Spring 2017 Workshops, tutorials, etc.

Events Co-hosted by the Data Science Initiative and Digital Scholarship

All events are in the Data Science Initiative seminar room, 360 Shields Library from 12 - 1.30pm on Friday, unless otherwise noted.

Please suggest topics you would like to have a workshop on. Send an email to datascience@ucdavis.edu.

  • 12 - 1.30 April, 14th Python for Data Analysis I
    Clark Fitzgerald

  • 12 - 1.30 April, 21st Python for Data Analysis II
    Nick Ulle

  • 12 - 1.30 April, 28th Making R Code Efficient
    Duncan Temple Lang

    It is relatively easy to write R code quickly, but harder to write quick R code. We'll discuss some general strategies for writing faster R code and then explore R tools for finding bottlenecks in code and how to make these faster. We'll use one or two in-depth case studies to see the process and reasoning you would use on code. We'll also look at alternative approaches to making code faster by integrating other languages, e.g., the shell, SQL and databases, and C and C++. The intent is to show that these are quite accessible.

  • 12 - 1.30 May, 5th Machine Learning I
    James Sharpnack

    Overfitting and Surrogate Losses

  • 12 - 1.30 May 12th Machine Learning II
    James Sharpnack

    Non-Parametric Methods and the Kernel Trick

  • 12 - 1.30 May 19th
    No workshop due to the ReComputing Social Sciences event on campus.

  • 12 - 1.30 May 26th Introduction to Docker
    Titus Brown

  • 12 - 1.30 June 2nd Data Visualization Principles
    Duncan Temple Lang

    In this workshop, we'll discuss best practices for displaying different types of static data. This is about designing data visualizations rather than the software and instructions we use to actually render them. We'll cover some basic principles to keep in mind, explore different types of plots, critique some good and bad examples of displaying data and how to think about creating effective plots.