6 Habits of Successful Data Scientists

Key Takeaways

Hello everyone, Ken here, back with another video for you. Today I'm talking about the six habits that successful data scientists practice. As usual, if you enjoy this video, please hit that like button. And if you want to see more content similar to this, please subscribe to my channel and turn on notifications. The first habit that successful data scientists get into is the habit of asking good questions. Good question should further a conversation rather than to stall it. So, in general, asking a question that comes out of a a lack of knowledge that you could have had before is not a good question, and a question that expands on the knowledge that you have and that whoever you're talking to has is is what would be classified as a good question. Now, data science is characterized by asking questions in general. So, you're asking questions of data, you're asking questions of people, you're asking questions about problems that are in your realm of expertise. And establishing this habit, getting good at this skill, is something that'll take you a long way in this career path. The second habit that successful data scientists practice is to stay up to date on new techniques and new technologies. The data science realm is constantly changing. There's new technologies that are being introduced almost daily. And it's important to not necessarily work with all of them, but to have a familiarity with what they all do. Additionally, uh there's constantly new techniques, innovations in the field. And there's a chance that you can integrate some new methodology into your work. That gives uh your work, you know, more accuracy, uh more meaning, can help yourself or your company more. So, just practice staying on top of what's new in the field. And I have another video that I'll put above on where you can do that. The third habit that successful data scientists practice is related to your code. The proper versioning of code and the proper, like, note-taking in code. A lot of data scientists believe that they're going to almost be exclusively looking at their work. And as the field progresses, I see a lot more teamwork within data science. People are going to be looking at your code, people are going to be reviewing your code, and it's really important to write it with that in mind. So, great data scientists really pay attention to their versioning, they really pay attention to what other people will think when they view their code or view their process. The next habit that I see constantly from from top data science performers is that they have great communication and great interaction with their the end users of their algorithms or the projects they're working on. They make Their habit is that they check in with these people routinely to make sure that the work that they're doing is in line with the business value that they're creating. I think that this is really important not only for having a good project, but also for establishing relationships in the workplace. Uh relationships in the workplace with your peers make a lot of things a lot easier. And they really lead to good results in the field. The fifth habit that I see that I've actually only recently adopted is getting involved regularly with data science communities, whether that's on Reddit, on Facebook, on uh KD Nuggets, Kaggle, any of these communities help you understand the field better. You network with other data scientists, you learn new things, and when you work with other people, they push you and you push them to expand your thinking, uh to challenge what you're doing, and, you know, those two things are an integral part of of growth as a data scientist, as well as growth as, you know, general professional growth. So, I would definitely start exploring different communities. Uh I'm a member of of many groups on Facebook as well as YouTube. Uh and, you know, just I can't hammer that home enough. It's important to do data science with other people. The final habit that I see as extremely important is working on projects. Now, that can be in your free time, that can be some time that you've carved out in your work to to do something that's uh outside of your main focus. But projects help you to, one, reestablish your fundamentals because everything builds on the the core work that that you've done or that you did. They also help you expand and learn new techniques. And data science, as I mentioned before, is is constantly evolving, it's constantly changing. And, you know, to to to stay on top of the game, to stay good, you just have to practice. And projects are the best way because they're defined by your own expectations, they're defined by exactly what you want to get out of it rather than defined by, you know, someone else, like in the workplace, where they're where they're telling you what you want. There's a lot more freedom, there's a lot more creativity, and creativity is what separates, you know, really good data scientists from great data scientists. So, those are the six habits that I see many successful data scientists having. Of course, there are many more that I I didn't get into today. Please, in the comment section below, leave your thoughts on the important habits for for data scientists to have. As usual, thank you so much for watching, and good luck on your data science journey.