Interview with Eric Weber, Influencer and Data Scientist
Hi Eric, can you tell us about yourself and your path in Data Science?
“My exposure to Data Science and programming started in 2008/2009. At that time, using R was not very user-friendly for me. The same was true for Python. My interest in data was always there, but it was just a matter of formalizing it as an actual role, so I did Data Science without calling it that for a few years. Around 2013/2014, I started thinking more about my path and my industry, and for me that meant thinking about what it means to solve practical problems, and not necessarily trying to publish about it on a regular basis. When you publish, you're looking for perfection, you're looking for a paper that meets multiple reviewer standards and responds to their feedback. When you work in an industry, perfection is kind of the enemy. You don't necessarily want to be perfect, you want to produce something that does its job, but honestly, if you're looking for perfection, it's not going to move. You're just not going to move fast enough.
At the time I started at LinkedIn, and I was actively writing on the platform, I contacted Laurent Meyer who leads Constructor Learning. We exchanged ideas on how I could be a part of Constructor Learning and actively teach while still doing my job in the industry. Over the last few years, that shift has happened for me. From being an individual contributor, working much more in the day-to-day of Data Science, to a leadership role. A lot of people who join Data Science organizations may not always be Data Scientists who code on a day-to-day basis. However, they will have an impact and you always have to start somewhere.”
You are an influencer in Data Science. What made you associate yourself with Constructor Learning?
“The decision to associate with a program provider is a pretty big one because in many ways your values and expectations for what Data Science means have to match. The biggest reason for me to associate with Constructor Learning is quality. There are a lot of opportunities to participate in programs and courses and learn from other people in the industry and around the world, but when you think about the number that really build up and focus on the quality of the experience for the person, it narrows it down to a few. There are a lot of universities and programs that are more focused on how much revenue they can make from a single person. The quality of the experience is important and it directly leads to the success of the participants that go through a program. Finally, quality leads to getting jobs in the industry. That's something that's particularly important to me. There are a lot of promises about how exactly a program can prepare you for a particular role. Very few have the same level of evidence as Constructor Learning, and that's one of the reasons I spend a lot of time with Constructor Learning. I believe in it and that's why I want to continue to work with them.”
What are the advantages of Constructor Learning’s Bootcamp?
“As you're thinking about moving into your career in Data Science, Constructor Learning's Bootcamp adds value in a number of ways. Before you start, there are some things that you need to consider. The first component is your skill development, the second is your preparedness for industry and the third is how you connect with the industry. Constructor Learning addresses each of these points in a great way.
Many Bootcamps on the market focus on generic skills. They try to cover a lot of things, rather than go deep. Constructor Learning takes the approach of going in-depth and really having rigorous exposure to these different ideas. That pays dividends as you start to go into your career as a Data Scientist. You don't just learn about the algorithms. You learn about why they work. You learn about the mathematics behind them and that proves really fundamental.
The second component is all about getting yourself ready for the industry. Something that Constructor Learning takes very seriously is getting you to think like you're on the job. How does the content and the techniques apply in a real industry setting and through the combination of exposure to real companies and real projects and problems? The curriculum gets you ready for actual industry work in a way that many Bootcamps are unable to do.
The third component is about connecting you with industry. I think where Constructor Learning stands out in a way that others do not is the deep partnerships that Constructor Learning has developed, maintained and actually grown over time. The way that students are connected with companies at the end of the program for projects is pretty much second to none. These three things make Constructor Learning stand out and I've seen a lot of Bootcamps and programs and this is a unique opportunity for you to address all three things at once.”
How to switch from another area to Data Science and how to build a successful career?
“The thing that makes you excellent in Data Science is your problem-solving skills. Likewise, using your knowledge of a particular industry or domain to actually help you become a more effective Data Scientist. I think there's an intention or a rumour going around: “you have to suddenly forget everything you knew to get into Data Science. You have a PhD or a Master's degree, but that doesn't matter.” Of course, it matters! You're an expert in a particular field, that you know how to do research, that you know how to be thorough, that you know how to write – all those things are extremely important. So, if you're thinking about building a career in Data Science, only a small part of it depends on how well you can program on a daily basis. A lot of it also depends on your ability to solve problems, communicate, to be persistent even when things are frustrating, and that's kind of the hallmark of a PhD or any advanced degree. That you're able to work through complexity and through things that are challenging and to still find a solution. And that’s Data Science more than any other thing. There's a focus on the technical statistics, SQL, coding, etc. that are certainly important. These are skills and some of the bigger things that will make you successful and allow you to build a career.
It's fascinating when you think about what allows people to grow in their careers. Typically, it's related to finding something that you're passionate about or super interesting to you, and you'd like to work on solving problems in that area. Very few people in Data Science end up loving it because they like to program. It's more about the problems they work on, the people they work with, and the impact they have. So, if you think about your previous career or having an advanced degree in another field, use that, because it's going to be very beneficial for you as you think about going into the world of Data Science.
I know it can be a little challenging to think, “I worked so hard for this degree…” or “I have 10 years of experience in this other field…”. Experience is important. You just need to think about how you can incorporate that into your professional development. Data Science helps you learn about the right skills. The mindset and the way you solve problems makes the big difference in the end.”
Interested in reading more about Constructor Learning and tech related topics? Then check out our other blog posts.