All The Things I Wish They Taught Us In Bootcamps | Eric Weber on The Artists of Data Science

On this episode of The Artists of Data Science, we get a chance to hear from Eric Weber, a lifelong learner, mathematician, and data scientist. He has cultivated a passion for sharing his work and experience with others to help them become excited about data science, as well as educating executives on all aspects of data science. He gives insight into his perspective of learning, how to be a leader in the data science field, and important skills that data scientists need to develop.

Eric shares with us what drew him to the field, and his transition from academia to the business side of data science. This episode highlights the journey and success of someone who has seen the field develop from the beginning, and has continuously improved over time. I think there is a lot to learn from this conversation!

Some notable segments from the show

[4:43] How Eric transitioned from academia to the business context

[11:40] What separates a good from a great data scientist

[20:59] Tips to communicate effectively with your team

[24:07] Is data science an art?

[34:52] Important soft skills that you might be missing

[41:15] How to navigate crazy job postings

Where to listen to the show

Eric’s journey into data science

Eric explains that his journey into data science began while he was in academia, teaching statistics and programming. He remembers receiving a phone call from his dad, where they discussed the rise of “big data”, and this kick started Eric’s fascination. Most of the concepts and idea that were based in data science were familiar to Eric already, but now he had the chance to work with data at scale.

Eric also got the opportunity to explore how data science can be used in a business context. He had to learn how to transition from a classroom setting to a business setting, which was an eye opening experience.

[4:43] “It’s actually been around for a number of years, but people’s journeys into it continue to amaze me. They’re all different. There’s no one set way to end up in a data science position. For me, I was in the academic world teaching statistics and programming, experimental design, things like that, all the way up until 2013, 2014. And I distinctly remember in a phone conversation with my dad and he at the time was an engineer for United Health. And he was like, well, they’re talking about all of this big data, stuff like blah blah blah, like, don’t you do things with data? Like I do. But I don’t know if I do things with big data. And that kind of kicked off my fascination.

Where is the field headed in 2–5 years?

The field is headed in a non-uniform direction, which is very valuable to the field.

Data science was always meant to be divided into sub-disciplines. There are very few people who are experts in everything, and it’s just infeasible at this at this point to hire someone who’s good at everything. Companies are changing in how they hire and what their expectations are, and specialists are becoming the norm.

Due to the recent economic pressure due to COVID, companies are going to really evaluate everything. This is time for data science to really prove it’s worth over these next few years.

[8:42] “I think I see the field head in in a non uniform direction. And I think that is maybe the most valuable thing that we have going on for us is data science has evolved into what it always probably was meant to be is a bunch of sub-disciplines, just like the idea of saying that you’re a data engineer or that you’re an engineer overall. There’s so many different types of engineering. They all require different skill sets. There’s very few people who are experts in everything. So as much as there’s been a lot of debate, I think people like, do we need specialists or generalists?

I think we’re getting to the point where specialties are the norm. But that’s not a bad thing.

Just because you’re focused on time-series work, generally speaking, doesn’t mean you’re not skilled. Just because you tend to focus on machine learning or system design doesn’t mean you’re not skilled. It’s just infeasible at this at this point to hire someone who’s good at everything. That isn’t just from a candidate perspective, though, it’s from a company perspective. They’re figuring out that what they’ve done in the past, which was hire data scientists and basically make them responsible for all things data. It’s actually really tricky to figure out how to use them effectively.

What will separate great data scientists from the rest of them?

Flexibility. What that means is the ability to have a different approach to different tasks, rather than using the same uniform methodology. Data science tasks require different skills and models.

Also, the ability to deliver business value, and not just scientific value. This is incredibly important. Some people go into data science positions thinking that it is doing a series of projects and establishing good code. But then they hand it off as if the business is going to magically use the thing that they’ve created. This is not a good assumption.

This flexibility to pick the right approach and the ability to really transform the business with your solutions are what make good data scientists great. Those two things are going to differentiate data scientists who are going to stick around at companies and data scientists who are going to be viewed as just “scientists”.

[11:40] “Flexibility like this idea of being flexible doesn’t mean that you can handle a whole bunch of tasks coming at once. It means that you can sort of ramp up your — not just flexibility. I think the best way to put this is, you know, what’s required to do different tasks. And you don’t always use a uniform approach to do everything. A data science task, task A is probably always going to be different from data science task B. And they’re probably going to require different skills. They’re going to require different models. They’re going to require people to understand how much is actually needed to solve the problem. You don’t need to build an incredibly powerful model for every situation, but you need to know what’s going to allow the business to thrive in a productive way.

