Add files via upload

NorfolkPop.csv

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+City,DATE,Population
+Norfolk,1/1/1970,307.951
+Norfolk,1/1/1971,302.1
+Norfolk,1/1/1972,286.1
+Norfolk,1/1/1973,287.8
+Norfolk,1/1/1974,288.6
+Norfolk,1/1/1975,285.1
+Norfolk,1/1/1976,282.2
+Norfolk,1/1/1977,281.3
+Norfolk,1/1/1978,277.2
+Norfolk,1/1/1979,269.3
+Norfolk,1/1/1980,266.979
+Norfolk,1/1/1981,273.916
+Norfolk,1/1/1982,265.322
+Norfolk,1/1/1983,274.258
+Norfolk,1/1/1984,276.757
+Norfolk,1/1/1985,266.585
+Norfolk,1/1/1986,269.371
+Norfolk,1/1/1987,275.951
+Norfolk,1/1/1988,268.37
+Norfolk,1/1/1989,263.064
+Norfolk,1/1/1990,261.425
+Norfolk,1/1/1991,255.351
+Norfolk,1/1/1992,258.174
+Norfolk,1/1/1993,255.514
+Norfolk,1/1/1994,248.916
+Norfolk,1/1/1995,244.623
+Norfolk,1/1/1996,244.176
+Norfolk,1/1/1997,239.095
+Norfolk,1/1/1998,234.533
+Norfolk,1/1/1999,233.497
+Norfolk,1/1/2000,234.645
+Norfolk,1/1/2001,235.673
+Norfolk,1/1/2002,239.93
+Norfolk,1/1/2003,236.804
+Norfolk,1/1/2004,240.442
+Norfolk,1/1/2005,236.231
+Norfolk,1/1/2006,238.832
+Norfolk,1/1/2007,235.853
+Norfolk,1/1/2008,234.363
+Norfolk,1/1/2009,233.419
+Norfolk,1/1/2010,243.019
+Norfolk,1/1/2011,243.701
+Norfolk,1/1/2012,246.247
+Norfolk,1/1/2013,245.598
+Norfolk,1/1/2014,246.756
+Norfolk,1/1/2015,246.667
+Norfolk,1/1/2016,246.042
+Norfolk,1/1/2017,244.908
+Norfolk,1/1/2018,244.168
+Norfolk,1/1/2019,243.581
+Norfolk,1/1/2020,237.738
+Norfolk,1/1/2021,235.025
+Norfolk,1/1/2022,232.995

