This has been a fantastic experience to interview all these amazing guests! I am so thrilled, I can’t even believe that we’re here, that we’ve had over 70 guests on the show, and we’ve discussed so many different topics, so many different stories have been shared, and so many insights. And I want to say thank you to all of the guests that have come on the show for sharing everything and for helping inspire people to get into data science. I’ve heard so much feedback about how this has been encouraging, and how this has even been life-changing for many people. I also want to thank the team that’s working on this podcast. You guys are doing a great job.
But most importantly, of course, I want to thank you guys, the listeners. Without you, none of this would have been possible, none of this would have even made any sense. So thank you so much for tuning into this podcast on a weekly basis, or on a monthly basis, or however often you tune into it. Thank you so much for listening to these episodes, for helping us do what we do, giving meaning to what we do, and giving us your feedback, or just even seeing that you’ve listened to this episode is always encouraging and always inspiring for us to keep going and to create more.
And so, as mentioned, I’ve interviewed about 70 guests. The last time I checked, it was definitely over 60, so now we’re close to 70. And we’ve prepared for you today is a compilation of some of the most amazing and inspiring quotes and ideas which have been shared by guests. Are you excited about this? I’m super excited and can’t wait! Let’s get this started!
“Data science is so new! We’re all learning what it means to be in data science. So don’t let that hinder you. Be a part of it and define the trend in your own way. Mark out your own fate.”
“I think we’re still in this phase where people are thinking about data science as a theoretical exercise, where we’re sitting down and we’re just trying to write really cool and sophisticated algorithms to solve things. Which is great, but we really need to think about how do we take it to the next level, and what do we do with the outputs of these models, or whatever we’re building?”
“AI can solve amazing real world challenges. So of course, that’s becoming a super important job, because you can solve these amazing problems with these amazing solutions.”
“The future needs people who understand the technology, who understand the process, who understand people, and deliver something which balances everything together.”
“You know, be comfortable with being uncomfortable. That’s my tip. The thing is, whenever you start a new role, or whenever you start a new sale, or something like that, you never feel quite comfortable and you’re always delaying yourself and stopping yourself and trying to prepare, trying to prepare. Rather than actually just doing it. Go half prepared, and learn from your mistakes. A lot of the time, people over-prepare and they’ll waste a lot of time, or they’ll miss an opportunity.”
“It was a bit of luck, but also always being curious in new trends and technologies. You can’t always sit back and just let things happen to you. You need to go and find out what the trends are and what’s happening.”
“The way I look at it, data science is really about asking the right hypothesis, or many hypotheses, and then validating or invalidating those hypotheses, and then you come to some kernel of truth. The best data scientists that I know are the ones that are not married to one approach.”
“The field is always changing, you always encounter new problems that maybe aren’t exactly like what you’ve encountered before. And so the ability to know where to turn to find the information you need is really important.”
“The field moves very, very fast. 3 months is a long time now in AI. And it’s a bit of a problem. It’s very hard to keep up with that. But there’s no question you have to know the fundamentals first. And there are certainly textbooks and books that do that.”
“So if you want to execute a project, a task, some kind of project that you’re working on, it’s communication. I think that’s what really separates myself and a lot of the data scientists, as they’ve described their roles, from the IT guys and the sales people, is they’re able to communicate the complex findings that are derived from data mining and whatnot, to the stakeholders, the executives, the managers.”
“We’ve talked so much about how fast this field is changing, be it the tools, the techniques, the best practices. At the end of the day, those who evolve along with it are the ones who are going to thrive.”
“I would say in terms of the difference in the number of roles, so essentially a data scientist who is a strategic presenter, and able to build relationships, would probably fill 3 in 5 roles. The other 2 of those being the more technical, hands-on, perhaps more introverted roles, then reporting in to someone else who’s able to present the insights.”
“The challenge now is not accessing or creating data, or recording data. It’s so easy to do in a [inaudible] event, but making sense of it is not going to go away, it’s something that’s really important.”
“Just because you worked in data science, or data analysis, or have a math degree, it doesn’t mean you’re going to be a good data analyst.”
“Anything huge that seems too overwhelming can always be solved by taking little baby steps. You know, solving one little problem, solving another little problem, solving another little problem. And if you solve enough of those little problems, you can complete your overall goal. And that’s what data science is to me. It’s breaking up a huge problem into little manageable chunks. And being able to do those manageable chunks. And it’s very interesting for me to be able to solve that puzzle and tell a story.”
“But in the data science world, you get to have that satisfaction of creating a creative, elegant solution, but then in addition, you get the satisfaction of seeing if you actually implement it, value being produced very immediately from that solution within the company, either for your users, or internally.”
“I feel like the best way is to be driven by a passion, but also to do a lot. So the best way to learn is to either apply your own algorithms, or create your own visualisations. And every time you do that, you learn something new that you can take on to your next project. And then get it more and more advanced. But also share your enthusiasm to people so that they notice that.”
“I believe in this philosophy that data science is useless unless someone else thinks it’s useful.”
“The field is very broad. So don’t assume that you can catch up with everything immediately. That takes years. But certainly catch up so that you can read the literature and understand what the excitement is about.”
“The number of companies that need data scientists is far greater than the number of data scientists that are available. And with the way data is exploding, it’s going to be become a bigger gap. So it’s super encouraging to see all of these different avenues people can take, whether they want to learn Tableau and Alteryx, or they want to learn d3 and R, or whatever it might be, right? It’s whatever you’re passionate about, pursue it. You’ll find a job.”
“Where I learn the most is when I’m in community with other data scientists, with other engineers.”
“The actual things that I learned, those were not as important as just the ability to think critically and scrutinise assumptions, and then try stuff out, and then adapt fast to new research directions. So even without a PhD, I believe that skillset is obtainable. It’s just something that I think either you won’t get naturally, or you won’t get through lots of trial and error, and solving lots of problems.”
“Just like put yourself out there and start talking to the different people there. Get plugged in locally, because that’s going to get you a good local mentorship from one or more people in that community, but it’s also going to really help you as far as developing connections in your local community as you’re trying to get into data science as well.”
“If you have a data science background, you’re on track to be the head of a company. You’re on track to be the next breed of entrepreneur.”
“There have been a few revolutions. You look at the industrial revolution, and then you have the computer revolution, and you have the web. And I believe the AI revolution will be bigger than all of them.”
“I believe that in the not too far future, there are only going to be two jobs. There is going to be the engineer and the artist. Because machines are going to develop so much that they are going to replace a lot of jobs.”
“The proudest moment of my career will be when the AI kills me because of an objective that I’ve overlooked.”
“If we can utilise even a quarter of the information that we have, we can change the way people think altogether.”
So there you have it. Those were our top highlights from our first 100 episodes. Super pumped about this. Thank you so much for being part of this journey. I can’t wait for the next 100 episodes, and I really look forward for you to be part of this new adventure. And I’ll see you back here next time. And until then, happy analyzing.
