• Data Visualization

    Does the course have an overall narrative?

    Overview of Course
    In one to two sentences, what is this class?
    This class teaches the fundamentals of data visualization and gives students practice storytelling through data. The implicit course material is applying design and narrative concepts to data visualization.

    Who is the audience?
    We don’t envision the class being programming heavy. This class is for students with a background in a creative discipline such as creative design, art, or writing. Current students in the data science track will find this course useful as well as those with more programming experience. It’s often the case that those more familiar with programming don’t think about how to communicate their analysis effectively through narratives and data vis. We want to give these students the mindsets, skills, and knowledge to do so.

    Why are you excited to teach it?
    Chris is excited to teach this to apply concepts to effective data visualizations and storytelling. He’s been hooked since reading The Visual Display of Quantitative Information.
    Jonathan: “Your best analysis is only as good as your ability to communicate it.” I want to enable scientists, engineers, and designers to look at data from a new perspective and share that lens with others.

    How will this course change its students?
    What will a student look like after this course?
    Six months after a student takes this class, you ask them “Tell me about the course. What do you remember? What are you doing with what you learned?” What is their response?

    I remember critiquing, decomposing, and making interesting and informative visualizations. I know that data visualization is more than just throwing color at a plot. Data needs to tell a story, and I can tell compelling stories by using the right visualization techniques. One of the best parts of the class was learning about data visualization theory and connecting it to the why certain graphics are effective. I know a little bit about d3.js and have enough knowledge to learn more about it on my own. This course was fun because I shared my work and have a stronger appreciation for what goes into designing effective visualizations.

    What are the “enduring understandings”? Six months after a student finishes this course what knowledge, skills and beliefs do they still retain (2-4 of each)?

    Attitudes/Beliefs
    Students will think like a writer and apply storytelling to communicate data. A visualization is a conversation.
    Everything is data or can be turned into data.
    Creating visualizations is a fluid and evolving process.
    Don’t be afraid of critique. Share your visualizations.

    Skills
    I can choose the appropriate visualization technique based on the structure of the data.
    I can create an effective visual narrative that speaks to a specific audience, is aware of bias (subjective and objective), or conveys emotion.
    I can let the visual exploration of data guide me to the next steps of improving a visualization. I can iterate on visualizations to tell communicate an idea, tell a story, or spark a discussion (example).
    I can “read” a visualization and decompose it into its visual encodings and techniques.

    Knowledge
    I know how to encode data visually using graphical elements such as color, scale, shape, etc.
    Come back to this.

    What final project could a student complete to demonstrate that they’ve mastered these goals?

    The student will create a visualization based on either a data set that they have found or one that the course will provide. The student will also write an “essay” on what they want to convey with their visualization. One (or a few) students will then give feedback on the visualization (without seeing the text) and what they have interpreted it as. Once finished they can then look at the essay on what the student tried to convey in the visualization and evaluate how effective the visualization achieved this.

    What does a student look like before taking this course?
    If you expect your target student has come from an existing Udacity class(es), which class(es) did they come from?
    The student may have come from CS101 or any of the Data Science courses such as Intro to DS, Data Munging or EDA. They may have also come from the Design of Everyday Things.

    How does your target student think about the subject matter before starting the course? What beliefs (correct or incorrect) might they bring with them?

    A visualization is just a pretty picture.
    Your choice of visual cues/elements are somewhat arbitrary. I want a “rainbow” plot.
    A visualization is a “side effect” of my analysis.
    The choice of graphical elements is not tied to the structure of the data.
    More is better. Color, shapes, gridlines, complexity, etc. enhance a visualization and draw viewers in.
    Visualizations can misrepresent the data and its underlying patterns, results, or trends.
    A visualization will have an impact (good or bad) and the creator should be responsible with its results.
    A visualization is easy and quick to create.
    Students may design a visualization with themselves in mind as the target audience.

    How does your target student’s thinking about the subject compare to the “enduring understandings” you identified earlier?

    Some students might have a limited understanding of what visualizations can do in communicating data effectively. They may also not understand visual encodings and how those design choices are important for communicating data effectively (i.e. readers can decode information and obtain the story/results easily). This impact is usually magnified if the designer does not understand who their audience is and what bias they will bring. More is only better if the audience can digest and “read” the visualization. The intended user experience of a visualization can often dictate what choices to make in display and interaction of data.

    How will the “before” student become the “after” student?
    For each enduring understanding, what are the biggest milestones in getting a student from “before” to “after”?

