Hi guys. Looks like we have a topic. What's interesting? I'd be happy to start. How would you define the difference between a Data scientist and a Data Analyst (or Product Analyst) at your company?
Data scientists are at least 3/5 in each of: - statistics - practical machine learning - theoretical machine learning - software engineering - communication And good ones 4+/5 in two of them. Data scientists help define and solve your most significant problems. Data analysts solve predefined problems using clean data.
First answer says data analysts should be stuck cleaning data. This answer says data analysts can only work with clean data :D
^This is the kind of shit I'd expect from an economics app
OP asked about how it works where we work. Both are probably correct :)
At Etsy, DS is more like machine learning engineering working only on search and recommendation algorithms. Data Analysts do everything else: simple & more complex data/statistical analysis, predictive modeling for non-search/recommendation use cases, experiments, analysis and reporting for business decision making etc
Data analysts are like customer support at our company. Data scientists are more like engineers
Lol. At Databricks, of course.
In Fintech DS work with algorithms and ML and DS are bank data standards experts, they clean data and do all data mapping tasks
My perspective is that a data scientist is focused on using disparate and often large data sets to solve business problems and develop insights and visualizations using data mining and machine learning techniques. A data analyst is focused on more traditional statistical and analytic techniques in more established and traditional roles (actuarial, etc)
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A Data scientist wastes time and money doing things a Data analyst should - "cleaning" data. But really biggest difference is supposed to be in ability with high level math and programming.
lmao what?
Sounds like the perspective of someone who didn't get the job! :P