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More than Just Charts and Graphs: What to Teach in a Data Visualization Course

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Title: More than Just Charts and Graphs: What to Teach in a Data Visualization Course
Language: English
Authors: Camm, Jeffrey D. (ORCID 0000-0002-9619-6348), McCray, Gordon E., Roehm, Michelle L.
Source: Decision Sciences Journal of Innovative Education. Jul 2023 21(3):112-122.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 11
Publication Date: 2023
Document Type: Journal Articles
Reports - Descriptive
Education Level: Higher Education
Postsecondary Education
Descriptors: Visual Aids, Masters Programs, Business Administration Education, Data Use, Course Content, Program Development
DOI: 10.1111/dsji.12282
ISSN: 1540-4595
1540-4609
Abstract: Based on our experience developing and delivering a highly successful data visualization course within a Master of Science in Business Analytics program, we present a taxonomy for data visualization courses and recommend content and pedagogical features for each type of data visualization course therein. We also discuss the interdependence between data visualization and business communication as a critical consideration in course design.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1386175
Database: ERIC
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  Value: <anid>AN0168591587;q1n01jul.23;2023Aug01.06:13;v2.2.500</anid> <title id="AN0168591587-1">More than just charts and graphs: What to teach in a data visualization course </title> <p>Based on our experience developing and delivering a highly successful data visualization course within a Master of Science in Business Analytics program, we present a taxonomy for data visualization courses and recommend content and pedagogical features for each type of data visualization course therein. We also discuss the interdependence between data visualization and business communication as a critical consideration in course design.</p> <p>Keywords: analytics; course design; data visualization; storytelling</p> <hd id="AN0168591587-2">INTRODUCTION</hd> <p>Remarkably strong industry demand for analytics professionals continues apace. Following a precipitous but brief decline toward the beginning of the COVID‐19 pandemic, the number of unique job advertisement postings containing the keyword "analytics" increased by nearly 145% from March 2020 to March 2022 (Emsi Burning Glass Technologies, [<reflink idref="bib3" id="ref1">3</reflink>]). Academia has responded to this demand for analytics talent by creating a myriad of analytics degree and nondegree programs targeting an array of student audiences. The landscape of academic programmatic offerings comprises bachelor's degrees, master's degrees, doctoral degrees, certificate programs, executive education offerings, and continuing education opportunities. Consider, for example, that the number of master's programs in analytics and data science has grown from approximately 100 in 2014 to more than 300 in 2022 (Rappa, [<reflink idref="bib9" id="ref2">9</reflink>])—and yet schools of business still are not fully meeting market demand for talent (Schroeder, [<reflink idref="bib10" id="ref3">10</reflink>]).</p> <p>Making the phenomenon of rapid growth in demand for analytics talent even more interesting, the pandemic accelerated movement into online course delivery for most institutions of higher learning. As a result, a number of analytics offerings are now available in a variety of delivery modalities. Sensing an uncommon opportunity to evolve degree and nondegree portfolios, many schools of business have introduced analytics offerings with a swiftness that is rare in business higher education. Though an admirable response to rapidly changing market demand, such alacrity can sometimes catch faculty members flat‐footed as they attempt to quickly pivot or evolve their knowledge bases and instructional methods to meet these new programmatic offerings. Unsurprisingly, a significant proportion of analytics programs include at least one data visualization‐like course. In August 2021, we conducted a curriculum audit of master's degree data analytics programs. We were able to find course titles for 83 business analytics programs. The data revealed that 36 (43%) require a course focused on data visualization. Hence, this article.</p> <p>Of course, it is the targeted learning outcomes associated with such a course that are of primary importance, as well as any pedagogies that are particularly well suited to the achievement of these targeted outcomes. Because of the paucity of actual degree programs in data visualization, rare are instructors with the broad multidisciplinary background and training from which data visualization so profoundly benefits. And so, the instructor assigned to teach—and perhaps also develop—a course in data visualization likely faces a foreign landscape rife with opinions and casually offered lists of heuristics and best practices, but sorely lacking in rigorous guidance.</p> <p>Our article is motivated by a former colleague of the first author who, nearly ten years ago, when the first author was strongly advocating for the addition of a data visualization course into a master's program in analytics, asked "Isn't this just charts and graphs?" Certainly, data visualization does involve charts and graphs. More accurately, though, it is concerned with human perception of, and reaction to, the visual representation of data and information. This implies, therefore, that we are ranging into both the business context (from whence the reason for crafting a visualization emanates and serves as the context for its consideration by the viewer) and cognitive psychology (which is a useful lens through which to understand how and why humans respond to visual stimuli in the ways we do), among several other secondary and tertiary lenses that can be applied, depending upon the depth, focus, and rigor of a course on data visualization.</p> <p>What content, then, is most appropriate for a course in data visualization? On what does our answer to that broad question depend? In an attempt to shed light on these fundamental questions, Bowers et al. ([<reflink idref="bib2" id="ref4">2</reflink>]) used text mining to study over 600,000 job advertisements together with data on academic programs in analytics and data science. It was found that more than two‐thirds of the job advertisements mentioned terms related to communication and interpersonal skills; in master's programs, less than 5% of required credits were dedicated to courses focused on interpersonal skills. In a similar study of job advertisements and curricula (though focusing exclusively on business analytics to the exclusion of data science per se), Seal et al. ([<reflink idref="bib11" id="ref5">11</reflink>]) pointed to a similar gap. These researchers report that competencies such as communication skills, team skills, data reporting, database tools, and spreadsheets "<emph>are being taught in a lower percentage of the programs compared with their demand levels in job ads</emph>." Stackpole ([<reflink idref="bib13" id="ref6">13</reflink>]) agrees that achieving influence through the use of data can be problematic for analytics professionals: "<emph>Countless organizations are dialing up analytics to turn the glut of enterprise data into actionable business insights. But many of the endless charts, dashboards, and visualizations fall flat with their intended audience. Sometimes it's a matter of overwhelming recipients with too much data; other times, it's about presenting the wrong data or not fully understanding how to create an effective narrative that will resonate with recipients</emph>." Clearly, there is a gap between the talent profile sought by industry and the talent profile provided by business higher education in the analytics and data science space.