SDS 050: Teamwork

Incredible. Just incredible. We are up to episode number 50 and I am so thankful that you are here, that you’re watching or listening to these episodes, that you’re part of this data science movement that we’re creating, and helping people build their careers. Thank you so much, I really appreciate you being part of this. And today I wanted to say that none of this would have been possible without the team behind it. I just record the episodes. But the team is the key to creating all of this. And I just wanted to give a big shout-out and huge thank you to Ruby, Matt, our editor, and Rachel, who does the transcripts, for helping out with these great episodes for making all of this happen.

And moreover, I wanted to give a huge shout-out to the whole team at SuperDataScience for working so hard on creating a better future for so many people in the world. So thank you very much to Martin, Damien, Paolo, April, Leonid, Camila, Juan-Pablo, and Naeli. You guys are just making people’s dreams come true and thank you so much for being part of this. I’m really proud to be working on SuperDataScience alongside with you guys.

And on that note, for everybody listening, I just wanted to talk today about teamwork. It’s very important to understand the value of being part of a team. The value that a team can bring to your projects, or the value you can bring to a team’s project. Because personally, when I was growing up, I’ve always kind of been more introverted and more focused on delivering my own results, and even in sports, I was always into the sports where you’re just dependent on yourself. You’re not dependent on a team. And I find that I probably would’ve benefited a lot from being more involved in team sports, more involved in team activities. But I’m closing that gap.

I’m learning now how valuable it is to be part of a team and for those of you who are working in data science roles, or looking to work in data science roles, it can be very easy to get carried away in becoming a data scientist in your own way, becoming a lone wolf data scientist who just does all the analytics, and you know all of these different things, and you can pretty much solve any problem. But at the same time, it doesn’t happen like that in life. You can do a lot of that, but when you have a team, or when you’re part of a team, you’re always going to be much more efficient, you’re always going to have more opinions on the different problems, you’re always going to be able to solve different problems much faster. So it’s very important to be conscious about developing these teamwork skills early on.

So whether you’re contributing to a team, whether you’re leading a team, whether you’re building a team, whether you’re just part of a team, it’s very important to understand how you operate in a team environment, how you contribute, what are your strengths, what are your weaknesses, where you can contribute, where you need to rely on somebody else. And so just look out for these things and even in data science, there’s always going to be people who are a bit better at programming. There are always going to be people who are a bit better at building a presentation. People who are a bit better at actually pitching the outcomes or identifying the problem at the start or preparing the data. And different people are passionate about different things as well. So it’s always important to look out for those strengths and weaknesses that you might have in your team and leverage the strengths of people so that together you have that synergy and you can really create huge impacts for either your clients or for your company and make things happen.

So that’s why teamwork is so important and maybe next week, look out for some of those tasks that you are doing on your own that you could be doing as part of a team or some of those things that you’re already doing as part of a team and that you could be doing better or what lessons can you learn from those. And on that note, once again, congratulations with episode number 50. I look forward to seeing you next time. Until then, happy analyzing.