Last year I gave a list of my favorite SciPy 2017 talks. The SciPy 2018 conference took place from July 9 to 15 and the talks and tutorials are now in a YouTube playlist created by Enthought. I have gone through all of this years talks and watched through any that seemed interesting. Read on for my suggestions!
Overall, it felt like there were a lot of machine learning and geoscience/geo-related talks and packages. A lot of the ML talks seemed to be missing practical insight into applying a specific method or seeing how someone solved a problem in a way that generalizes to other problems. The geoscience ones weren't very interesting to me, but might be worth checking out if you're interested.
Tracy's keynote gave a great perspective on how we see and value data in science. It was fascinating to me that she showed results from a survey of Bioinformatics Resource Australia that training and community building were more requested than funding!
Your user knows they want a healthyish but tasty pasta for dinner but aren't quite sure exactly which recipe to choose. How can you help narrow their search and show them closely related recipes to give them enough options without making their search exhausting? This talk will show you BuzzFeed/Tasty tech's solution to creating a consistent method for finding similar Tasty recipes using word2vec.
This talk gave an application of machine learning in industry and gave a great example of how ML was used to add value to a product.
Choosing the visual form for a visualization is a decision about what aspects of the data matter most. Highlight or ignore outliers? Look at values, differences, or changes? Compare to 0, median, or mean?
In scientific analysis we risk missing discoveries by failing to notice important features of our data, yet we often use default parameters and charts without realizing what we might miss.
I will demonstrate how to translate questions about your data into chart parameters. Using Python examples, I'll illustrate powerful techniques like using color intentionally and creating 'small multiples' of charts that vary visual form or data.
This talk had a lot of really great practical tips for producing good visualizations for yourself and for others.
These were a series of 5 minute lightning talks from core packages in the SciPy ecosystem. They are definitely worth a watch to get up to speed in all that's happening in the ecosystem.