MIS 420: Data Visualization, Representation, and Presentation Content adapted from Chapter 2 and 3 of datos/descargas/libros/Data-Points.pdf

[data visualization: the medium is the message] Data visualization: a communications medium that tells a story visually Main Goal: “The main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information“ – Friedman (2008)

[data visualization: a short history] Visualizations are OLD 1786: William Playfair (1757 to 1823): created the line graph, the bar chart, and the pie chart Until 1970s: (computers), most people did visualizations by hand Not much data Present Day: Data deluge More data, more things to visualize

[data visualization: utilities for business] Common Business Tools: Some specialized Tools (for specific data types): Images: Image Plot Networks: Gephi Statistics: R

[presenting information and graphics] Data might make sense to you You become engulfed in data When presenting: Safe to assume that audience nothing about your data Patterns often are revealed when you tell them Good practice: Use shapes, colors, size to represent the data (light colors mean one thing, dark colors mean another) Use words to put more meaning behind visualizations This is where GOOD STORYTELLING happens

President Obama’s 2013 Budget Proposal Bubble Chart Using Tableau

[representing and presenting data is like cooking] YOU YOU: Data Analyst Ingredients (components): dataset, visual cues, coordinate systems, scales, and context Good Story (Meal): Data communicated efficiently and effectively In other words, it tastes good!

[four components of visualization] Component #1: visual cues Visual Cues: encoding data with shapes, colors, sizes Types of visual cues: Position: you compare values based on where others are placed in a given space or coordinate system Length: (think bar charts): longer a bar is, the greater the absolute value, and it can work in all directions: horizontal, vertical, or even at different angles on a circle Angle: (think pie chart): communicating data in degrees (from 0 to 360)

[four components of visualization] examples position, length, angle MATCH THE COMPONENT WITH THE GRAPHIC

[four components of visualization] Component #1: visual cues Visual Cues: encoding data with shapes, colors, sizes Types of visual cues: Direction: see which way is up, which is down Shapes: (think a map): differentiate categories and objects; can provide context that points alone can’t Area: (think bubble chart): bigger objects represent greater values

[four components of visualization] examples direction, shapes, area MATCH THE COMPONENT WITH THE GRAPHIC

[four components of visualization] Component #1: visual cues Visual Cues: encoding data with shapes, colors, sizes Types of visual cues: Volume: how much 3-D space you give to objects Color hue (color): just refer to as color. That’s red, green, blue, and so on. Differing colors used together usually indicates categorical data, where each color represents a group Color saturation: amount of hue in a color, so if your selected color is red, high saturation would be very red, and as you decrease saturation, it looks more faded. Color hue + saturation: used together, you can have multiple hues that represent categories, but each category can have varying scales

[four components of visualization] examples volume, color hue, color saturation MATCH THE COMPONENT WITH THE GRAPHIC

[four components of visualization] Component #2: Coordinate System Coordinate system: gives meaning to an x- y coordinate or a latitude and longitude pair; structured space and rules that dictate where the shapes and colors go Types of coordinate systems: Cartesian (most commonly used): x and y coordinate plain; bar chart Polar: pie chart; useful in cases in which the angle or direction is important Geographic (latitude and longitude): the mapping of location data using latitude and longitude

[four components of visualization] examples Cartesian, Polar, Geographic MATCH THE COMPONENT WITH THE GRAPHIC

[four components of visualization] Component #3: Scales Scales: dictates where in those dimensions your data maps to. Types of scales: Numeric: scales made with numbers (linear scale, percent scale). Spacing between numbers means something. Categorical: data representing categories (cities, states, gender, etc.). Spacing is arbitrary, but it may create meaning (e.g., ranking restaurants from bad to good, good to bad) Time: plot temporal data on a linear scale, but you can divide it into categories such as months or days of the week. Advantage of lending a reader connection because time is a part of everyday life (creates a meaningful context)

[four components of visualization] Component #4: Context Context: information that lends to better understanding the who, what, when, where, and why of your data. Can make the data clearer for readers and point them in the right direction TO TELL A GOOD STORY, REMEMBER 5 W’s (and HOW!): Who: is the data about? Who are you telling the story to? To whom is the data relevant? What: happened? Is happening? Can be done based on the findings? When: did this happen? Do things change based on time? Where: is the data relevant? Why: is what is happening, happening? Why are you telling the story you are telling? Why is this story necessary? How: how can things be changed based on your findings? THE BIGGEST QUESTION YOU NEED TO ANSWER IS: SO WHAT??

[wrapping up: key points] Challenge of visualization, representation, and presentation: figure out what shapes and colors work best, where to put them, and how to size them. This is ultimately where good and bad stories come from To jump from data to visualization, know your ingredients Visual cues, coordinate systems, scales, and context are your ingredients Visual cues are the main thing that people see, and the coordinate system and scale provide structure and a sense of space. Context breathes life into the data and makes it understandable, relatable, and worth looking at Challenge of more data is that you have more visualization options, and many of those options will be poor ones. To filter out the bad and find the worthwhile options—to get to visualization that means something—you must get to know your data

[now let’s work on HOA#3]: visualizations and storytelling ml