Julia is a high level, dynamic programming language built to be as fast as C or C++ while remaining as easy to use as Python. For data scientists, this is a computational dream come true.
In this post, we will talk about the following topics with the goal being to convince a data scientist that the Julia ecosystem is worth investing time into. At a high level, the main reason to switch to Julia (or use it to suppliment existing workflows) is the productivity it enables for developers. Who doesn’t love being able to work more effectively?!
Topics we will cover:
- Julia use-cases 🧑💻
- Data Science packages 🤖
- Interoperability 🔀
- Speed ⚡️
- Learning Resources 📚
To find out more, check out the full medium post I wrote up here: https://medium.com/@logankilpatrick/why-you-should-invest-in-julia-now-as-a-data-scientist-30dc346d62e4.
Top comments (3)
@suvojitbarick check this.
@ifihan let me know what you think of this!
This is nice! I'd like to write on this with you too. I'm almost done with writing on one of the topics stated there too!