objectives.md

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+# CS 625 Topic Objectives
+
+## Chapter 1 - Introduction
+
+* Define visualization.
+* Explain the importance of humans in the visualization process.
+* Explain why human vision is particularly well-suited for information transfer.
+* Give an example of a visualization idiom.
+* Explain why it is best to consider multiple alternatives for vis before selecting a solution.
+* Explain at a high-level the "why-what-how" framework for analyzing visualization use.
+* Differentiate between Seaborn, Vega-Lite, D3, and Tableau and describe the type of tasks for which each tool might be most appropriate.
+
+## Chapter 2 - Data
+
+* Distinguish among the four basic dataset types.
+* Distinguish among the five core data types.
+* Distinguish between categorical and ordered attributes.
+* Distinguish between ordinal and quantitative attributes.
+* Explain why understanding the dataset and data types and semantics matter for designing effective visualizations.
+* Distinguish between scientific vis and information vis in terms of how spatial data is used.
+* Explain the difference between a flat table and a multidimensional table.
+* Explain some of the complexities of dealing with temporal data.
+* Identify two tools for cleaning data.
+
+## Chapter 5 - Marks and Channels
+
+* Explain how marks and channels are related.
+* Distinguish between the identity channel type and the magnitude channel type and indicate which channels belong to each type.
+* Distinguish between the principles of expressiveness and effectiveness in visual encoding.
+* List the channels for ordered attributes in order from most effective to least effective.
+* List the channels for categorical attributes in order from most effective to least effective.
+* Describe the effects of accuracy, discriminability, separability, popout (preattentive processing), and grouping and give one example that illustrates each.
+* Explain the implication of Stevens' Law for visualizations.
+* Explain the implication of Weber's Law for visualizations.
+
+## Chapter 7 - Arrange Tables
+
+* Explain why the arrange design choice is the most crucial visual encoding choice.
+* Explain how the concepts of express, separate, order, and align all relate to arranging tabular data.
+* For each idiom example in the text (from scatterplot to normalized stacked bar chart), identify the "what: data" properties of the idiom.
+* For each idiom example in the text, identify the "how: encode" properties of the idiom.
+* For each idiom example in the text, identify the "why: task" properties of the idiom.
+* For each idiom example in the text, identify the "scale" properties of the idiom.
+* Differentiate between line charts and bar charts and explain when each is appropriate
+* Explain some of the disadvantages of pie charts.
+* Explain how a radial layout maps to a rectilinear layout.
+* Given a particular dataset and task, suggest an idiom and explain why it might be appropriate
+* Identify a visualization where an inappropriate arrange design choice was made and explain why the choice was inappropriate.
+
+## Chapter 10 - Map Color and Other Channels
+
+* Describe the components of color.
+* Describe the three main types of colormaps.
+* Explain the importance choosing an appropriate colormap.
+* Given a set of data and a task, determine an appropriate colormap.
+* Identify an inappropriate use of a colormap and suggest a more appropriate one.
+* Explain why rainbow colormaps should only be used with great care.
+* For the visual channels other than color, identify which are magnitude and which are identity channels.
+
+## Chapter 8 - Maps (8.1-8.3)
+
+* Describe how the arrange design choice is different with spatial data as opposed to tabular data.
+* Describe a choropleth map.
+
+## Chapter 6 - Rules of Thumb
+
+* Explain potential difficulties with the use of 3D visualization.
+* Identify situations in which the use of 3D visualization would be appropriate.
+* Explain why "eyes beat memory".
+* Explain what happens when people experience cognitive load.
+* Define change blindness.
+* Explain the tradeoff between resolution and immersion.
+* Explain the Shneiderman mantra "overview first, zoom and filter, details on demand".
+* Explain the alternate concept of "search, show context, expand on demand" and identify in what situations it may be more appropriate than the Shneiderman mantra.
+* Explain the importance of the design slogan "get it right in black and white".
+
+## Chapter 13 (through 13.4.1) - Reduce Items and Exploratory Data Analysis (EDA)
+
+* Explain the need to reduce data, both in terms of number of items and number of attributes.
+* Explain the difference between filtering and aggregation and the purposes of each.
+* Identify instances of scented widgets, as opposed to standard filtering widgets.
+* Contrast histograms with bar charts.
+* Explain the importance of binwidth in a histogram.
+* Describe the components of a boxplot.
+* List the three steps in the iterative cycle of EDA.
+* Explain the concept of covariation.
+* Name some questions to ask about patterns found in data.
+* Given a dataset, generate questions aimed at examining correlation and understanding underlying patterns in the data.
+
+## Storytelling
+
+* List the seven genres of narrative visualization.
+* Describe the Martini glass structure of narrative visualization.
+* Describe a stepper in an interactive visualization.
+* Describe how narrative visualization and presentation visualization differ from exploratory/analysis visualization, especially in terms of tools and approaches.
+
+## Visualization Literacy
+
+* Explain the concept of visual literacy.
+* List 6 tips for spotting misinformation.
+* Demonstrate the use of Fermi estimation.
+* Explain confirmation bias.
+* List 10 suggestions for spotting misinformation online.
+* Given a misleading visualization, identify the misleading elements and suggest how the visualization could be improved.
+
+## Chapter 12 - Multiple Views
+
+* Explain the importance and usefulness of faceting data across multiple views.
+* Contrast the two major approaches to faceting information.
+* Describe the four major design choices for juxtaposed views.
+* Explain the concept of linked highlighting.
+* Describe the three alternatives for sharing data between two juxtaposed views.
+* Contrast the use of small multiples with a grouped bar chart.
+* Given a multiform visualization, identify the ways in which the data was split into multiple views and the design choices that were made.
+
+## Chapter 11 (through 11.4) - Manipulate View
+
+* Describe why changing a view might aid in understanding a dataset.
+* Explain why order can make such an impact in understanding.
+* Describe some of the design choices that can be made with selection.
+* Explain the difference between selection and highlighting.
+* Given an interactive visualization, identify the interaction idioms used.