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Sophia N. Wassermann, PhD

Marine ecologist interested in quantitative approaches to issues at the intersection of fisheries and climate change. Postdoc in the Punt Lab, School of Aquatic & Fisheries Science, University of Washington, in collaboration with the NOAA Alaska Fisheries Science Center & Northwest Fisheries Science Center. PhD in Earth & Ocean Science from the National University of Ireland, Galway.

Summer of Data Science

Today I want to tell you about what I’m going to do this summer because if I tell you, you’ll hold me accountable to reaching my goals. Right? Thanks.

There’s a challenge by Renee, author of the Becoming A Data Scientist blog, called the Summer of Data Science (#SoDS17 on Twitter). As she describes it, “the Summer of Data Science is a commitment to learn something this summer to enhance your data science skills, and to share what you learned.” Sounds like the motivation I need, as I’ve been working on a proposal to use both Bayesian analysis for model selection and a neural network to create a new model of collective behavior from video of fish schooling.

Unfortunately, I know very little about Bayesian statistics or neural networks.

For the former, I’m attending a course with Highland Statistics in Norway (!) this fall. I’ll let you know how that goes. For the latter, I’m going to attempt to teach myself about neural nets/deep learning in Python. I’ve been researching machine learning programs on and I’ve decided to start with the DataCamp Deep Learning in Python course. I chose it because of the praise I’ve heard for DataCamp and because I’m thinking that deep learning (specifically neural networks) will be the best approach for me. I found this article explaining the differences between machine and deep learning very helpful.

I’ll update this blog as I work towards my deep learning mastery goals, including what resources I’ve found helpful. If you’d like to join in on the fun, check out Renee’s post on the challenge here: Summer of Data Science 2017.