2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Experiences
• Telecom big data Innovation
• Retail Media Network (RMN)
• Customer Data Platform (CDP)
• Know-your-customer (KYC)
• Digital Transformation
• Research
• Data Ops (ML Ops)
• Business Data Analysis, AI
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7. Get ready to your Orange 3
• Open source machine learning and data visualization
• Version: 3.36.2
• https://orangedatamining.com/
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8. Story telling With Data (SWD)
• Always remember Data Comparison!
• Focus on simplicity and ease of interpretation
• The takeaways!
8
https://www.storytellingwithdata.com
11. Allow the labels to be written in a single,
easily readable line
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12. Rainbow palette, overly distracting!
• If the goal is to observe the「fluctuation of commercials across
categories over the five years」, we could better achieve that by
iterating to a different graph type.
• On the other hand, if we’re meant simply to compare the overall
category trends,「toning down the color」usage might be beneficial.
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13. Color in only the year with the highest
number of commercials in each category
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This results in a visually chaotic!
2023
2022
Over-Time
14. The Over-Time means the Line-Graph
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An overly complex visualization with numerous overlapping data series
15. In order of total number of commercials
across all five years of data
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16. Bar charts instead of line graphs, we can
intentionally emphasize that aspect of our data
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The number of commercial advertisers in each category, in each year, is a countable
17. The area graph small multiple chart
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A visualization of this on social media.
It maintains visual interest while facilitating more straightforward
comparisons across categories over several years.
18. A combination of line graphs with descriptive
captions to convey these insights more clearly
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19. A combination of line graphs with descriptive
captions to convey these insights more clearly
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20. A combination of line graphs with descriptive
captions to convey these insights more clearly
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21. Conclusion
• There is no singularly correct approach to data visualization.
• The key is to consider the audience's needs, the context of the
presentation, and the intended message.
• Visualizing data is as much an art as it is a science, requiring
experimentation, iteration, and feedback, rather than adherence to a
strict set of rules.
•All about communications!
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https://www.storytellingwithdata.com/blog
22. What is data visualization?
• Data visualization is the graphical representation of information and
data.
• By using visual elements like charts, graphs, and maps.
• A way to see and understand trends, outliers, and patterns in data.
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23. What is data visualization?
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https://www.tableau.com/learn/articles/data-visualization#advantages-disadvantages
24. 24
The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium
25. 25
The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium
26. Static chart
• There are generally THREE STEPS in drawing a chart:
• Observing the data, determine the relationship, and select the chart.
• What type of data it is, and what content you want to express.
• Category
• Numeric
• Text
• Datetime
• After clarifying the content to be expressed, you can choose which chart to
use to express it.
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27. Pie chart
• You must have some kind of whole
amount that is divided into a number
of distinct parts.
• Your primary objective in a pie chart
should be to compare each group’s
contribution to the whole.
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28. Line chart
• Line charts provide the clearest
graphical representation of time-
related variables and are the
preferred mode for representing
trends or variables over time.
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29. Histogram chart
• It is used to summarize discrete
or continuous data that are
measured on an interval scale.
• It is often used to illustrate the
major features of the distribution
of the data in a convenient form.
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30. Bar chart
• It provides a way of showing
data values represented as
the comparison of multiple
data sets side by side.
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31. Differences between histogram and bar chart
Comparison terms Bar chart Histogram
Usage
To compare different categories of
data.
To display the distribution of a variable.
Type of variable Categorical variables Numeric variables
Rendering
Each data point is rendered as a
separate bar.
The data points are grouped and
rendered based on the bin value.
The entire range of data values is
divided into a series of non-
overlapping intervals.
Space between bars Can have space. No space.
Reordering bars Can be reordered. Cannot be reordered.
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32. Scatter Plot
• It uses dots to
represent values for
two different numeric
variables and observe
relationships between
variables.
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Pearson Correlation
33. Box plot
• Q1: The first quartile (25%) position.
• Q3: The third quartile (75%) position.
• Interquartile range (IQR)
• Lower and upper 1.5*IQR whiskers:
These represent the limits and
boundaries for the outliers.
• Outliers: Defined as observations that
fall below Q1 − 1.5 IQR or above Q3 +
1.5 IQR.
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39. Modify your output file path
• Check each of
Python widget,
change the old
path to your
existing path.
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40. Dataset description (titanic.csv)
• In total with 12 columns.
• A training dataset to
predict whether passengers
will survive in the Titanic
accident.
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41. Data Summary
• Load titanic.csv
• Data description
• Look at Names, Types, Role,
Values in table.
• Change the configurations
of Columns.
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42. Data Summary
• Missing values
• Using the Features
Statistics Widget
• How about those missing
ratios?
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48. Scatter plot
• Using scatter plot widget.
• It used to observe the degree
of correlation between
features
• positive correlation
• negative correlation
• noncorrelation
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49. Box plot
• Using box plot widget.
• Comparing multiple
features with each other
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50. Pivot Table
• Using pivot table widget.
• It summarizes the data
of a more extensive
table into a table of
statistics.
• The statistics can include
sums, averages, counts,
etc.
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51. 1. Show me top 10 data rows
• Hint: Use Data Sampler widget
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52. 2. Show me dataset info
• How many Rows?
• How many Features?
• All information like this!
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54. 4. Survival Conclusion
• For features, SEX, PCLASS, SIBSP,
PARCH, EMBARKED
• Women had a higher chance of survival
than men.
• First-class passengers had a higher
chance of survival.
• Passengers with siblings, spouses had a
higher chance of survival.
• Passengers with children and parents
had a higher chance of survival.
• Departing from the S terminal may
lead to lower cabin class and lower
chances of survival.
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56. 6. Look at survival rate by SEX and PCLASS
• Women in first class had a survival rate as high as 96.8%. In contrast,
men in economy class only had a 13.54% chance of survival
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57. 7. Look at survival rate by SEX, AGE and
PCLASS
• In the event of a disaster, women in
first class or business class have a 90%
chance of survival regardless of age.
• On the other hand, if a man is in
economy class and older than 18, the
chance of survival is only 13.36%.
• To summarize, in a disaster scenario,
girls and women have a higher chance
of survival compared to boys and men.
• Additionally, the higher the class (such
as first class), the higher the chances
of survival.
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58. 8. The price paid of each class
• Try to plot Pclass and Fare chart
to visualize data
• Every seat had someone board
for free, while others spent over
500 pounds for a first-class
ticket. It's quite an interesting
observation!
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59. 9. Visualizing data and express your thoughts
• Using today’s teaching knowledge and referencing
Story_telling_with_data.pdf, please visualize and analysis this data
(20240320_HW.csv) with the theme of sales.
• Based on your observations, explain the relationship between sales
and these variables.
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