In this article, I share several techniques and an actionable template to organize the content that we consume and make room for new creative ideas in our busy lives as data professionals.
The amount of information and technologies in the data space is overwhelming — as a data engineer, I experienced it first-hand. Technical concepts, databases, cloud services, open-source frameworks, and tools for data science, visualizations, and ETL processes — it seems impossible to keep up. We consume so much content these days that we shouldn’t blame ourselves for not being able to manage this information overload. In this article, I share several techniques and an actionable template to organize the content that we consume and make room for new creative ideas in our busy lives as data professionals.
In the constantly evolving world of technology, it’s hard to stay up-to-date with the recent developments. Luckily, we don’t have to know everything, but rather know where to find the information we are looking for.
I encountered many software engineers who don’t write anything down and rely solely on a Google search (or rather Stack Overflow search) for information retrieval. Don’t get me wrong, I love Stack Overflow, but I hate solving the same problem twice only because I forgot how I previously arrived at the solution.
Therefore, I developed a Getting-Things-Done system that I can rely upon, and that let me store and later find everything I need. The basic principle is that:
“Your mind is for having ideas, not (for) holding them. “
— David Allen 
If you can capture things you’ve learned to some external system (your “second brain”) and rely on it to quickly find the relevant information, you don’t need to store everything in your head. It gives you more space for new ideas and to focus on things that are most important.
The implementation details, such as which app you end up using to take notes (such as Notion, Evernote, Roam, OneNote, etc.), don’t matter as long as it lets you capture, organize and retrieve everything. Many still prefer to take physical notes, and that’s fine if it works for you.
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What is the most important thing to do after you got your skills to be a data scientist? It has to be to show off your skills. Otherwise, there is no use of your skills. If you want to get a job or freelance or start a start-up, you have to show off your skills to people effectively.
Find out here. Although data science job descriptions require a range of various skillsets, there are concrete prerequisites that can help you to become a successful data scientist. Some of those skills include, but are not limited to: communication, statistics, organization, and lastly, programming. Programming can be quite vague, for example, some companies in an interview could ask for a data scientist to code in Python a common pandas’ functions, while other companies can require a complete take on software engineering with classes.