You can define many roles. As many as anyone who writes an article gives their opinion on the subject.
As a paradigm development team in the Aura project, we will give our humble opinion trying to break down the roles in a few, based on the two ideas we have taken at the beginning of the article: data storage/processing and analysis of these.
It’s the “evolution of the Data Analyst”. In many cases, they are considered the same profile with a different approach. For us, it is a more specific role and less aligned with the business vision.
Like the DA, it requires to know mathematics, statistics and Machine Learning, of programming languages like R or Python, or use of notebooks and ecosystems big data, but what we think that differentiates the data Scientist is that it is in charge to take value to the data S.
It also gets, processes, and visualizes but has a more focused role in predicting based on learned behaviors.
Considering the data Scientist as a more modern version of the Data Analyst, it is more typical of them to use newer libraries like Tensor Flow for Deep Learning techniques based on neural networks.
Also, many of its developments are linked to techniques of Artificial intelligence and neuron-linguistic programming (NLP). But, once again, they are quite similar profiles, and the inclusion of technologies is not strict for one role or another.
Focusing first on the most data-oriented profiles, the data Analyst is a pre-profile to data Scientist. They are even called in some cases “Data Scientist Junior”.
They have a fairly general role, covering a wide range of functions including mining, data collection and/or retrieval as well as processing, advanced study and visualization.
The Advanced study or analysis of the data is done based on mathematical and statistical algorithms and methods. Therefore, this profile requires mainly knowledge of mathematics and statistics applied to data mining and automatic learning or Machine learning.
The latter makes it also essential to know how to program (at least in current projects). Although its specialty is the Machine Learning, the use of libraries of statistical methods like that of pandas requires to know below the operation of each algorithm, as well as the basic functionality of the corresponding language, in this case, Python. Another common language for the data Analyst could be R.
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