Last Thursday, my co-instructor and I showed up to the Data Visualization course we teach at the University of Washington with a bag of rocks. The bag consisted of a fairly diverse collection that I myself put together across a set of treks in various regions of California. Our students are fairly used to the quirky, hands-on activities we ask them to participate in most classes, but this seemed a bit out there, even for us. In this article, I’ll focus on the following two points, which collectively speak to the importance of domain-specific integration into data science Education: A description of the actual task we had students do with these rocks. A deep dive into the discussion that followed—which largely focused on the point of making them do this and its deeper connections to data science. What to Do with a Bunch of Rocks? Once the students were seated in their respective groups, we asked them to do the following: Choose two rocks per group. Attempt to formally identify the rocks without the aid of any internet or mobile apps. At this point, most students made it as far as determining if a rock appeared to be igneous, sedimentary, or metamorphic. Refine their initial guesses by now taking advantage of their electronic resources. Students now got much more specific, identifying scoria, slate, red jasper, gneiss, and a host of other rocks in the collection. Design and implement a chart (using software or on paper) that either compared the qualities of their rocks or displayed engaging information about one of them. They were encouraged to search online for supporting data, such as hardness, mineral makeup, potential uses, and so on. Once finished, they submitted their visualizations to us, and we proceeded with a class discussion. What Do Rocks Have to Do With Data Science? Quite a bit, as it happens. As we went around the room, students shared a host of insights about their various rocks. In many cases, the discussion focused on the utility of a particular visual approach students had taken. For example, one group chose to compare their two rocks via a data table that included various points of relevant information. This led to a discussion on how data tables are in fact a type of data visualization, especially useful in two situations: When you have a limited amount of data When it is important that the user be able to pick out precise pieces of data for their purposes Other conversations revolved around the effectiveness of area as an encoding, the particularities of color scales, and so on. All standard discussions for a data visualization course. Once we finished this initial conversation, I posed a more involved question for the class: “So far, we’ve talked about standard visual elements of a chart. We could have discussed these with any kind of data. So why go to the trouble of bringing a giant bag of rocks to the class and asking you to identify them? What’s the point?” The class stared blankly. The moment dragged. Then, one student hesitantly raised his hand. “Um … so we can get comfortable working with unfamiliar domains, or something like that?” Precisely! We’d mentioned this point sparingly to the students before, but this activity really drives the point home. As eventual designers and engineers working in data visualization—and, more broadly, in data science, it is essential for these students to know how to work with domains they may be unfamiliar with. The same goes for you if you are reading this article. As the data expert on a team, you will rarely also be the domain expert, and you must adjust to the data given to you. Sometimes quite quickly. In a previous article, “The Three Building Blocks of Data Science,” I dove into this point in greater detail. The first two building blocks—statistics and computer science—are incredibly important. That said, the actual data comes from the domain. Without the domain, there would be no need for data science. As a data scientist, while you will have the support of a domain expert, you will still need to design solutions and write code corresponding to data you may be deeply unfamiliar with. As such, it is incredibly important to gain exposure to this reality as part of one’s data science education. My co-instructor and I teach in a design and engineering department, with students largely interested in pursuing fields such as UI/UX research and data engineering. We chose to make them work with rocks precisely because we knew they were unlikely to know too much about them (at least at the level of detail needed) beforehand. And that lack of prior knowledge made all the difference. Final Thoughts If you’re reading this, I’m guessing you’re training to be a data scientist, or interested in doing so. Perhaps you already are one and are just rounding out your knowledge. Whatever your position may be, my point remains the same: Every chance you get, expose yourself to new data. By its very nature, literally every field, every discipline, every topic known to man has some kind of data, and an associated group of people interested in gaining insights about it. And the person they turn to for help might just be you. The post I Teach Data Viz with a Bag of Rocks appeared first on Towards Data Science.

Researchers reviewed ocean floor samples collected during the Deep Sea Drilling Project in 1975. Credit: Deposit Photos / Oleg Dorokhin Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. There was a time long ago when the Atlantic Ocean didn’t exist. The general understanding among geologists is that the body of water originated between 83 to 113 million years ago, when South America and Africa split into their two respective continents to form the Equatorial Atlantic Gateway. However, Earth’s marine history appears to require a multimillion-year revision thanks to a recent discovery roughly half a mile beneath the ocean floor. The evidence is explored in a study published in the June edition of the journal Global and Planetary Change. According to geologists at the UK’s Heriot Watt University, gigantic waves of mud and sand sediment about 250 miles off the coast of Guinea-Bissau in West Africa indicate the Atlantic Ocean actually formed around four million years earlier than previous estimates. To understand just how intense all of this movement was, imagine waves that are about half a mile long and over 300 feet high.  “A whole field formed in one particular location to the west of the Guinea Plateau, just at the final ‘pinch-point’ of the separating continents of South America and Africa,” study co-author Uisdean Nicholson explained in a statement. Nicholson and their colleagues initially came across these layers of mud waves after comparing seismic data with core samples collected from wells during the Deep Sea Drilling Project (DSDP) of 1975. Five layers in particular were utilized to recreate the tectonic processes that broke apart the ancient supercontinent of Gondwana during the Mesozoic Era. “One layer was particularly striking: it included vast fields of sediment waves and ‘contourite drifts’—mud mounds that form under strong bottom currents,” said Nicholson. These waves initially formed as dense, salty water poured out from the newly created Equatorial Atlantic Gateway, “like a giant waterfall that formed below the ocean surface,” he added. Just before the geologic event, huge salt deposits formed at the bottom of what is now the South Atlantic. After the gateway opened, the underground mudfall occurred when dense, relatively fresh Central Atlantic water in the north combined with very salty waters in the south. The resulting sedimentary evidence examined by the study’s authors now indicates this opening seems to have started closer to 117 million years ago. “This was a really important time in Earth’s history when the climate went through some major changes,” explained study co-author Débora Duarte. “Up until 117 million years ago, the Earth had been cooling for some time, with huge amounts of carbon being stored in the emerging basins, likely lakes, of the Equatorial Atlantic. But then the climate warmed significantly from 117 to 110 million years ago.” Duarte and Nicholson believe part of that major climatic change  helped from the Atlantic Ocean, as seawater inundated the newly formed basins. “As the gateway gradually opened, this initially reduced the efficiency of carbon burial, which would have had an important warming effect,” said Duarte. “And eventually, a full Atlantic circulation system emerged as the gateway grew deeper and wider, and the climate began a period of long-term cooling during the Late Cretaceous period.” The ramifications go beyond revising Earth’s geological timeline or the gateway’s role in Mesozoic climate change. Better understanding the influence of oceanic evolutionary journeys on ancient climate patterns can help to predict what the future holds for the planet.  “Today’s ocean currents play a key role in regulating global temperatures,” explained Nicholson. “Disruptions, such as those caused by melting ice caps, could have profound consequences.”