplicable results in scientific...
Paraview is an open-source cross-platform program for interactive scientific visualization. Paraview can run as a stand-alone visualization tool, as well as in a client-server fashion for remote visualization. In this tutorial, we will explore the visualization pipeline and basic features in Paraview. We will show how to prepare data files from scratch. We will demonstrate...
While Python becomes the most popular programming language since 2019, data scientists often have a few common complaints about its slow speed and the limited capabilities of handling the big data scenarios. In this workshop series, we will present an extensive discussion on how to improve the performance of Python in data science by looking...
Survival analysis models time-to-event outcomes. This workshop introduces usage of the survival package in R for some of the most commonly used survival methods. Topics include data setup, Kaplan-Meier estimates and curves, log-rank tests, fitting the Cox proportional hazards model, assessing the proportional hazards assumption, and modeling time-varying covariates. Experience in both survival analysis and...
This is an overview of how our Research team is working with researchers from different domains to enable research and to help accelerate projects. Outlining the various resources, tools, and different approaches to empower institutions and researchers to do more, faster. In this session we can discuss specifics on how these approaches might apply to...
Research data has grown to unprecedented sizes making local storage, efficient data analysis and long-term archiving a significant challenge. In this session we will introduce the stages of the research data lifecycle. We will discuss how data management plays a role at key stages of the data lifecycle. Finally, we will review tools and resources...