    The student can decode a visualization. The student can identify what works and what doesn’t work in a visualization.

    reading a visualization — what information is a visualization conveying.
    Skills 4
    deconstructing a Viz — what visual elements contribute to the message.
    Knowledge 1
    Skills 4
    adapt a Viz — how changes to the visual elements affect the message.
    Skills 1
    Skills 4
    Skills 2 — adapt a Viz to get rid of emotion/bias. Or convey a different message.
    create a Viz with a given dataset — how a student finds a new insight in a already visualized dataset. Tell them what the mesage is.
    Skills 2
    Skills 3
    find a novel dataset to visualize — have them explore the dataset and discover their own insight. Post on a forum
    Attitudes 1-4
    Skills 3
    Skills 2

    What activities, exercises, or experiences could a student do to progress through each of these milestones?

    Technologies: Excel, Chartio, HTML, CSS, JS, d3
    Visualize a data set at the start of the class and visualize the same data set at the end of the class (see how the course has change how you visualize data)
    Reading a Visualization

    What will the course look like?
    What sequence of lessons would effectively take a student from “before” to “after”? Outline your lessons below with a 1 - 2 sentence description for each.

    Lessons can be end to end and address the milestones of reading, deconstructing, adapting, and creating visualizations.

    Incorporate iteration of visualizations throughout the lesson.

    Problem Sets can have deeper dives into main skills for each lesson. Solutions are optional. Students like feedback so consider how we will provide this feedback.

    L1: data vis examples, coding expectation,
    Show students the power of what they are going to learn. Show one example from the NYTimes (something from traditional data vis, not a story like the Silk Road piece).
    Include a coding exercise towards beginning of L1. Communicate that this class will use programming to generate data visualizations.

    Data Visualization Competition - have a data visualization expert choose the best projects to showcase, feature on the Udacity blog (future promotion/re-engagement of learners)
    marketing comms
    could run this competition twice to re-engage users who don’t finish are who are dormant in the class

    Sharing visualizations should be doable through the forums either embedded or linked.

    What unique personality traits does each instructor bring to this course? How will we showcase your personality in the class?

    Chris is enthusiastic about data science and has many experiences in curriculum design. I’m love to learn and can ask great question during interviews about topics that I know little to nothing about. We can feature Chris in interview or headshots.

    Jonathan has been passion about teaching ever since he left school; first cutting his teeth with web development then transitioning into the complexity of data science. He loves to find new subjects to teach in fun and clever ways. As an example, teaching sorting by lining the students up and running a ‘human’ algorithm or having the students cluster themselves.

  • Lesson 1

    What do we want to teach?

    How do we want to teach it? What experiences, tasks, or exercises

    How can students check their understanding?

    How are we putting students at the center of active learning experiences?

  • Lesson 2

    What do we want to teach?

    How do we want to teach it? What experiences, tasks, or exercises

    How can students check their understanding?

    How are we putting students at the center of active learning experiences?

  • Lesson 3

    What do we want to teach?

    How do we want to teach it? What experiences, tasks, or exercises

    How can students check their understanding?

    How are we putting students at the center of active learning experiences?

  • Lesson 4

    What do we want to teach?

    How do we want to teach it? What experiences, tasks, or exercises

    How can students check their understanding?

    How are we putting students at the center of active learning experiences?

  • Opening Image

    page 1

    If you had to choose a photo to establish what “the world” is like before your story starts, this is it.

    Make it stand out, because first impressions matter. They set the tone and mood and scope, and hook the audience (or reader).

    If it’s a vast epic, the opening image should be grand. If it’s a small intricate family drama, the opening images are small and subdued.

