Understanding different roles in data science; Who does what?

Data Analyst

A data analyst builds systems that collect and examine data, then extract insights to answer business questions with actionable solutions. One must have great communication and collaborative skills, especially when explaining ideas to non-technical people.

Data Analyst: Responsibilities, skills, and programming language required
Generally responsible for accessing data, performing statistical analysis, and visualizing and communicating the results.
Proficiency in R/Python; SQL.
Good knowledge of probability and statistics.

Data Scientist

The data analyst’s role has been taken a step further. A data scientist is someone who is responsible for building machine learning models and working with algorithms to make accurate predictions on data that has been collected.

Data Scientist: Responsibilities, skills, and programming language required

Generally responsible for analyzing data, and building & training machine learning models.
Proficiency in R/Python(based on requirement)
Must have a strong foundation in maths, stats, and ML methods.

ML Engineer

ML Engineer: While a data analyst determines data worth exploring and a data scientist builds predictive models on top of that data, it’s the ML Engineer who puts these models into production using his mix of knowledge on engineering fundamentals and machine learning.

ML Engineer: Responsibilities, skills, and programming MLE is responsible for building production-grade ML pipelines(MLOps)
Proficiency in R/Python; JAVA/C++ Robust understanding of Data structures, Vector algebra, system design, and big data: Apache Spark (for Data Engineer)

By now, you must have observed a major skill set overlap among these roles, which is true. In reality, people tend to perform multiple roles based on project/business requirements. My suggestion here is: You start with what suits you currently and gradually broaden your skills.

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