This week, I've been gearing up for my batch at the Recurse Center, prompted by a flurry of facilitator emails. I've joined Zulip and indicated my interest in a reading/project group for Mastering Large Datasets with Python; posted my introduction to the Welcome thread and read about who else will be joining me; and connected with my onboarding buddy, a current RC attendee who can help me out during my first week. I'm a bit uncertain about strategies for scheduling my time - how many books can I read / study groups can I join, anyway?
I'm also clearing the decks by closing / bookmarking tabs that have accumulated, sorting them into browser windows and desktops, etc. It's always an interesting look at what's caught my eye in the last several months. I definitely have too many resources I'm trying to work with. For example, I've been reading François Chollet's Deep Learning with Python, on a rec from a friend, and it's slowly occurring to me that DL might be overly advanced for where I am in my data science journey. This sentiment is reinforced by having signed up for the NY Dept. of Labor's free Coursera program, which means I can now pursue a DeepLearning.AI TensorFlow Developer Certificate at no cost except my time (and the exam fee). I'm going to give it a go, again, picking up where I left off in the spring. I also have Allan Downey's Think Stats and Think Bayes, Skiena's Data Science Design Manual, and a pile of other, even more academic, tomes. (I'm a bit of a packrat when it comes to digital resources, I'm afraid!) Obviously I'm not planning to go through every one of them in depth, but hopefully I can find something that hits the right tone and level of assumed expertise for me.