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The Rise of the Julia Programming Language

Zacharias Voulgaris, PhD joins the show to talk about his experience with the Julia programming language and his perspective on the future of Julia’s widespread adoption. 

Oct 2022
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Key Quotes

Don't just rely on what you learn theoretically or intellectually. Instead, build some muscle memory around it. The only way to really learn Julia is to find some interesting problem that you want to tackle, even if it's already been solved either by you or someone else, and try to solve it in Julia and see how it goes. See where you get stuck and figure out how you can get unstuck. Talk to other people, even if it's in a problem-solving fashion, to see how you can tackle different challenges in Julia. This is how you will gain a deeper  and more effective knowledge.

I understand the fears that Julia’s ecosystem is not big enough. It’s easier to bet on a language that is more established. There are more people and more libraries. But thirty years ago, Python was very new. Would you have thought of investing in Python back then? And if everybody regarded Python with those same fears, nobody would have ever shifted from Fortran, C, or Java. So, the fact that there are new languages gaining popularity shows that there are people willing to take a chance on something new. In the case of Julia, even if it's not as big an ecosystem as other more established languages, I think the risk is minimal because of the bridge libraries from other languages, and partly because it is a growing and very active community.

Key Takeaways


You can migrate code from other programming languages in Julia, which allows teams to adopt Julia through a gradual process instead of having to plan a sudden transition.


The best way to learn Julia is to actively use it. Identify a problem you want to tackle, even if it’s already been solved, and try to solve it using Julia. This process will help you gain a deeper understanding of the language and how to properly utilize it.


When trying to convince leaders to adopt Julia or any other programming language, you need to demonstrate the value, ease of use, and ease of transition through examples that can be compared to your organization’s current language of choice.

About Zacharias Voulgaris

Photo of Zacharias Voulgaris
Zacharias Voulgaris

Zacharias Voulgaris, PhD, is the author of Julia for Data Science. As a Data Science consultant and mentor with 10 years of international experience that includes the role of Chief Science Officer at three startups, Zacharias is an expert in data science, analytics, artificial intelligence, and information systems.

Photo of Richie Cotton
Richie Cotton

Richie helps organizations get from a vague sense of "hey we ought to get better at using data" to having realistic plans to become successful data-driven organizations. He's been a data scientist since before it was called data science, and has written several books and created many DataCamp courses on the subject.


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