"A mockup shows what we should expect to take away from a project. In contrast, an argument sketch tells us roughly what we need to do to be convincing at all. It is a loose outline of the statements that will make our work relevant and correct. While they are both collections of sentences, mockups and argument sketches serve very different purposes. Mockups give a flavor of the finished product, while argument sketches give us a sense of the logic behind the solution." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"A very powerful way to organize our thoughts is by classifying each point of dispute in our argument. A point of dispute is the part of an argument where the audience pushes back, the point where we actually need to make a case to win over the skeptical audience. All but the most trivial arguments make at least one point that an audience will be rightfully skeptical of. Such disputes can be classified, and the classification tells us what to do next. Once we identify the kind of dispute we are dealing with, the issues we need to demonstrate follow naturally." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"All stories have a structure, and a project scope is no different. Like any story, our scope will have exposition (the context), some conflict (the need), a resolution (the vision), and hopefully a happily-ever-after (the outcome). Practicing telling stories is excellent practice for scoping data problems." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Building exploratory scatterplots should precede the building of a model, if for no reason other than to check that the intuition gained from making the map makes sense. The relationships may be so obvious, or the confounders so unimportant, that the model is unnecessary. A lack of obvious relationships in pairwise scatterplots does not mean that a model of greater complexity would not be able to find signal, but if that’s what we’re up against, it is important to know it ahead of time. Similarly, building simple models before tackling more complex ones will save us time and energy." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Contexts emerge from understanding who we are working with and why they are doing what they are doing. We learn the context from talking to people, and continuing to talk to them until we understand what their long-term goals are. The context sets the overall tone for the project, and guides the choices we make about what to pursue. It provides the background that makes the rest of the decisions make sense. The work we do should further the mission espoused in the context. At least if it does not, we should be aware of that." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Data science, as a field, is overly concerned with the technical tools for executing problems and not nearly concerned enough with asking the right questions. It is very tempting, given how pleasurable it can be to lose oneself in data science work, to just grab the first or most interesting data set and go to town. Other disciplines have successfully built up techniques for asking good questions and ensuring that, once started, work continues on a productive path. We have much to gain from adapting their techniques to our field." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Data science is already a field of bricolage. Swaths of engineering, statistics, machine learning, and graphic communication are already fundamental parts of the data science canon. They are necessary, but they are not sufficient. If we look further afield and incorporate ideas from the 'softer' intellectual disciplines, we can make data science successful and help it be more than just this decade’s fad." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Data science is the application of math and computers to solve problems that stem from a lack of knowledge, constrained by the small number of people with any interest in the answers." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Keep in mind that a mockup is not the actual answer we expect to arrive at. Instead, a mockup is an example of the kind of result we would expect, an illustration of the form that results might take. Whether we are designing a tool or pulling data together, concrete knowledge of what we are aiming at is incredibly valuable. Without a mockup, it’s easy to get lost in abstraction, or to be unsure what we are actually aiming toward. We risk missing our goals completely while the ground slowly shifts beneath our feet. Mockups also make it much easier to focus in on what is important, because mockups are shareable. We can pass our few sentences, idealized graphs, or user interface sketches off to other people to solicit their opinion in a way that diving straight into source code and spreadsheets can never do." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Models that can be easily fit and interpreted (like a linear or logistic model), or models that have great predictive performance without much work (like random forests), serve as excellent places to start a predictive task. [...] It is important, though, to not get too deep into these exploratory steps and forget about the larger picture. Setting time limits (in hours or, at most, days) for these exploratory projects is a helpful way to avoid wasting time. To avoid losing the big picture, it also helps to write down the intended steps at the beginning. An explicitly written-down scaffolding plan can be a huge help to avoid getting sucked deeply into work that is ultimately of little value. A scaffolding plan lays out what our next few goals are, and what we expect to shift once we achieve them." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Most people start working with data from exactly the wrong end. They begin with a data set, then apply their favorite tools and techniques to it. The result is narrow questions and shallow arguments. Starting with data, without first doing a lot of thinking, without having any structure, is a short road to simple questions and unsurprising results. We don’t want unsurprising - we want knowledge. [...] As professionals working with data, our domain of expertise has to be the full problem, not merely the columns to combine, transformations to apply, and models to fit. Picking the right techniques has to be secondary to asking the right questions. We have to be proficient in both to make a difference." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"There are four parts to a project scope. The four parts are the context of the project; the needs that the project is trying to meet; the vision of what success might look like; and finally what the outcome will be, in terms of how the organization will adopt the results and how its effects will be measured down the line. When a problem is well-scoped, we will be able to easily converse about or write out our thoughts on each. Those thoughts will mature as we progress in a project, but they have to start somewhere. Any scope will evolve over time; no battle plan survives contact with opposing forces." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"To walk the path of creating things of lasting value, we have to understand elements as diverse as the needs of the people we’re working with, the shape that the work will take, the structure of the arguments we make, and the process of what happens after we 'finish'. To make that possible, we need to give ourselves space to think. When we have space to think, we can attend to the problem of why and so what before we get tripped up in how. Otherwise, we are likely to spend our time doing the wrong things." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
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