What is software engineering for data science?

18 May 2016 By Roger Peng

Editor’s note: This post is a chapter from the book Executive Data Science: A Guide to Training and Managing the Best Data Scientists, written by myself, Brian Caffo, and Jeff Leek.

Software is the generalization of a specific aspect of a data analysis. If specific parts of a data analysis require implementing or applying a number of procedures or tools together, software is the encompassing of all these tools into a specific module or procedure that can be repeatedly applied in a variety of settings. Software allows for the systematizing and the standardizing of a procedure, so that different people can use it and understand what it’s going to do at any given time.

Software is useful because it formalizes and abstracts the functionality of a set of procedures or tools, by developing a well defined interface to the analysis. Software will have an interface, or a set of inputs and a set of outputs that are well understood. People can think about the inputs and the outputs without having to worry about the gory details of what’s going on underneath. Now, they may be interested in those details, but the application of the software at any given setting will not necessarily depend on the knowledge of those details. Rather, the knowledge of the interface to that software is important to using it in any given situation.

For example, most statistical packages will have a linear regression function which has a very well defined interface. Typically, you’ll have to input things like the outcome and the set of predictors, and maybe there will be some other inputs like the data set or weights. Most linear regression functions kind of work in that way. And importantly, the user does not have to know exactly how the linear regression calculation is done underneath the hood. Rather, they only need to know that they need to specify the outcome, the predictors, and a couple of other things. The linear regression function abstracts all the details that are required to implement linear regression, so that the user can apply the tool in a variety of settings.

There are three levels of software that are important to consider, going from kind of the simplest to the most abstract.

1. At the first level you might just have some code that you wrote, and you might want to encapsulate the automation of a set of procedures using a loop (or something similar) that repeats an operation multiple times.
2. The next step might be some sort of function. Regardless of what language you may be using, often there will be some notion of a function, which is used to encapsulate a set of instructions. And the key thing about a function is that you’ll have to define some sort of interface, which will be the inputs to the function. The function may also have a set of outputs or it may have some side effect for example, if it’s a plotting function. Now the user only needs to know those inputs and what the outputs will be. This is the first level of abstraction that you might encounter, where you have to actually define and interface to that function.
3. The highest level is an actual software package, which will often be a collection of functions and other things. That will be a little bit more formal because there’ll be a very specific interface or API that a user has to understand. Often for a software package there’ll be a number of convenience features for users, like documentation, examples, or tutorials that may come with it, to help the user apply the software to many different settings. A full on software package will be most general in the sense that it should be applicable to more than one setting.

One question that you’ll find yourself asking, is at what point do you need to systematize common tasks and procedures across projects versus recreating code or writing new code from scratch on every new project? It depends on a variety of factors and answering this question may require communication within your team, and with people outside of your team. You may need to develop an understanding of how often a given process is repeated, or how often a given type of data analysis is done, in order to weigh the costs and benefits of investing in developing a software package or something similar.

Within your team, you may want to ask yourself, “Is the data analysis you’re going to do something that you are going to build upon for future work, or is it just going to be a one shot deal?” In our experience, there are relatively few one shot deals out there. Often you will have to do a certain analysis more than once, twice, or even three times, at which point you’ve reached the threshold where you want to write some code, write some software, or at least a function. The point at which you need to systematize a given set of procedures is going to be sooner than you think it is. The initial investment for developing more formal software will be higher, of course, but that will likely pay off in time savings down the road.

A basic rule of thumb is

• If you’re going to do something once (that does happen on occasion), just write some code and document it very well. The important thing is that you want to make sure that you understand what the code does, and so that requires both writing the code well and writing documentation. You want to be able to reproduce it down later on if you ever come back to it, or if someone else comes back to it.
• If you’re going to do something twice, write a function. This allows you to abstract a small piece of code, and it forces you to define an interface, so you have well defined inputs and outputs.
• If you’re going to do something three times or more, you should think about writing a small package. It doesn’t have to be commercial level software, but a small package which encapsulates the set of operations that you’re going to be doing in a given analysis. It’s also important to write some real documentation so that people can understand what’s supposed to be going on, and can apply the software to a different situation if they have to.