</p> <p>Different approaches have been taken to close the chasm. Varying roles of data visualization courses found across institutions and academic programs are reflected in the varying titles given to those courses. Our survey of 36 existing data visualization‐focused courses revealed <emph>22</emph> distinct course titles (see Table 1). Content analysis of syllabi and other available descriptive information suggests three broad categories of data visualization courses:</p> <p></p> <ulist> <item> Data visualization—exclusively focused on the design/construction of visual representations of data and information (i.e., charts and graphs); there were 16 such courses (44.4%) in our survey of courses.</item> <p></p> <item> Data visualization paired with a technical topic—the design/construction of visual representations of data and information considered within the context of other topics such as data acquisition, data management, data warehousing, exploratory data analysis, and data mining; there were 12 such courses (33.3%) in our survey of courses.</item> <p></p> <item> Data visualization paired with topics aligned with communication—the design/construction of visual representations of data and information considered within the context of other topics like influencing, persuasion, public speaking, and storytelling. There were eight such courses (22.2%) in our survey of courses.</item> </ulist> <p>1 TABLE Titles of data‐visualization‐related courses in a sample of business analytics masters programs</p> <p> <ephtml> <table><tbody><tr><td>Communicating & Visualizing Data</td><td>Data Warehousing and Visualization</td></tr><tr><td>Data Acquisition, Preparation and Visualization</td><td>Exploratory Data Analysis and Data Visualization</td></tr><tr><td>Data Exploration and Visualization</td><td>Exploratory Data Analytics and Visualization</td></tr><tr><td>Data Management and Visual Analytics</td><td>Information Visualization</td></tr><tr><td>Data Management and Visualization</td><td>Information Visualization & Communication</td></tr><tr><td>Data Mining and Visualization</td><td>Information Visualization for Business</td></tr><tr><td>Data Visual Analysis and Visualization</td><td>Interactive Data Visualization</td></tr><tr><td>Data Visualization</td><td>Statistical Computing and Data Visualization</td></tr><tr><td>Data Visualization & Reporting</td><td>Visual Analytics and Influencing</td></tr><tr><td>Data Visualization & Story Telling</td><td>Visualization and Persuasion</td></tr><tr><td>Data Visualization and Communications</td><td>Visualizing Information</td></tr></tbody></table> </ephtml> </p> <p>Assuming a course focused on data visualization has found its way into a curriculum, the question, of course, is whether one of these three approaches is superior to the other two. Does one align better with the needs of industry? Does one appeal more to prospective and enrolled students? Are there more compelling synergies across content in one as compared to the other two? The answer is that context matters. What is the role of the course within the broader curriculum? In what industries and in what roles do graduates tend to pursue their careers and what does this imply for the program's data visualization learning goals?</p> <p>In pondering the positioning of the course, it is also useful to consider that one can categorize data visualization courses into two broad categories based on the intended use of visualizations themselves: data visualization for <emph>exploring</emph> or data visualization for <emph>explaining</emph> (Camm et al., [<reflink idref="bib4" id="ref7">4</reflink>]). That is, will a visualization be used as a means of exploring a data set in a quest to uncover interesting relationships in the data, or will it be used as part of a communication approach in which such insights have already been developed and the purpose, instead, is to share them with other people?</p> <p>The distinction is critical because the majority of visualization design decisions flow from this first and fundamental designation. An optimal design for use in data <emph>exploration</emph> likely is unsuitable (at best) for data <emph>explanation</emph>, and vice versa. Broadly speaking, the designer of data visualizations for data <emph>explanation</emph> must be attendant to far more design guidelines and best practices, and it is with this more challenging context that this article primarily is concerned. How do we design a course that teaches how to craft data visualizations optimized for data <emph>explanation</emph>? And what, if any, other content domains are preferable for pairing with the relevant data visualization content in such a course?</p> <p>Our goals in writing this article are several. First, any course focused on data visualization, regardless of whether it is paired with other content, needs to address the fundamentals of data visualization design. Thus, we make recommendations on coverage of the nature and scope of those fundamentals. Second, we discuss more broadly the aforementioned data visualization course taxonomy and make content recommendations for each. We then explore the interdependence between data visualization and storytelling, types of data visualization courses for various student populations, assessment challenges, and software considerations. The ways in which we have designed and implemented a highly effective course in data visualization for data <emph>explanation</emph> is provided as an example.</p> <hd id="AN0168591587-3">TEACHING BEST PRACTICES IN DATA VISUALIZATION</hd> <p>There is ongoing debate in data visualization circles as to the appropriateness of hewing to hard‐and‐fast rules (i.e., so‐called best practices) in the design of data visualizations. Any set of guidelines put forward for application in the design of data visualizations can be fraught. Are these guidelines exactly that: rules? Or are they suggestions that may or may not be indicated depending upon a myriad of contextual factors? Either way, is there a set of definitive rules that should always be followed?</p> <p>In attempting to answer this question in a way that facilitates the teaching and learning of data visualization, we advocate for conceptualizing data visualization proper as involving not only rules and heuristics, but also a step‐wise <emph>process</emph> intended to guide the data visualization designer through a series of activities that increase the likelihood of achieving optimal visualization design. This process, which is sorely lacking in most readily available resources on data visualization design, borrows from the world of art. In art circles, it is sometimes said that there are two broad approaches to the creation of an artwork: (<reflink idref="bib1" id="ref8">1</reflink>) additive and (<reflink idref="bib2" id="ref9">2</reflink>) reductive. Creation through addition involves the introduction of new elements. For example, the painter who masterfully places (i.e., <emph>adds</emph>) oil paint onto a canvas is exhibiting an additive approach. A sculptor working a large block of marble, on the other hand, removes (i.e., <emph>reduces</emph>) marble until the desired figure or form emerges. So, too, with data visualization. In practical terms, achieving an optimized visualization involves both additive and reductive steps. There are three sequential phases of design activity overall, which we consider in turn.