Hi, I’m James. I woke up one morning and realized that there is a huge gap in data pedagogy, and I should fill it.
On the one side, there is a plethora of resources, from blog posts to university-curated MOOCs (massive open online courses) and boot camps, from which a person can learn data science. Then, learners jump to a platform like Kaggle (online machine learning community), create their own projects, or get assigned a data task at work.
This is a huge jump.
My first Kaggle playground was overwhelming, and I had already completed my master’s in economics. Everything had to happen: data cleaning, exploratory data analysis, feature selection, model building, prediction, and model evaluation. I can only imagine what that would have felt like in a work context.
So I am going back to my teaching roots, which are in English teaching. We gave students the basic grammar and vocabulary, then quickly have them practice that. With my Data Gym, I intend to do the same: isolate data “muscles” and provide a framework for strengthening them. I have found, and my research has confirmed, that there is a didactic gap here.
In addition to an English teaching background, I have also worked in: adventure tourism, nonprofit fundraising, hospitality, and economic research. My academic background is economics, with a passion for econometrics. My programming experience revolves around statistics and machine learning, but I have expanded that as I have gotten deeper into the Python community, in part through data talks at Python conferences.
In my free time, I’m our garden’s compost guru and child distracter, a mediocre acoustic guitar player, a reader of science fiction and other stories, and an occasional card player (Skat, anyone?). I’m also volunteering as a data analytics and German teacher (not at the same time).
Feel free to reach out if you have thoughts, comments, critiques, questions, or suggestions! jimbodonahue@gmail.com