Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.
This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.