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Coursera Courses Notes > Course-6 Share Data Through the Art of Visualization > Flashcards

Course-6 Share Data Through the Art of Visualization Flashcards

1
Q

Data Visualization

A

The Graphic representation and Presentation of data.

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2
Q

Data Visualizations process starter

A

1)Looking at visuals in order to understand and draw conclusions about data.
2) Creating visual using raw data to tell a story.

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3
Q

The Four elements of effective data Visualizations

A

1) Information (Data)
2) The Story ( Concept)
3) The Goal ( Function)
4) The Visual Form ( Metaphor)

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4
Q

Examples of Data Visualizations

A

1) Bar Graphs
2) Line Graphs
3) Pie Charts
4) Maps
5) Histograms
6)Correlation Charts

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5
Q

Bar Graphs

A

Use Size contrast to compare two or more values.

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6
Q

Line Graphs

A

Help your audience understand shifts or changes in your data.

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7
Q

Pie charts

A

Show how much each part of something makes up the whole.

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8
Q

Maps

A

Help organize data Geographically

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9
Q

Histogram

A

A chart that shows how often data values fall into certain ranges.

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10
Q

Correlation charts

A

Show relationships among data

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11
Q

Causation

A

Occurs when an action directly leads to an outcome.

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12
Q

Static Visualizations

A

Do not change over time unless they are edited.

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13
Q

Dynamic Visualizations

A

Visualizations that are interactive or change over time.

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14
Q

Tableau

A

A business intelligence and analytics platform that helps people see, understand, and make decisions with data.

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15
Q

A decision Tree

A

Is a flowchart that you can use to help frame decisions as a series of smaller yes/no decisions.

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16
Q

The elements of art

A
  • Line
  • Shape
  • Color
  • Space
  • Movement
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17
Q

Colors

A

They can be described by their
- Hue
- Intensity
- Value

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18
Q

Data Composition

A

Combining the individual parts in a visualization and displaying them together as a whole.

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19
Q

Over time data Ex: Clicks since Jan 2020

A
  • Line Graphs
  • Bar Graphs
  • Stacked Bar Graphs
  • Area Graphs
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20
Q

Between Objects Ex: Mobile vs. Desktop traffic

A
  • Ordered Bar
  • Ordered Column
  • Grouped Bar
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21
Q

Composition Ex: What percentage of our traffic comes from each platform?

A
  • Stacked Bar
  • Pie Chart
  • Donut
    -Tree-map
  • Stacked Area
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22
Q

Relationships Ex: How has clicks increased with increased spend?

A
  • Scatterplot Chart
  • Bubble Chart
  • Column/ Line Chart
  • Heat-Map
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23
Q

Elements for effective visuals

A
  • Clear meaning
  • Sophisticated use of contrast
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23
Q

Nice Basic Principles of design

A
  • Balance
  • Emphasis
  • Movement
    -Pattern
  • Reputation
  • Proportion
  • Rhythm
    -Variety
  • Unity
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24
Q

Balance

A

Refers to the distribution of visual elements in a way that creates a sense of equilibrium.

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25
Q

Balance example

A

If you have a bar chart with categories of different sizes, you can balance it by putting the biggest bar on one side and the smaller ones on the other side.

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26
Q

Emphasis

A

Refers to the visual element or elements that stand out the most in the design.

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27
Q

Emphasis example

A

A scatter plot, you can emphasize the data points by making them bigger or a different color.

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28
Q

Movement

A

Refers to the way the eye movies through the design, following a path or series of paths.

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29
Q

Movement example

A

A Line chart, you can create movement by having the line curve or change direction.

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30
Q

Pattern

A

Refers to the repetition of elements in a design, creating a visual structure.

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30
Q

Pattern

A

Refers to the repetition of elements in a design, creating a visual structure.

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31
Q

Pattern example

A

A Stacked bar Chart, you can create a pattern by repeating the same color scheme for each category.

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32
Q

Repetition

A

Refers to the repetition of elements in a design, creating a sense of cohesiveness.

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33
Q

Proportion

A

Refers to the size relationship between elements in a design.