</p> <hd id="AN0168591587-4">Phase 1 (Additive)—creating the initial version of the visualization</hd> <p>This first activity in the design of a data visualization requires familiarity with the data set as well as clarity regarding the purpose of the visualization. With these two prerequisites met, chart type selection can occur. Chart type selection is, as the name implies, a process of considering (<reflink idref="bib1" id="ref10">1</reflink>) the nature of the data with which one is working, (<reflink idref="bib2" id="ref11">2</reflink>) the very specific use to which the visualization will be put, (<reflink idref="bib3" id="ref12">3</reflink>) the sophistication of the consumer of the visualization, and (<reflink idref="bib4" id="ref13">4</reflink>) conventions for visually representing data and information in the specific use case context.</p> <p>There are seemingly innumerable decision aids readily discovered online that, to varying degrees, steer the visualization designer toward the most appropriate chart type. Some decision aids resemble decision trees, while others offer categorization schemes. Some offer exhaustive selections of chart types, while others offer more parsimonious sets of options that focus on the most often or most easily comprehended chart types. Sometimes these aids focus on the nature of the data themselves (i.e., quantitative versus categorical, and cross‐sectional versus time series), while others emphasize the message the visualization is intended to convey (i.e., to show rank order, to show composition, or to show a relationship). For example, a simple single‐variable time series data set might best be represented by a line chart or a column chart. A more complex data set where the goal is to show how composition is changing over time may best be represented by overlayed lines or stacked columns.</p> <p>Which of these available decision aids is best? As any practitioner of data visualization knows, it is a combination of factors that ultimately informs the selection of a specific chart type. For the designer of a data visualization course, perhaps the most important guidance is that the ideal chart type selection aid that applies to all student audiences likely does not exist, and we will not advocate for one in particular; what matters is that students of data visualization design <emph>have</emph> a decision aid. The task confronting the designer of the data visualization course, then, is to choose from the boundless array of readily available aids or to build a custom aid her/himself. Either will work (though the latter obviously will consume more time than the former).</p> <p>Regardless of the aid that is adopted, chart type selection likely will be fraught in the classroom. We believe this is good inasmuch as it confronts common but ill‐informed conventions and common practices, opens a dialogue about design and human cognition, and ultimately allows guided discovery to lead learners toward more thoughtful considerations of this first and vital decision in the design of data visualizations.</p> <p>Consider, for example, a debate that may very well ignite around the utility of pie charts. On the one hand, there is evidence that people are inherently more adept at comparing differences in length than they are at comparing differences between angles (Simkin & Hastie, [<reflink idref="bib12" id="ref14">12</reflink>]). That evidence suggests that we should use bar or column charts rather than pie charts for displaying composition. Some would therefore advocate for replacing all pie charts with bar or column charts. On the other hand, others would argue that pie charts are acceptable if there are just a few categories and there are clear differences in the percentage each category contributes to the whole. Certainly, there are numerous examples of the misuse of pie charts, and both camps would agree you should not use a pie chart unless it involves composition. But whether we would all be better served by simply banning pie charts is an unresolved matter. Where scientific evidence on such matters is equivocal or absent, it is the discussion and debate that contribute most to student learning and development.</p> <p>This is only one example among a landscape littered with opinion and perhaps not enough scientifically based fact. Instructors should advocate for the adoption by learners of the clear and concise guidance provided in whichever chart type selection decision aid the instructor(s) have selected, and then acknowledge that these aids deliver to us a starting point more than the ending point. Context may move the designer away from the recommended chart type for good reasons. But it is vital to stress that chart type selection is not, at base, a matter of opinion or preference. It is rooted in human cognition, and much (but not all) is known about how best to convey various types of messages through the visual representation of data and information. The best among the myriad of decision aids finds their foundation in that one fact.</p> <p>With the chart type determined, the visualization is now created. In this initial design phase, visualization implies the use of data visualization software like Tableau, PowerBI, or even Excel, to create an initial version of the visualization. Sketches created by hand may have been used earlier in this initial design phase, but the second phase requires a digital version on which further design work is undertaken.</p> <hd id="AN0168591587-5">Phase 2 (Reductive)—sanitizing the data visualization</hd> <p>Data visualization pioneer Edward Tufte defined the concept of the <emph>data‐ink ratio</emph> as the ink used to represent <emph>data</emph> in a visualization divided by the <emph>total</emph> amount of ink in the visualization. If we imagine a digitally rendered visualization against a typical white background, we can update this definition to the number of nonwhite pixels used to represent data divided by the total number of nonwhite pixels. The goal, Tufte avers, is a high data‐ink ratio while ensuring the message conveyed via the visualization is clear to the intended audience. A closely related concept is signal‐to‐noise ratio, or SNR, with which some readers may be familiar. The goal is to achieve high SNR, which means that "noisy" visualization elements, which comprise design elements that are not conveying important and useful information, are eliminated.</p> <p>An effective approach to increasing the likelihood of achieving a high data‐ink ratio is to edit the data visualization created at the end of Phase 1, described above. Very simply, the task is to <emph>remove</emph> design elements from the visualization that most visualization software introduces by default. In our experience, learners gaining first exposure to design are reluctant to edit features of a chart or graph that visualization software includes automatically. We therefore have elevated this important step in the design process to the status of its own design "phase" and require its completion before other design considerations are addressed. We sometimes draw a comparison to renovating an older home: often the first step (usually referred to as "demolition" in that context) is to remove all the things you know you do not want to retain. Only when the items and features the owner does not want have been removed, can introduction of new items and features begin in earnest. We might, therefore, refer to Phase 2 as "demolition."</p> <p>What, then, are these design elements that can lower data‐ink ratio? Here, much care must be taken, because the use case context can be important. A design feature that lowers the data‐ink ratio for most visualizations may not lower it for <emph>all</emph> visualizations. Context matters. Still, there are several elements that commonly appear and for which we advocate removal, at least temporarily, until more careful consideration may allow them back into the visualization in Phase 3. A partial list of these offending features includes</p> <p></p> <ulist> <item> gridlines</item> <p></p> <item> excess axes increments</item> <p></p> <item> unnecessary chart and/or axis titling</item> <p></p> <item> excessive data granularity</item> <p></p> <item> excessive use of color</item> <p></p> <item> unnecessary reliance on legends</item> </ulist> <p>This list is partial and may not align fully with every context, as already noted. But these design "features" routinely are introduced by default by visualization software and rarely survive careful design scrutiny. Almost universally, they lower the data‐ink ratio and make the message in the visualization less accessible to viewers.