Subject archive for "domino-data-science-field-note," page 3

Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and local affiliates. Members of Domino were in the live audience for the City Arts talk. This Domino Data Science Field Note provides insights excerpted from Crawford’s City Arts talk and from her NIPS keynote for additional breadth, depth and context for our data science blog readers. This blog post covers Crawford’s research that includes bias as a socio-technical challenge, implications when systems are trained on and ingest biased data, model interpretability, and recommendations for addressing bias.

This Domino Data Science Field Note provides highlights and video clips from Addhyan Pandey’s Domino Data Pop-Up talk, “Leveraging Data Science in the Automotive Industry.” Addhyan Pandey is the Principal Data Scientist at Cars.com. Highlights covered in this blog post include Pandey using word2vec to identify duplicate vehicles on the platform, how his data science team refers to predictive models as “data products”, and the company’s overall approach to data science. While this post covers highlights and video excerpts, the full video of his talk is available. If this type of content interests you, visit the Domino Data Science Pop-Up Playlist or consider attending Rev.

This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”. In his talk, Wickham advocates that, unlike a GUI, using code provides reproducibility, data provenance, and the ability to track changes so that data scientists have the ability to see how the data analysis has evolved. As the creator of ggplot2, it is not a surprise that Wickham also advocates the use of visualizations and models together to help data scientists find the real signals within their data. This blog post also provides clips from the original video and follows the Creative Commons license affiliated with the original video recording.

Grace Tang, Data Scientist at Uber, presented insights, common pitfalls, and “best practices to ensure all experiments are useful” in her Strata Singapore session, “Turning Fails into Wins”. Tang holds a Ph.D. in Neuroscience from Stanford University.