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34
Q

Proportion example

A

In a Pie chart, you can maintain proportion by making sure that each slice is proportional to the data it represents.

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35
Q

Rhythm

A

Refers to the repetition of elements with variation, creating a sense of movement.

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36
Q

Variety

A

Refers to the use of different elements in a design to create interest.

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37
Q

Variety example

A

A line chart, you can create variety by using different line styles and colors for different categories.

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38
Q

Unity

A

Refers to the sense of wholeness and coherence in a design.

39
Q

Unity example

A

A scatter plot, you can create unity by using the same color palette for data points and the background.

40
Q

Design thinking

A

A Process used to solve complex problems in a user-centric way

41
Q

Five Phases of the design process

A
  • Empathize
  • Define
  • Ideate
    -Prototype
  • Test
42
Q

Empathize

A

Thinking about the emotions and needs of the target audience for the data visualizations.

43
Q

Define

A

Figuring out exactly what your audience needs from the data.

44
Q

Ideate

A

Generating ideas for data visualizations

45
Q

Prototype

A

Putting visualizations together for testing and feedback.

46
Q

Test

A

Showing prototype visualizations to people before stakeholders see them.

47
Q

Line

A

A line is a continuous mark on a surface that defines an edge or contour. In data visualizations, lines can be used to show trends or patterns over time, such as line graphs.

48
Q

Shape

A

A shape is a two-dimensional area that is defined by an outline or boundary. In data visualization, shapes can be used to represent data points, such as in scatter points or bar charts.

49
Q

Color

A

Colors refers to the hue, saturation, and brightness of a visual element. In data visualization, color can be used to represent different categories or to highlight important data points.

50
Q

Space

A

Space refers to the area around, between, and within visual elements. In data visualizations, space can be used to separate data points or to show the relative size of different data points.

51
Q

Movement

A

Movement refers to visual illusion of motion created by the arrangement of visual elements. In data visualization, movement can be used to show changes over time or to simulate the flow of data.

52
Q

Headline

A

A line of words printed in large letters at the top of the visualization to communicate what data is being presented.

52
Q

Headline

A

A line of words printed in large letters at the top of the visualization to communicate what data is being presented.

53
Q

Subtitle

A

Supports the headline by adding more context and description.

54
Q

Legend

A

Identifies the meaning of various elements in a data visualization.

55
Q

Annotation

A

An annotation briefly explains data or helps focus the audience on a particular aspect of the data in a visualizations.

56
Q

Ways to make data visualizations accessible:

A
  • Labeling
  • Text alternatives
  • Text-based format
  • Distinguishing
    -Simplify
57
Q

Alternative text

A

Alternative text provides a textual alternative to non-text content.

58
Q

Types of data in Tableau

A

-#: Numeric data
-Abc: String data
- Globe: Geographic data
- Calendar: Date data
- Calendar with a clock: Date and time data.

59
Q

Diverging color palette

A

Displays two ranges of values using color intensity to show the magnitude of the number and the actual color to show which range the number is from.

60
Q

Dashboard

A

A tool that organizes information from multiple datasets into one central location for tracking, analysis, and simple visualization.

61
Q

Dashboard criteria

A

A tool for showing only the data that meets a specific criteria while hiding the rest.

62
Q

Data storytelling

A

Communicating the meaning of a dataset with visuals and a narrative that are customized for each particular audience.

63
Q

3 data storytelling steps

A
  • Engage your audience
  • Create compelling visuals
  • Tell the story in an interesting narrative.
64
Q

Engagement

A

Capturing and holding someone’s interest and attention.

65
Q

Questions to consider while telling a story using data.

A
  • What role does this audience play?
  • What is their stake in the project?
  • What do they hope to get from the data insights I deliver?
66
Q

Spotlighting

A

Scanning through data to quickly identify the most important insights.

67
Q

Dashboard

A

A tool that organizes information from multiple datasets into one central location for tracking, analysis, and simple visualization through tables, charts and graphs.

68
Q

Static data

A

Involves providing screenshots or snapshots in presentations or building dashboards using snapshots of data.

