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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 Oken Lab, Department of Wildlife, Fish, and Conservation Biology at the University of California, Davis. 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.