Not everyone in the industry agrees on this, but here's my take: If you are applying the scientific method, then you're doing science. Some data scientists do this often: peforming controlled experiments or quasi-experiments using large volumes of data to gain new knowledge. ML is sometimes in this category, and sometimes not. We also do engineering: formulating problems, defining resources and constraints, and designing/building solutions within that space. IMO the Science label is reasonable, but can't realistically be applied to every DS job description. More on reddit.com

The main difference is that data science is focused on analyzing and interpreting data to generate insights, while machine learning is more about building models that can make predictions or decisions from that data. Data scientists often use ML tools as part of their workflow, but ML engineers usually go deeper into model development and deployment. If you want a good starting point that gives you exposure to both, I’d recommend checking out this article . It breaks down IBM’s Introduction to Data Science course step by step and includes hands-on exercises, which makes it easier to figure out whether you’d rather lean toward data science or machine learning in the long run. More on reddit.com

Somewhere around $50k More on reddit.com

I have to say, I think Data engineering is a better field in many ways than data science. I say this AS a data scientist. Demand FAR outstrips supply, and its not as easy to teach people to be a great data engineer vs Masters students printing out data scientists nowadays.

It's a very broad set of skills, but as long as you have the core competencies, people will overlook a large number of gaps tbh.

In my view of the world, data engineers get paid more too (consulting) frequently than data scientists.

From a technical perspective, a unicorn (and I really stress that) data engineer:

• DevOps skills (e.g. IaC, CI/CD, Jenkins, unit testing, etc)

• Cloud-native architecture (specifically around the data space)

• Docker, Kube

• Spark

• Kafka

• Hadoop knowledge (not extensively, I think its a dying pattern, rightly)

• data modelling

• aligned to company tech stack across scala/python/Java

• Event driven architecture

• Airflow

• SQL! Both using it as a user, and designing efficient queries and data models

I'm sure more tools hace cropped up in the meantime too, but its a good start

I'm separating out MLOps/ML Engineering as a separate resource, fwiw

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