Key takeaways from the episode

[14:25] I think we often go to school and we think about getting a degree and we continue to improve enough until we get granted our certificate. But in almost every case, those skills you learn are going to be outdated. In data science, that happens every two years, probably less.

To get the most out of your education, focus on the energy and desire to go into uncomfortable situations. You want to be uncomfortable most days.

You should work on stuff that isn’t clear or easy.

[35:14] Being able to connect with people with clear communication. Most people say a lot, without actually saying much. Are you able to communicate something useful in a clear way? That is the only thing that matters in a business context.

[41:32] You have to be comfortable with what value you can deliver. If you think that your skill set and your background can provide value for that company in that position, then go for it. Companies often will post things because they’ve seen their competitors use a similar job posting or they’ve just aggregated all the words from all the job postings that they’ve seen. It doesn’t mean that they know what they’re looking for.

[24:07] Science in general is an art. Any science done right requires technical mastery, yet there is a lot of gray area in how you do things. As you become better at data science, you start to see that there’s a lot of ways to approach problems. It’s not always obvious what makes one data scientist better than another. It’s not in the model that they build, typically. It’s in how they define a question and then pursue an answer.

[27:32] The challenge is that step by step problem solving doesn’t work well in the real world. In the real world, problem solving happens in a continuous cycle of development. A lot of people have a difficult time adjusting to this way of problem solving.

[30:52] Being a good leader is about figuring out how to unlock, amplify, and develop the people around you. Most companies evaluate leadership based on your impact, and your impact as an individual contributor can be huge. You can make it easier for your whole organization to do something. So it is not always about titles. It comes down to your impact and how it helps the people around you.

Memorable quotes

[6:35] “…my journey was all about figuring out two things. One, how to work with data at scale. And two, what does it mean to actually do data science in a business context. And those two things are really, really important…”

[12:17] “You don’t need to build an incredibly powerful model for every situation, but you need to know what’s going to allow the business to thrive in a productive way.”

[19:48] …”getting by is not a long term solution to delivering value for a business, because what you’re doing right now to get by is probably going to be automated in a few years…”

[23:50] “You’re not always gonna be the expert in the room. And if you are, you’re probably in the wrong room.”

The one thing that Eric wants you to learn from his story

[47:16] You have to be pretty tenacious if you want to be good at something. If you want to make an impact, you are going to have to do things that make you uncomfortable. I started posting on LinkedIn two years ago somewhere on that range. I was terrified of posting on social media like I, generally speaking, don’t like social media. But I started sharing things and people find a way to resonate with it. And it becomes super powerful and amazing. If you just think about it in the right way, but you have to have the right mindset. If you’re going to do big things, you kind of have to be willing to be tenacious and be willing to also fail.

From the lightning round

Be humble. If you’re humble about what you do, it naturally brings people to connect with you. And it’s not just about connecting with people. It’s about keeping yourself humble. As soon as you start thinking you’re the best, you’re in a position that is not going to be good for you long term. It’s not enough to be humble in your speech and outwardly interactions, you need to humble in your own mind. Realize that you’re never gonna be the most skilled at what you do. If you think you are, you’re not hanging out with the right people.

If Eric could go back in time, he would tell himself to continue to do what you’re doing. At 20 years old he was on the academic path with aspirations to be a teacher; and if he hadn’t gone into the academic world, he wouldn’t have found his way into data science. It was because he took that journey through academia that he was able to pave his own lane in data science and become such a positive role model for our community.

Eric is motivated by the desire learn, grow, and acquire more knowledge today than he did yesterday.

[53:24] I like to know more than I did yesterday or whatever, that sometimes that’s in work, sometimes it’s not. Sometimes that’s knowing more about myself, which is an uncomfortable thing to learn, and it’s just like I’m doing something or I know or understand something I didn’t quite get yesterday. And I think sometimes that small, sometimes that’s big.

Without hesitation, Eric thinks that all data scientists should spend more time studying social science.

[48:17] I think understanding the world in which you live and operate and the dynamics and behavior of human beings, that’s at the end of the day, who are really dealing with the most cases. If you don’t understand the broader context in which they live and operate and the stressors present there. It’s hard to understand who your users are and what you’re building things for.

Recommended book

Blink: The Power of Thinking Without Thinking” by Malcolm Gladwell

Books and other media mentioned in this episode

Multipliers: How the Best Leaders Make Everyone Smarter” by Liz Wiseman, Greg McKeown

Thinking Fast and Slow” by Daniel Kahneman

The 5AM Club” by Robin Sharma

Find Eric Online

LinkedIn

Twitter

Episode Transcript

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