Workshop Posts:


Grad Orientation

Graduate Student Orientation: Data Science & Management Workshop

Presented by the UC Davis Data Science Initiative & Data Management Program for all incoming graduate students.

  • Learn about data-enabled research support services, education and training opportunities at UC Davis
  • Discover how you can get involved in Data Science
  • Identify best practices for managing …
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Workshop - Data Visualization principles

June 9, 2017: 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 …

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RecomputingSS

Re-Computing Social Sciences: What Have We Learned After a Decade?

Featuring 12 invited guest speakers from Stanford, UC Berkeley, UPenn, UCLA, Illinois, etc., and 12 UC Davis faculty members. The workshop will be fast-paced and very interactive (the longest presentation will be 5 min long!) For more information and how …

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Workshop - Lessons from Binary Classification

In this second session, we will continue to explore machine learning with data driven examples.

Binary classification is the canonical machine learning task and its study has a rich history. Much of the main principles of machine learning have been discovered in this context. These principles continue to guide the …

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Workshop - Machine Learning

May 5 & 12, 2017: Machine Learning (James Sharpnack)

May 5: Part I - Lessons from Binary Classification: Overfitting and surrogate losses (James Sharpnack) Binary classification is the canonical machine learning task and its study has a rich history. Much of the main principles of machine learning have been discovered in this …

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Workshop - Efficient Code in R

April 28: Efficient Code in R (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 …

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