</p> <p>One challenge, of course, is that we are accustomed to seeing these features in the significant majority of visualizations produced in contemporary work settings. These features are accepted without questioning and are assumed appropriate and useful. Indeed, the visualization designer who begins removing them may be challenged by colleagues or managers. Fortunately, there is a scientifically rooted justification for this design phase that points to the way in which the human brain processes visual stimuli. The preconscious memory is performing the initial processing of the data visualization, and "less is more" for that particular brain function. (Specifically, the portion of the brain that performs preconscious data visualization processing is called iconic memory. For more information on this and the Atkinson‐Shiffrin model of memory, see Izawa, [<reflink idref="bib6" id="ref15">6</reflink>].) Very simply, "clean" visualizations are simply more easily comprehended.</p> <p>In this design phase, then, we ruthlessly strip away many of the design elements that were gifted to us by visualization software. Most often, we are left with pixels representing the data themselves (i.e., the line(s) in a line graph, the bars in a bar chart or columns in a column chart), the data points in a scatterplot, and so forth) together with one or two axes with very few axis values indicating range or magnitude. Little to nothing else survives. And of course, any elements that are removed can be reintroduced downstream, but only if it is later determined that, within their use case context, they enhance viewer comprehension or ease of use. Because "less is more" in the processing of data visualizations by the human brain, the bar for reintroduction typically is very high. Once removed during Phase 2, few of these elements find their way back into the visualization in Phase 3.</p> <p>Cole Knaflic, in her popular book <emph>Storytelling with Data</emph> (Knaflic, [<reflink idref="bib7" id="ref16">7</reflink>]), wisely carries this idea to its logical extreme with regard to <emph>color</emph>. She advocates for the removal of all colors except white (as the background color) and gray (for all other design elements) in the early iteration of a visualization. (Here, "color" refers to colors we would commonly consider "bold." Generally speaking, these would be colors other than white and shades of gray.) The prominent use of color, then, is not assumed.</p> <p>The final activity in Phase 2 is the transfer of the data visualization from the software in which it was created into other software that offers superior power and ease of use in evolving it in Phase 3. This recommendation comes as a surprise to many first‐time learners in data visualization. After all, with industrial strength data visualization tools like Tableau and PowerBI, surely everything we would ever desire in a visualization must be easily achievable in these robust tools.</p> <p>Alas, this is not always the case. While it is true that feature‐laden visualization software allows remarkable manipulation of visual representations of data and information, it also is true that the visualizations created fully within these tools can be cumbersome to incorporate into common presentation software. Given the emphasis of this article on data visualizations for <emph>explanation</emph> (rather than for <emph>exploration</emph>), we make the practical assumption that the mode of visually sharing visualizations is through presentation software (most often PowerPoint). It is a matter of practicality, then, to consider how easily a visualization can be refined in Phase 3 and ultimately used in some form of presentation in which presentation software is used. That is the assumed use case herein. Tools like Tableau and PowerBI are remarkably valuable for data <emph>exploration</emph>, but they can present challenges when used to craft visualizations for data <emph>explanation</emph>. Furthermore, simpler tools such as Excel are more than adequate for creating "sanitized" visualizations as described above. In the case of using Excel, there also is the nontrivial benefit of seamless interplay with PowerPoint if that is the presentation software being used. In fact, providers of corporate training on data visualization often offer this same advice: use industrial strength data visualization software for data <emph>exploration</emph> and simpler tools (especially Excel) for data <emph>explanation</emph>. (The matter of corporate dashboards, which resides somewhere between data exploration and data explanation, will be addressed directly.)</p> <p>The astute user may ask, what about data sets that are too large for Excel? The answer is that tools like Tableau and PowerBI offer the ability to aggregate the data from very large data sets into data sets small enough for use in Excel. This is accomplished by crafting a basic version within Tableau of PowerBI of the desired visualization, and then exporting the data associated with that visualization; these data can then be imported into Excel. Tableau and PowerBI generate data that are aggregated and arranged in a way that directly reflects a visualization, which can have the effect of dramatically reducing the amount of data. The details of this technique are beyond the scope of this article, but this is a topic that is explored in our graduate data visualization courses. In all our data visualization courses, we provide exposure to Excel, Tableau, and PowerBI, but we advocate for using the latter two principally for data exploration.</p> <hd id="AN0168591587-6">Phase 3 (Additive)—refining data visualizations</hd> <p>In this third and final phase of the data visualization design process, a wide array of design features is considered for inclusion in the visualization. It is the thoughtful and often clever introduction of these features that sets apart exemplary and highly effective visualizations from the lackluster majority. Effectively educating future generations of visualization developers requires that we teach reasonably robust visualization design guidelines (if not rules) that are far more likely than not to produce highly effective visualizations, <emph>ceteris paribus</emph>.</p> <p>We have experimented with a variety of resources for these guidelines, including the familiar Gestalt principles (see Camm et al., [<reflink idref="bib4" id="ref17">4</reflink>], pp. 38–91), and more than a few seemingly exhaustive yet suspect sets and lists of guidelines proffered by well‐intentioned practitioners. In the end, we required that the guidelines we leveraged in our data visualization courses be (<reflink idref="bib1" id="ref18">1</reflink>) scientifically based and validated, (<reflink idref="bib2" id="ref19">2</reflink>) simple to understand conceptually and easy to remember, and (<reflink idref="bib3" id="ref20">3</reflink>) easy to apply in practitioner settings. The well‐respected and proven "pre‐attentive attributes" met all these conditions exceptionally well and form the basis of data visualization guidelines we use today across multiple audiences. For the newcomer to data visualization, the pre‐attentive attributes allow rapid movement up the learning curve of visualization design. To further support learner progression, we provide a "guide sheet" for the pre‐attentive attributes, with a brief description and visual example for each. On multiple occasions, our working professional students have shared photographs of this Guide Sheet taped to the wall next to their computer screen, which they provide as evidence of the practicality of that resource. In short, the pre‐attentive attributes are an especially useful and easy‐to‐use resource.