Which career path: Data Analyst or Data Scientist?

I’m leaning more toward the data scientist path. I’ve been a financial analyst for some time now. That has included building financial models in Excel. I want to keep my value by enhancing my skill set. Has anyone taken either of these two career paths (or both) here at Code Academy? What was your experience?

Hey and welcome to the forums!

If you’re trying to decided between the two I’d recommend taking a read through this article on the Codecademy blog, it explains the major differences between the two roles:
https://www.codecademy.com/resources/blog/data-analyst-vs-data-scientist/

The condensed version is pretty much that data science is a much broader field (which data analysis is a subset of) and the DS path includes things like machine learning/natural language processing which the DA path does not (so it’s a little shorter to take the DA path). The data analysis path on the other hand is all about teaching you to take data, process it and leverage it to make data-driven decisions, which sounds to be more similar to your current role.

Happy coding! :slight_smile:

1 Like

Hey @ruby6028313642
I’ve taken the data analyst and data scientist path on Codecademy. Additional, I have worked in startups under both these roles( more specifically I’ve worked as a business data analyst and data scientist- both roles at product companies). Here are a couple of observations I made:
Analyst roles involve a lot of querying and more importantly appropriately relaying that information to other teams and maybe executives of the company depending on the level of your role. The storytelling aspect is essential and I noticed that being able to tie in data inferences to business metrics or business problem statements is very valuable.
As a data scientist, there is a lot more experimentation. At companies like Google, data scientists are heavily involved in A/B experimentation. So there is more math and design involved in those roles. Some data science roles also involve a lot of machine learning. Being able to structure a machine learning pipeline in that sense becomes more important.

Personally, I enjoy the data science role more because there is more scope for experimentation and testing compared to the analyst role.

Hope this is somewhat useful!

3 Likes

Great feedback. Thank you.