We are proud to announce our 2022 Keynote speakers!
R is a fantastic language for creating interesting analyses, the results of which you often want to share with stakeholders and executives. Unfortunately, there is often a lot of work between what you do in an R Markdown file and what you want to put in a PowerPoint presentation. Cleaning charts, culling results, and creating a story are core components of the data science work, but these are often less discussed than earlier parts of the data science process. In this talk I’ll walk through the difficulties with getting data science output ready and some lessons I have learned on the topic during my career.
Dr. Jacqueline Nolis is a data science leader with over 15 years of experience in managing data science teams and projects at companies ranging from DSW to Airbnb. Jacqueline has a PhD in Industrial Engineering and coauthored the book Build a Career in Data Science. For fun she likes to use data science for humor—like using deep learning to generate offensive license plates.
This talk will present 10 simple rules I follow when teaching R for Data Science. These rules have been largely learned from others in the R, data science and computational education community. I have trialed and tested them in my teaching of R for Data Science throughout the past 6 years in both undergraduate and graduate Data Science courses at the University of British Columbia.
Tiffany Timbers is an Associate Professor of Teaching in the Department of Statistics and an Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia (UBC). She received PhD in Neuroscience in 2012 from UBC, following which she held a Banting Postdoctoral Fellowship at Simon Fraser University where her research focused on cell biology & genomics. This postdoctoral research was data intensive and required the application of data science and statistical methodologies. After her research Postdoctoral Fellowship, Tiffany joined the founding team who developed the Master’s of Data Science program at UBC as a Postdoctoral Teaching and Learning Fellow. In 2018, she joined the Statistics Department at UBC in her current role of an Assistant Professor of Teaching. Currently she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. She primarily teaches courses courses on introductory statistics and data science, computer programming, reproducible workflows and collaborative software development.