</p> <p>The pre‐attentive attributes are illustrated in Figure 1. These are visual features that our preconscious mind processes very rapidly when we first receive visual information. Labeling a feature in a data visualization as a pre‐attentive attribute implies that there is a feature in the visualization to which the attention of the preconscious mind will be drawn because in "stands out" from its surroundings. There is something distinctive and notable about it. The meaning of that distinctiveness will be determined later, but the attention will have been focused. Pre‐attentive attributes can therefore be used to <emph>influence</emph> audience members, guiding and directing their attention in ways that support the intended message.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0001.jpg" title="1 Pre‐attentive attributes. Source: Tableau https://help.tableau.com/current/blueprint/en‐us/bp_why_visual_analytics.htm" /> </p> <p></p> <p>We will use a simple popular example to illustrate the power and ease of use of pre‐attentive attributes (see Wexler et al., [<reflink idref="bib15" id="ref21">15</reflink>] or Camm et al., [<reflink idref="bib4" id="ref22">4</reflink>]). Which table in Figure 2 makes it easier to answer the question "How many eights are in the table?" The answer, of course, is Table B, because we have used the pre‐attentive attributes of size, color hue, and color intensity to draw your attention to the eights. The answer is three.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0002.jpg" title="2 Which table makes it easier to count the number of 8s?" /> </p> <p></p> <p>Examples are important in teaching data visualization, and here we turn to another example for the same reason: an illustration of the power of pre‐attentive attributes. Borrowing from an example provided in Camm et al. ([<reflink idref="bib5" id="ref23">5</reflink>]) and based on an exercise in Camm et al. ([<reflink idref="bib4" id="ref24">4</reflink>]), we will move through all three design phases in this example.</p> <p>Suppose we need to present truck sales data to management at Fiat Chrysler. To motivate a change in promotional activity, we need to demonstrate to management that we ranked third in US sales, and we want to ensure that they know the sales volumes for the top five companies in the market.</p> <p>Perhaps because we think it looks impressive, we begin with Chart A in Figure 3, entering Phase 1 of the design process. However, viewed through the lens of the data‐ink ratio heuristic, Chart A has serious problems, including that it is unnecessarily in three dimensions, which adds nothing but irrelevant ink and adversely affects our ability to compare across competitors. If we remove the third dimension, we get Chart B. Our ability to compare is enhanced, but there are still problems. First, the labels are far from the chart, so we have to clumsily move back and forth to relate the pieces of the pie via color. Second, we might ask whether we have selected the optimal chart type. A pie chart would seem to indicate that these are the only truck models sold in the United States, which is untrue. Honda, Hyundai, and others also sell trucks in the US market. Also, as mentioned previously, when the goal is comparison of categories, data presented as a bar or column chart usually is preferable. We will therefore recast the data and select a bar chart as our final mode of presentation.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0003.jpg" title="3 Phase 1 of designing a visualization for the truck sales data" /> </p> <p></p> <p>Chart C in Figure 4 shows a default bar chart as created in common data visualization software. Notice that when we changed from a pie chart to a bar chart, we no longer need to use color to identify categories. This bodes well for our visualization because color can now be used for other things, like drawing attention to a particular brand. Also, comparing the <emph>lengths</emph> of the bars will be much easier for our audience than comparing the <emph>areas</emph> of the slices of the pie in the pie chart.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0004.jpg" title="4 Phase 2 of designing a visualization for the truck sales data" /> </p> <p></p> <p>Phase 2 of visualization design demands that we sanitize the bar chart of the many distracting elements that appear as default features. Thus, we remove gridlines, extraneous titles, and labels. Chart D in Figure 4 illustrates this stripped‐bare version of the chart.</p> <p>As we enter Phase 3, we can apply pre‐attentive principles to highlight key insights. To communicate that Fiat Chrysler Ram ranks third amongst its competitors, we sort the data by volume of sales and put Fiat Chrysler Ram in a defining blue hue, adding bar labels to communicate actual sales volumes to management. Finally, we can add a descriptive chart title that sums up the take‐away. With these revisions, we have our final visualization. Chart E in Figure 5 captures these improvements.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0005.jpg" title="5 Phase 3 of designing a visualization for the truck sales data" /> </p> <p></p> <p>Compare Chart A in Figure 3 with Chart E in Figure 5. Which chart is more effective for conveying our <emph>message</emph> to our audience?</p> <hd id="AN0168591587-12">THE INTERDEPENDENCE OF DATA VISUALIZATION AND COMMUNICATION</hd> <p>Our incorporation of data visualization as a required course in the Master of Science in Business Analytics (MSBA) program at the Wake Forest University School of Business has evolved over time. We have taught, and continue to teach, exploratory data visualization concepts (i.e., the aforementioned visual data <emph>exploration</emph>) within our required technical courses in statistics, data mining and forecasting, and we reinforce this in our required visual analytics and influencing course. Examples include the use of scatter plots for exploring relationships between variables and time series plots for forecasting. The focus of this article, however, remains on our approach to teaching data visualization best practices for data <emph>explanation</emph>. That, of course, puts us squarely in the realm of "communicating with data," an oft‐heard phrase in the world of business analytics.</p> <p>The Wake Forest MSBA program originally required two sequential half‐semester courses with the titles (<reflink idref="bib1" id="ref25">1</reflink>) <emph>Data Visualization</emph> and (<reflink idref="bib2" id="ref26">2</reflink>) <emph>Analytics in the Boardroom</emph>, the former focused on best practices in data visualization and the latter focused on communication and delivering an effective presentation <emph>while heavily leveraging data visualizations developed through the application of the three phases described above</emph>. After teaching these two courses separately several times, we concluded that the achievement of student learning outcomes would be dramatically enhanced through the merging of these two courses into a single semester‐length course. This allowed the rationalization of order of coverage across the full range of relevant topics in both courses, and it offered a rich setting in which to explore and demonstrate the interdependencies across those topics. For example, using data visualization as a tool for communication involves knowing your audience members, knowing the message you want to communicate to your audience, and designing the best visualizations to communicate your message to that audience. Teaching the two courses separately created an artificial and adverse separation of these key components. Even something as clearly delineated as chart type selection was stymied, absent an understanding of the role of the audience profile and broader contextual factors informing the creation of a data visualization in the first place. As noted earlier, context is everything. Hence, the new course entitled <emph>Visual Analytics and Influencing</emph>, which is dramatically more focused on the actual <emph>process</emph> of evaluating the context of data visualization, designing visualizations, and then using them to achieve influence with other people.