    {"cards":[{"_id":"535e879b4de898da0684401c","treeId":"535e879b4de898da0684401a","seq":1,"position":2,"parentId":null,"content":"# Data Visualization\n\nDoes the course have an overall narrative?\n\n**Overview of Course**\nIn one to two sentences, what is this class?\nThis class teaches the fundamentals of data visualization and gives students practice storytelling through data. The implicit course material is applying design and narrative concepts to data visualization.\n\n**Who is the audience?**\nWe don’t envision the class being programming heavy. This class is for students with a background in a creative discipline such as creative design, art, or writing. Current students in the data science track will find this course useful as well as those with more programming experience. It’s often the case that those more familiar with programming don’t think about how to communicate their analysis effectively through narratives and data vis. We want to give these students the mindsets, skills, and knowledge to do so.\n\n**Why are you excited to teach it?**\nChris is excited to teach this to apply concepts to effective data visualizations and storytelling. He’s been hooked since reading The Visual Display of Quantitative Information.\nJonathan: “Your best analysis is only as good as your ability to communicate it.” I want to enable scientists, engineers, and designers to look at data from a new perspective and share that lens with others.\n\n**How will this course change its students?\nWhat will a student look like after this course?\nSix months after a student takes this class, you ask them “Tell me about the course. What do you remember? What are you doing with what you learned?” What is their response?**\n\nI remember critiquing, decomposing, and making interesting and informative visualizations. I know that data visualization is more than just throwing color at a plot. Data needs to tell a story, and I can tell compelling stories by using the right visualization techniques. One of the best parts of the class was learning about data visualization theory and connecting it to the why certain graphics are effective. I know a little bit about d3.js and have enough knowledge to learn more about it on my own. This course was fun because I shared my work and have a stronger appreciation for what goes into designing effective visualizations.\n\n**What are the “enduring understandings”? Six months after a student finishes this course what knowledge, skills and beliefs do they still retain (2-4 of each)?**\n\n**Attitudes/Beliefs**\nStudents will think like a writer and apply storytelling to communicate data. A visualization is a conversation.\nEverything is data or can be turned into data.\nCreating visualizations is a fluid and evolving process.\nDon’t be afraid of critique. Share your visualizations.\n\n**Skills**\nI can choose the appropriate visualization technique based on the structure of the data.\nI can create an effective visual narrative that speaks to a specific audience, is aware of bias (subjective and objective), or conveys emotion.\nI can let the visual exploration of data guide me to the next steps of improving a visualization. I can iterate on visualizations to tell communicate an idea, tell a story, or spark a discussion (example).\nI can “read” a visualization and decompose it into its visual encodings and techniques.\n\n**Knowledge**\nI know how to encode data visually using graphical elements such as color, scale, shape, etc.\nCome back to this. \n\n\n**What final project could a student complete to demonstrate that they’ve mastered these goals?**\n\nThe student will create a visualization based on either a data set that they have found or one that the course will provide. The student will also write an “essay” on what they want to convey with their visualization. One (or a few) students will then give feedback on the visualization (without seeing the text) and what they have interpreted it as. Once finished they can then look at the essay on what the student tried to convey in the visualization and evaluate how effective the visualization achieved this. \n\n**What does a student look like before taking this course?**\nIf you expect your target student has come from an existing Udacity class(es), which class(es) did they come from? \nThe student may have come from CS101 or any of the Data Science courses such as Intro to DS, Data Munging or EDA. They may have also come from the Design of Everyday Things.\n\n**How does your target student think about the subject matter before starting the course? What beliefs (correct or incorrect) might they bring with them?**\n\nA visualization is just a pretty picture.\nYour choice of visual cues/elements are somewhat arbitrary. I want a “rainbow” plot.\nA visualization is a “side effect” of my analysis.\nThe choice of graphical elements is not tied to the structure of the data.\nMore is better. Color, shapes, gridlines, complexity, etc. enhance a visualization and draw viewers in.\nVisualizations can misrepresent the data and its underlying patterns, results, or trends.\nA visualization will have an impact (good or bad) and the creator should be responsible with its results.\nA visualization is easy and quick to create.\nStudents may design a visualization with themselves in mind as the target audience.\n\n**How does your target student’s thinking about the subject compare to the “enduring understandings” you identified earlier?** \n\nSome students might have a limited understanding of what visualizations can do in communicating data effectively. They may also not understand visual encodings and how those design choices are important for communicating data effectively (i.e. readers can decode information and obtain the story/results easily). This impact is usually magnified if the designer does not understand who their audience is and what bias they will bring. More is only better if the audience can digest and “read” the visualization. The intended user experience of a visualization can often dictate what choices to make in display and interaction of data.