My name is Billy, I'm a data scientist that likes to learn. This blog is dedicated to my data science journey, the broader data science industry, and my passion for tech and math.

Inspired by Scott Young's Ultralearning, I decided to dedicate a year to learning data science fundamentals. Even though I still have a long way to go, I learned quite a bit about data science in that year. Mainly that:

People don't want a picture, they want a playground. Something they can interact with and learn from.

The analysis can only be as good as the data you have. Garbage in garbage out.

The money is in eliminating pain points - if it makes them mad (and you can fix it) it makes you money

Again, there is so much more to learn but these are the main ideas I keep coming back to.

The picture at the top of this page represents a stylized view of that year of data science self-study. The height of each bar represents the amount of time I spent on my learning project on a given day, with an average of 98.86 minutes spent learning and a total of 3 / 366 days missed. I am confident if I can continue to add more bars to this chart, I can add a few more bullet points to the above list.

I have found that when trying to make sense of data most people don't want a picture they want a playground. This means that an audience wants to be able to explore, interact with, and discover insights from the dataset themselves. That's where DIMMiN comes in. I want DIMMiN to be a data playground where users can interact with my models, learn to build models on their own, or just track some of the projects I am working on as I am figuring them out.

The vast majority of the time spent on my learning project was on my own. There's nothing inherently wrong with that but there is a reason that one of Scott Young's main principles of learning is feedback. If I never hear about where my code could be improved or why my models might be under-performing I will never reach the level of skill in data science that I want to. The long term goal of this blog (besides world domination) is to build a community of data-oriented people who can help each other learn and grow as they solve the world's most wildly impractical (ok, sometimes slightly practical) problems. Have an idea for how I can get through a sticking point in one of my projects? Send me a message. Heard about an interesting dataset/data source that's worth looking into? Send me a message. Know tomorrow's winning lottery numbers? Send me a message immediately.

I am looking forward to learning from each other. After all, what would a data scientist be without input?