</p> <p>The notion that, within our assumed context in this article, the end goal of any data visualization is the conveying of a very specific message to audience members through the visual representation of data or information deserves careful consideration. What are the implications of such a perspective in designing and delivering a course revolving around data visualization?</p> <p>If the ultimate goal of a visualization is known (as it must be), then we can define the process of influencing with data into three distinct components: (<reflink idref="bib1" id="ref27">1</reflink>) designing the data visualization(s) as previously described, (<reflink idref="bib2" id="ref28">2</reflink>) crafting a "story" within which that visualization(s) will be shared and find meaning, and (<reflink idref="bib3" id="ref29">3</reflink>) the actual <emph>telling</emph> of that story to the audience members. The approach to visualization design described previously enables the directing of attention of audience members in such a way as to emphasize specific aspects of the data while deemphasizing others. This is the essence of applying the pre‐attentive attributes, in fact, using visual features to emphasize features in the data.</p> <p>The creation of a story within which the visualization(s) will find meaning—something we call "storycrafting"—provides the narrative context within which data visualizations find meaning. Indeed, the story itself (i.e., why sales are falling following several strong years, and recommendations for remediation) is foundational to the design and use of data visualizations for explanation. Without a story, there is no basis for data visualization design. The story provides the narrative wherein data visualizations dramatically reinforce very specific massages. Furthermore, approached appropriately, the story itself is tailored to a specific set of audience members. The story, then, is not just intimately related to data visualization for explanation; story is foundational and is a required precursor.</p> <p>Finally, storytelling is the <emph>delivery</emph> of the crafted story, be it a live presentation, video, or written narrative. With a knowing wave of the hand, this is sometimes interpreted as simply "business communications." While there certainly is a communications aspect to storytelling, to equate these two topical areas likely is to forego the influence that can be achieved through the effective delivery of a story (that leverages data visualizations, of course). Storycrafting and storytelling are intimately related, but also distinct from one another. If <emph>storycrafting</emph> is akin to writing a screenplay, then <emph>storytelling</emph> is akin to the actors and actresses performing that screenplay. One without the other leaves tremendous value on the table and steals the opportunity to achieve maximum leveraging of data visualizations for explanation.</p> <p>As is discussed directly, a data visualization course may or may not include an emphasis on storycrafting and storytelling. Like data visualization itself, this depends on the audience for the course and the intended learning outcomes. Still, at least within the content of the Wake Forest MSBA program, we remain convinced of the remarkable multiplicative power of including all three domains (data visualization design, storycrafting, and storytelling) in a single learning experience.</p> <hd id="AN0168591587-13">TYPES OF COURSES AND THEIR CONTENT</hd> <p>Different student audiences imply different goals for a data visualization course. As previously mentioned, data visualization for exploring data is often embedded in a statistics course or a course in which data visualization is bundled with data management. For courses focused on explaining with data, however, there are two broad approaches: consumer‐focused and creator‐focused. Consumer‐focused data visualization courses focus on best practices in data visualization from the perspective of consumers of data visualizations. That is, how can viewers of data visualizations be made more discerning? As such, the audience for this type of course is general. For example, the course might be an elective open to all undergraduate students. This type of course emphasizes how charts and graphs can be misleading and teaches best practices in data visualization from a defensive perspective (see <emph>Calling Bullshit: Data Reasoning in a Digital World</emph>; Bergstrom & West, [<reflink idref="bib1" id="ref30">1</reflink>]). Typically, this type of consumer‐focused course does not require much (if any) hands‐on construction of data visualizations and does not require the use of data visualization software.</p> <p>Creator‐focused data visualization courses emphasize the design and creation of data visualizations that follow best practices, and generally use software such as Excel, Tableau, Power BI, SAS Visual Analytics or open‐source software such as R or Python in the actual construction of data visualizations. This type of course necessarily focuses more explicitly on best practices including the use of pre‐attentive attributes and concepts like the data‐ink ratio. Storycrafting is likely a part of this course in that each chart is designed to convey a message. If the course also incorporates the delivery of that story to a targeted audience, then we would term this a creator‐focused data visualization and influencing (or communication) course. In such a course, clear guidance on how to construct and deliver compelling stories are required course content. The introduction of an accepted story framework such as the Freytag Pyramid (see Figure 6) almost certainly is required as a means of prescribing story structure and movement (Freytag & MacEwan, [<reflink idref="bib8" id="ref31">8</reflink>]).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01jul23/dsji12282-fig-0006.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12282-fig-0006.jpg" title="6 Freytag's pyramid" /> </p> <p></p> <p>Another important consideration when teaching a creator‐focused data visualization course is whether to include the topic of data dashboards. As noted earlier, dashboards sit between data exploration and data explanation. This is because they are intended to efficiently draw attention to specific features in what likely is a dataset evolving in real time (i.e., potentially problematic outliers or trends) while, in many settings, simultaneously giving the dashboard user the ability to engage in activities such as drilling down and rolling up the data representations, filtering, aggregating, and the like. These latter activities clearly are aimed at data exploration. Given the prevalence of data dashboards in contemporary work settings, and the power of tools like Tableau and PowerBI to enable construction of dashboards, we are favorably inclined for their inclusion in creator‐focused data visualization courses. This will make the course more technical, of course, and carries implications for the visualization software adopted for the course. It also introduces the need to explore in depth the matter of dashboard design, key performance indicators (KPIs), and the technicalities of real‐time updating of data. We discuss in more detail the characteristics of different types of courses in the Summary and Conclusion section.