I went from spending 6 hours a day writing code as a Computer Science major to only writing a couple of lines a week in my free time. I've kept up with the industry. But lately, I have had such a desire to get back into it. I love the data science space. I even had the opportunity to TA an Intro to Data Science course for a month last summer. Simplifying problems to teach others was impactful not only for the benefit of the student but also for myself. I was able to chat openly about my love for the data science space, learn new concepts, and apply the problem and solution to provide as homework. When I'd get stuck on a concept, it would encourage me to iterate on ways to make it more accessible to the students.
My data science infused summer was wonderful. I explored many introductory concepts and even began poking at my projects in the space - could I experiment with AI/ML next? Maybe use Computer Vision to help reduce bike accidents across Boston? Unfortunately, I fell off the wagon during the pandemic. I was tired of staring at my laptop screen hunched over in a chair for 12 hours a day between work and side projects. Quite simply, I'd lost the motivation to learn.
But that changed...
Three days ago, I began listening to the book Ultralearning by Scott H. Young, a book that boils down to 9 core principles on self-directed learning. As I listened to the book, my mind floated to the idea of returning to data science. I was passionate about the data science space but had lost the motivation to learn. Later that evening, I stumbled upon Ken Jee's YouTube channel and the 66 Days of Data challenge he'd kicked off to get himself back into Data Science.
The beauty of the 66 Days of Data challenge is that the goal is to build data science as a habit into your daily life, not to learn all components of data science within the timeframe. The baseline is 5 minutes a day for 66 days which is easily doable, as long as I prioritize it.
Step 1: Build a curriculum
Following the steps from Ultralearning, I began creating a curriculum to learn data science. To start, I looked at data science curriculums across multiple universities. After comparing each one, I built myself a curriculum that covered the core classes and added a few 'electives.' The complete curriculum, including links to the sites I am using, can be found here
Using Notion, I'm tracking my start and end dates for each section along with the tools I referenced. Eventually, I'll include notes and whiteboard drawings.
Step 2: Build the Habit
I need to spend a minimum of 5 minutes each day learning data science. To do this successfully, I need to make the material accessible. I did this by:
Curating a list of books, podcasts, and videos on data science (I ensured I had items that did not require an internet connection too)
Compiling a list of websites that provide hands-on material with data science
Creating a central location to find my resources (Notion page)
By doing this, I made learning data science simple and accessible for myself. I know my strengths and weaknesses when it comes to sticking to habits. By creating a seamless experience for myself, I've increased the chances of building data science into my daily routine.
Step 3: Build something
My ultimate goal for this challenge is to build a foundation in data science. At the end of my 66 days, I'll kick off work on a personal project. I'm already thinking of potential research projects around climate change I can do. The Python refresher was a ton of fun to complete. Also,
According to JetBrains State of Developer Ecosystem survey: Python is one of the top 5 languages, one of the fastest-growing languages, and one of the top 5 languages developers are migrating to or adopting. It's a promising outlook to learn a language that is continuously growing in demand and interest.
I would love to work in the data science space -- whether that be content creation, as a PM, or programming -- and having a foundation in these core areas will help me achieve that.
I'll post weekly updates here on my progress and goals.
I'll catch you in the next one đź‘‹
Alyssa
Top comments (1)
Hi Alyssa!
I read your post and I really like it! I too read the Ultralearning book and have been inspired to start on my own Ultralearning project. However, I'm just now starting out on my first ever Ultralearning project and I'm trying to figure out how I should go about doing my research for my project. I'm currently in the metalearning phase and I want to come up with a solid plan before with a couple good resources before I start my project. I was wondering if you could maybe share some light on some resources, tips, and/or recommendations for my Ultralearning project?
For my project I want to learn how to build a full-stack application using Angular and SpringBoot if that helps at all.