"A good software developer (or engineer) and a good data scientist have several traits in common. Both are good at designing and building complex systems with many interconnected parts; both are familiar with many different tools and frameworks for building these systems; both are adept at foreseeing potential problems in those systems before they’re actualized. But in general, software developers design systems consisting of many well-defined components, whereas data scientists work with systems wherein at least one of the components isn’t well defined prior to being built, and that component is usually closely involved with data processing or analysis." (Brian Godsey, "Think Like a Data Scientist", 2017)
"A notable difference between many fields and data science is
that in data science, if a customer has a wish, even an experienced data
scientist may not know whether it’s possible. Whereas a software engineer
usually knows what tasks software tools are capable of performing, and a
biologist knows more or less what the laboratory can do, a data scientist who
has not yet seen or worked with the relevant data is faced with a large amount
of uncertainty, principally about what specific data is available and about how
much evidence it can provide to answer any given question. Uncertainty is,
again, a major factor in the data scientific process and should be kept at the
forefront of your mind when talking with customers about their wishes."
"The process of data science begins with preparation. You need to establish what you know, what you have, what you can get, where you are, and where you would like to be. This last one is of utmost importance; a project in data science needs to have a purpose and corresponding goals. Only when you have well-defined goals can you begin to survey the available resources and all the possibilities for moving toward those goals." (Brian Godsey, "Think Like a Data Scientist", 2017)
"Uncertainty is an adversary of coldly logical algorithms, and being aware of how those algorithms might break down in unusual circumstances expedites the process of fixing problems when they occur - and they will occur. A data scientist’s main responsibility is to try to imagine all of the possibilities, address the ones that matter, and reevaluate them all as successes and failures happen." (Brian Godsey, "Think Like a Data Scientist", 2017)
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