</p> <hd id="AN0168591587-15">ASSESSMENT IN A DATA VISUALIZATION COURSE</hd> <p>In some way, assessment of learning in a data analytics course may be undertaken as is commonly done in other analytics courses. The technical aspects of data visualization (i.e., chart type selection, knowledge of pre‐attentive attributes) provide opportunities for problem‐based assessment. For example, an instructor can provide data sets and ask for a final data visualization that can be graded on a very specific question posed about the data. Alternatively, objective knowledge or heuristics and rules are easily assessed (i.e., what is the maximum recommended number of time periods advisable for a column chart). This means, then, that some learning assessment is easily achieved through exams, quizzes, and the like.</p> <p>Interestingly, it has been our experience that command of heuristics and rules, familiarity with decision aids, and critiques of examples of both good and bad data visualizations (of which there is no shortage online through resources like https://junkcharts.typepad.com/) does not reliably predict an ability to actually design and build data visualizations in scenario‐based assignments, live‐client exercises, and so forth. Because such assignments better approximate real‐world applications, we advocate strongly for significant graded work focused upon case‐ and scenario‐based data visualization design in creator‐focused data visualization courses. Furthermore, these assignments should follow pedagogical strategies in which the first attempts of students are so‐called low‐stakes assignments intended to enable double‐loop learning. By cycling through a series of relatively quick‐hit double‐loop learning opportunities (with emphasis on time‐on‐task), learners receive timely, detailed, and practical feedback on their own data visualization creations.</p> <p>For a data visualization course that also includes a strong emphasis on presentation skills and storytelling, developmental feedback mechanisms commonly associated with traditional public speaking and communication learning contexts are in order. These experiences, at their best, are based on robust coaching‐like learner‐instructor interactions. Of course, regardless of the learning experience, provision of a well‐defined set of rubrics that is shared with learners should be considered a best practice.</p> <hd id="AN0168591587-16">SOFTWARE CONSIDERATIONS</hd> <p>There is a myriad of software options available for the creation of data visualizations. Some are open source (i.e., R and Python), while others are not (i.e., Tableau, PowerBI, and SAS Visual Analytics). Some require coding to generate visualizations, while others are principally point‐and‐click or drag‐and‐drop. Some are more flexible than others. Some enable creation of data dashboards, while others do not.</p> <p>If almost any visualization design can be realized via almost any visualization software product, then what is the basis of preferring one software solution over another? We suggest the answer at least in part be informed by the relationship the academic program has with the marketplace for talent. What are recruiters desiring or expecting? Many of the firms accessing talent coming out of the Wake Forest MSBA program are keenly interested in graduates possessing threshold ability in Tableau and PowerBI. Hence, our inclusion of these two visualization tools in our graduate analytics program. At the same time, for the reason explained earlier, we rely heavily on Excel and PowerPoint (the latter because of our emphasis on storytelling with data).</p> <hd id="AN0168591587-17">SUMMARY AND CONCLUSION</hd> <p>Based on our experience developing a successful data visualization course for our Master of Science in Business Analytics program at Wake Forest University, we have discussed what we believe to be critical considerations, particularly for new instructors. These include fundamentals of design and a three‐phase process for applying them, which we have illustrated with a simple example. We recommended topical coverage for different possible course audiences as summarized in Table 2.</p> <p>2 TABLE Summary of topical coverage by type of course</p> <p> <ephtml> <table><thead><tr><th /><th>Chart</th><th>Pre‐attentive</th><th>Effective</th><th>Bad</th><th>Data viz</th><th>Story‐</th><th>Freytag's</th><th>Required</th><th>Data</th></tr><tr><th>Audience</th><th>Selection</th><th>Attributes</th><th>Design</th><th>Examples</th><th>Software</th><th>Crafting</th><th>Pyramid</th><th>Presentation</th><th>Dashboards</th></tr></thead><tbody><tr><td>Consumer (C)</td><td>◑</td><td>◑</td><td>◑</td><td>●</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Creator—Data viz (CDV)</td><td>●</td><td>●</td><td>●</td><td>◑</td><td>●</td><td>◑</td><td>○</td><td>◑</td><td>◑</td></tr><tr><td>Creator—Data viz & influencing (CDVI)</td><td>●</td><td>●</td><td>●</td><td>◑</td><td>●</td><td>●</td><td>●</td><td>●</td><td>◑</td></tr></tbody></table> </ephtml> </p> <p>1 None ○ ; medium ◑ ; heavy ●.</p> <p>In Table 2, the C (consumer) audience is likely an undergraduate elective in how to be an intelligent consumer of charts and graphs and could be a stand‐alone course or part of a larger course on data‐literacy and reasoning (see Bergstrom & West, [<reflink idref="bib1" id="ref32">1</reflink>]). The CDV (creator‐data visualization) audience is hands‐on, and hence requires software. It focuses more on best practices and creating a chart that effectively conveys its message to the intended audience. This course exists in many analytics and data science programs (either required or as an elective) and could be intermingled with a technical topic like data wrangling or data management.</p> <p>CDVI (creator‐data visualization and influence) combines best practices in data visualization with skills for effective storytelling and presentation in written and oral forms. We have implemented this as a required course in our MSBA program and an elective in our MBA program at Wake Forest. There is also a similar course in our Master of Science in Accounting program. We believe this type of course, which can be challenging to teach, is very beneficial to Business and STEM students at all levels (including PhD), who must communicate with data.</p> <p>In a 2019 survey of our MSBA alumni, we asked about the use of various topics on the job, with alternative answers of daily, occasionally, and never. Fifty‐five percent of the respondents said they used data visualization daily and 81% said they use data storytelling daily. In response to the faculty member who asked, "Isn't that just charts and graphs?" the first author replied, "Not everything we teach needs to be technically difficult. But everything we teach needs to be useful." There is no question that strong data visualization skills are very useful in today's business environment.</p> <p>GRAPH: Supplement information</p> <p>GRAPH: Supplement information</p> <ref id="AN0168591587-18"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref8" type="bt">1</bibl> <bibtext> Bergstrom, C. & West, J. (2019) Calling Bullshit: Data reasoning in a digital world. Course syllabus. https://<ulink href="http://www.callingbullshit.org/syllabus.html">www.callingbullshit.org/syllabus.html</ulink></bibtext> </blist> <blist> <bibl id="bib2" idref="ref4" type="bt">2</bibl> <bibtext> Bowers, M., Camm, J. & Chakraborty, G. (2018) The evolution of analytics and implications for industry and academic programs. Interfaces, 48 (6), 487 – 499.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref1" type="bt">3</bibl> <bibtext> Emsi Burning Glass Technologies. (2022) https://kb.emsidata.com/methodology/emsi‐data‐basic‐overview/</bibtext> </blist> <blist> <bibl id="bib4" idref="ref7" type="bt">4</bibl> <bibtext> Camm, J., Cochran, J., Fry, M. & Ohlmann, J. (2022) Data visualization: Exploring and explaining with data. Boston, MA : Cengage Learning, Inc.