If you're interested in a more visual approach you can try https://traverse.link/ - it's an app I created which has spaced repetition, but really its goal is to cover the whole learning process, so it also has mind mapping and note-taking so you get a big picture view of what you're learning, why reinforcing bottom-up with spaced repetition
I run Traverse (https://traverse.link/) - an app which helps students learn faster using mind maps, flashcards and notes.
It's been running for over 2 years now and provides steady recurring income - I've prioritized continuity over exponential growth. Have reduced operational work to a minimum now so I can spend most of my time working on marketing strategies to make it big ;)
This is an editable template to learn data science skills most companies are looking for (from my 3-year experience in London fintech as a data scientist, and from the self-learning journey leading up to that).
The study content provided in the template is minimal, but you can go as in-depth as you like with the linked resources. The idea is that you study those resources by yourself, and then write down what you learned in your own words, directly into your own copy of the template.
I like to learn with flashcards (especially to memorize common interview questions), so I’ve added some example flashcards to help you get started - you can add your own flashcards or delete them if it isn’t for you.
The template is based around 3 pillars:
- Math & Stats
- Software Engineering & Tools
- Data & Business Communication
The Math & Stats section contains a structured list of recommended topics and principles to learn, with links to relevant resources like Khan Academy videos and the classic books.
The Software Engineering & Tools sections walks through tools to learn (based around the Jupyter-Python-Pandas ecosystem), and links to tutorials, videos, example notebooks and cheat sheets (all created by other fantastic people, I take no credit for the linked resources) to learn Python, Pandas, Scikit-Learn and Matplotlib.
The Data & Business Communication section is the real core of the template, where both of the previous sections come together. It’s shaped after the process for a typical business data science project:
- Data collection
- Data exploration
- Data cleaning & preparation
- Machine learning modeling: here I mention some common models actually used in businesses, like linear+logistic regression, random forests and timeseries forecasting
- Model evaluation
- Reporting & data visualization: focus on creating clear plots here
- Communicating with stakeholders: this is where I go more in depth on communicating your results to business decision makers, and telling a story which a layman can understand
Wish this article had been written a year ago when when I added Anki import to my learning app (https://traverse.link/)!
Especially importing media files and de-renaming them was a pain, as well as handling the different types of cloze deletes (some of this is described quite well in anki's docs, for example here https://docs.ankiweb.net/#/templates/generation)