\n\n**How will the “before” student become the “after” student?\nFor each enduring understanding, what are the biggest milestones in getting a student from “before” to “after”?**\n\nThe student can decode a visualization. The student can identify what works and what doesn’t work in a visualization.\n\n reading a visualization -- what information is a visualization conveying. \nSkills 4\n deconstructing a Viz -- what visual elements contribute to the message. \nKnowledge 1\nSkills 4\n adapt a Viz -- how changes to the visual elements affect the message. \nSkills 1\nSkills 4\nSkills 2 -- adapt a Viz to get rid of emotion/bias. Or convey a different message.\n create a Viz with a given dataset -- how a student finds a new insight in a already visualized dataset. Tell them what the mesage is.\nSkills 2\nSkills 3\n find a novel dataset to visualize -- have them explore the dataset and discover their own insight. Post on a forum\nAttitudes 1-4\nSkills 3\nSkills 2\n \n**What activities, exercises, or experiences could a student do to progress through each of these milestones?**\n\nTechnologies: Excel, Chartio, HTML, CSS, JS, d3\nVisualize a data set at the start of the class and visualize the same data set at the end of the class (see how the course has change how you visualize data)\nReading a Visualization\n\n**What will the course look like?**\nWhat sequence of lessons would effectively take a student from “before” to “after”? Outline your lessons below with a 1 - 2 sentence description for each. \n\nLessons can be end to end and address the milestones of reading, deconstructing, adapting, and creating visualizations.\n\nIncorporate iteration of visualizations throughout the lesson.\n\nProblem Sets can have deeper dives into main skills for each lesson. Solutions are optional. Students like feedback so consider how we will provide this feedback.\n\n\n\nL1: data vis examples, coding expectation, \nShow students the power of what they are going to learn. Show one example from the NYTimes (something from traditional data vis, not a story like the Silk Road piece).\nInclude a coding exercise towards beginning of L1. Communicate that this class will use programming to generate data visualizations.\n\n\n\nData Visualization Competition - have a data visualization expert choose the best projects to showcase, feature on the Udacity blog (future promotion/re-engagement of learners)\nmarketing comms\ncould run this competition twice to re-engage users who don’t finish are who are dormant in the class\n\nSharing visualizations should be doable through the forums either embedded or linked.\n\n**What unique personality traits does each instructor bring to this course? How will we showcase your personality in the class?**\n\nChris is enthusiastic about data science and has many experiences in curriculum design. I’m love to learn and can ask great question during interviews about topics that I know little to nothing about. We can feature Chris in interview or headshots.\n\nJonathan has been passion about teaching ever since he left school; first cutting his teeth with web development then transitioning into the complexity of data science. He loves to find new subjects to teach in fun and clever ways. As an example, teaching sorting by lining the students up and running a ‘human’ algorithm or having the students cluster themselves.\n"},{"_id":"535e879b4de898da0684401d","treeId":"535e879b4de898da0684401a","seq":1,"position":1,"parentId":"535e879b4de898da0684401c","content":"## Lesson 1\n\nWhat do we want to teach?\n\nHow do we want to teach it? What experiences, tasks, or exercises \n\nHow can students check their understanding?\n\nHow are we putting students at the center of active learning experiences?\n"},{"_id":"535e879b4de898da0684401e","treeId":"535e879b4de898da0684401a","seq":1,"position":1,"parentId":"535e879b4de898da0684401d","content":"### Opening Image\n###### page 1\nIf you had to choose a photo to establish what \"the world\" is like before your story starts, this is it.\n\nMake it stand out, because first impressions matter. They set the tone and mood and scope, and hook the audience (or reader).\n\nIf it's a vast epic, the opening image should be grand. If it's a small intricate family drama, the opening images are small and subdued."},{"_id":"535e879b4de898da06844023","treeId":"535e879b4de898da0684401a","seq":1,"position":2,"parentId":"535e879b4de898da0684401c","content":"## Lesson 2\n\nWhat do we want to teach?\n\nHow do we want to teach it? What experiences, tasks, or exercises \n\nHow can students check their understanding?\n\nHow are we putting students at the center of active learning experiences?"},{"_id":"41b16ba6f9ccf65ac2000026","treeId":"535e879b4de898da0684401a","seq":1,"position":2.5,"parentId":"535e879b4de898da0684401c","content":""},{"_id":"41b16bf0f9ccf65ac2000027","treeId":"535e879b4de898da0684401a","seq":1,"position":2.75,"parentId":"535e879b4de898da0684401c","content":"## Lesson 3\n\nWhat do we want to teach?\n\nHow do we want to teach it? What experiences, tasks, or exercises \n\nHow can students check their understanding?\n\nHow are we putting students at the center of active learning experiences?\n"},{"_id":"41b16c2af9ccf65ac2000028","treeId":"535e879b4de898da0684401a","seq":1,"position":2.875,"parentId":"535e879b4de898da0684401c","content":"## Lesson 4\n\nWhat do we want to teach?\n\nHow do we want to teach it? What experiences, tasks, or exercises \n\nHow can students check their understanding?\n\nHow are we putting students at the center of active learning experiences?"}],"tree":{"_id":"535e879b4de898da0684401a","name":"Data Visualization Udacity","publicUrl":"data-visualization-udacity"}}