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref23" type="bt">5</bibl> <bibtext> Camm, J., Fry, M. & Shafer, J. (2017) A practitioner's guide to best practices in data visualization. Interfaces, 47 (6), 473 – 499.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref15" type="bt">6</bibl> <bibtext> Izawa, C. (Ed.) (1999) On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson‐Shiffrin Model. East Sussex, United Kingdom : Psychology Press.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref16" type="bt">7</bibl> <bibtext> Knaflic, C. (2015) Storytelling with data: A data visualization guide for business professionals. Hoboken, New Jersey : John Wiley and Sons.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref31" type="bt">8</bibl> <bibtext> Freytag, G. & MacEwan, E. (2014) Freytag's technique of the drama: An exposition of dramatic composition and art. An authorized translation from the 6th German. In E. J. MacEwan (Ed.), Primary Source Edition. New York, New York : Nabu Press.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref2" type="bt">9</bibl> <bibtext> Rappa, M. (2022) Institute for advanced analytics website. N.C. State University. https://analytics.ncsu.edu/?page_id=4184</bibtext> </blist> <blist> <bibtext> Schroeder, B. (2021) The data analytics profession and employment is exploding—Three trends that matter. Forbes.com, https://<ulink href="http://www.forbes.com/sites/bernhardschroeder/2021/06/11/the‐data‐analytics‐profession‐and‐employment‐is‐exploding‐three‐trends‐that‐matter/?sh=248656933f81">www.forbes.com/sites/bernhardschroeder/2021/06/11/the‐data‐analytics‐profession‐and‐employment‐is‐exploding‐three‐trends‐that‐matter/?sh=248656933f81</ulink></bibtext> </blist> <blist> <bibtext> Seal, K., Leon, L., Przasnyski, Z. & Lontok, G. (2020) Delivering business analytics competencies and skills: A supply side assessment. INFORMS Journal on Applied Analytics, 50 (4), 239 – 254.</bibtext> </blist> <blist> <bibtext> Simkin, D. & Hastie, R. (1987) An information‐processing analysis of graph perception. The Journal of the American Statistical Association, 82 (398), 454 – 465.</bibtext> </blist> <blist> <bibtext> Stackpole, B. (2020) The next chapter in analytics: Data storytelling. MIT Sloan Management, Ideas That Matter, Analytics. https://mitsloan.mit.edu/ideas‐made‐to‐matter/next‐chapter‐analytics‐data‐storytelling</bibtext> </blist> <blist> <bibtext> Tufte, E. (2001) The visual display of quantitative information (2nd edn.). Cheshire, CT : Graphics Press.</bibtext> </blist> <blist> <bibtext> Wexler, S., Shaffer, J. & Cotgreave, A. (2017) The big book of dashboards: Visualizing your data using real‐world business scenarios. Hoboken, NJ : John Wiley & Sons.</bibtext> </blist> </ref> <aug> <p>By Jeffrey D. Camm; Gordon E. McCray and Michelle L. Roehm</p> <p>Reported by Author; Author; Author</p> <p></p> <p>Jeffrey Camm is Senior Associate Dean for Faculty, the Inmar Presidential Chair in analytics, and the Academic Director of the Center for Analytics Impact at the Wake Forest University School of Business. He earned his BS in mathematics from Xavier University (Ohio) and his PhD in management science from Clemson University. His research is on the application of optimization modeling and solution algorithms to difficult decision problems in a diverse set of application areas, as well as empirical studies on the impact of analytics.</p> <p>Gordon McCray is Associate Professor of information systems and AT&T Faculty Fellow in the School of Business at Wake Forest University. He teaches data visualization‐related courses across a variety of undergraduate and graduate programs. His research focuses on project management, the outsourcing of IT services, and the visual representation of data and information. He holds a BS in physics from Wake Forest University, an MBA from Stetson University, and a PhD from Florida State University.</p> <p>Michelle Roehm is the Peter C. Brockway Chair in strategic management at the School of Business at Wake Forest University. She received her undergraduate and master's degrees in communications from the University of Illinois at Urbana‐Champaign and her PhD in marketing from Northwestern University. Since joining the Wake Forest faculty in 1997, her teaching and research interests have included business communication, branding, and consumer behavior.</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib11" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib12" firstref="ref14"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref21"></nolink>
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  Data: More than Just Charts and Graphs: What to Teach in a Data Visualization Course
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  Data: <searchLink fieldCode="AR" term="%22Camm%2C+Jeffrey+D%2E%22">Camm, Jeffrey D.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9619-6348">0000-0002-9619-6348</externalLink>)<br /><searchLink fieldCode="AR" term="%22McCray%2C+Gordon+E%2E%22">McCray, Gordon E.</searchLink><br /><searchLink fieldCode="AR" term="%22Roehm%2C+Michelle+L%2E%22">Roehm, Michelle L.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Decision+Sciences+Journal+of+Innovative+Education%22"><i>Decision Sciences Journal of Innovative Education</i></searchLink>. Jul 2023 21(3):112-122.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: <searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink><br /><searchLink fieldCode="DE" term="%22Masters+Programs%22">Masters Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Administration+Education%22">Business Administration Education</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Content%22">Course Content</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Development%22">Program Development</searchLink>
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  Data: 10.1111/dsji.12282
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  Data: 1540-4595<br />1540-4609
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  Data: Based on our experience developing and delivering a highly successful data visualization course within a Master of Science in Business Analytics program, we present a taxonomy for data visualization courses and recommend content and pedagogical features for each type of data visualization course therein. We also discuss the interdependence between data visualization and business communication as a critical consideration in course design.
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        Value: 10.1111/dsji.12282
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 112
    Subjects:
      – SubjectFull: Visual Aids
        Type: general
      – SubjectFull: Masters Programs
        Type: general
      – SubjectFull: Business Administration Education
        Type: general
      – SubjectFull: Data Use
        Type: general
      – SubjectFull: Course Content
        Type: general
      – SubjectFull: Program Development
        Type: general
    Titles:
      – TitleFull: More than Just Charts and Graphs: What to Teach in a Data Visualization Course
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Camm, Jeffrey D.
      – PersonEntity:
          Name:
            NameFull: McCray, Gordon E.
      – PersonEntity:
          Name:
            NameFull: Roehm, Michelle L.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 1540-4595
            – Type: issn-electronic
              Value: 1540-4609
          Numbering:
            – Type: volume
              Value: 21
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Decision Sciences Journal of Innovative Education
              Type: main
ResultId 1