69
Q

PROS

A
  • Can tightly control a point- in-time narrative of the data and insight.
  • Allows for complex analysis to be explained in-depth to a larger audience.
70
Q

CONS

A
  • Insights immediately begins to lose value and continues to do so the longer the data remains in a static state.
  • Snapshots can’t keep up with the pace of data change.
71
Q

Live data

A

means that you can build dashboards, reports, and views connected to automatically updated data.

72
Q

Pros

A

-Dashboards can be built to be more dynamic and scalable.
- Gives the most up-to-date data to the people who need it at the time when they need it.
- Allows for up-to-date curated views into data with the ability to build a scalable “ single source of truth” for various cases.

  • Allows for immediate action to be taken on data that changes frequently.

-Alleviates time/ resources spent on processes for every analysis.

73
Q

CONS

A
  • Can take engineering resources to keep pipelines live and scalable, which may be outside the scope of some companies data resources allocation.
74
Q

Narrative Factors

A
  • Characters
  • Setting
  • Plot
  • Big reveal
  • Aha movement
74
Q

Narrative Factors

A
  • Characters
  • Setting
  • Plot
  • Big reveal
  • Aha movement
75
Q

Hypothesis

A

The theory you are trying to prove or disprove with data.

76
Q

The McCandless Method

A

1) Introduce the graphic by name.
2) Answer obvious questions before they are asked.
3) State the insight of your graphic.
4) Call out data to support that insight
5) Tell your audience why it matters.

Then Present the possible business impact of the solution and clear actions stakeholders can take.

77
Q

Data analyst responsibilities

A

1) Analyze the data
2) Present your findings effectively.

78
Q

Presentation tips

A

1) Channel your excitement.
2) Start with the broader ideas.
3) Use the five second rule
- Wait five seconds after showing a data visualization.
- Ask if they understand
- Give your audience another five seconds.
- Tell them the conclusion
4) Preparation is key

79
Q

Your audience

A
  • Will not always see the steps you took to reach a conclusion.
  • Has a lot on their mind.
  • Is easily distracted
80
Q

How you speak

A
  • Keep your sentences short
  • Build in intentional pauses
  • Keep the pitch of your sentences level
81
Q

Be mindful of nervous habits

A
  • Stay still and move with purpose
  • Practice good posture
  • Make positive eye contact
82
Q

Things to remember

A

Remember that these are skills that you can practice with every presentation.

83
Q

How to prepare for a presentation?

A
  • Understand your stakeholder’s expectations.
  • Make sure you have a clear understanding of the objective and what the stakeholders wanted.
  • If you misunderstood your stakeholder’s expectations or the project objectives, you won’t be able to correctly answer the questions.
    -Start with zero assumptions
84
Q

The colleague test

A

Do a test-run of your presentation.

85
Q

Start with zero assumptions

A

Don’t assume that your audience is already familiar with jargon, acronyms, past events , or other necessary background information.

86
Q

Be Prepared to consider any limitations of your data by:

A
  • Critically analyzing the correlations.
  • Looking at the content
  • Understanding the strengths and weakness of the tools.
87
Q

Types of objections

A
  • About the data
  • About your analysis
  • ## About your findings
88
Q

Objections about the data

A
  • Where you got the data?
  • What systems it came from?
  • What transformations happened to it?
  • How fresh and accurate is the data?
89
Q

Objections about your analysis

A
  • is your analysis reproducible?
  • Who did you get feedback from?
90
Q

Objections about your findings?

A
  • Do these findings exist in previous time periods?
  • Did you control for the differences in your data?
91
Q

Responding to possible objections

A

-Communicate any assumptions
- Explain why your analysis might be different than expected.
- Acknowledge that those objections are valid and take steps to investigate further.

92
Q

More tips to prepare for Q and A

A
  • Listen to the whole question
  • Repeat the question ( If necessary)
  • Understand the context
  • Involve the whole audience
  • Keep your responses short and to the point
93
Q

Important aspects of a presentation

A
  • define your purpose
  • keep it concise
  • Have some logical flow to your presentation.
  • Make the presentation visually compelling.
  • How easy is it to understand?
94
Q

X axis

A

Used to represent Categories, time periods and other variables.

95
Q

Y axis

A

It has the scale of values for the variables.

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