data visualization

1. EDA Visualization
Orozco Hsu
2023-10-31
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2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
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3. Tutorial
Content
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Data Science Process
Exploration Data Analysis and Visualization
Home work

4. Code
• Download materials:
• https://drive.google.com/drive/folders/1KPC3K19_vJgRb5Op5M9bqQYJEh2zv
rnr?usp=sharing
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5. Get ready to your Orange 3
• Version: 3.36.1
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6. 6
The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium

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The Pyramid of Data Needs (and why it matters for your career) | by Hugh Williams | Medium

8. 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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9. 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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10. 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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11. 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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12. 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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13. 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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14. Scatter Plot
• It uses dots to
represent values for
two different numeric
variables and observe
relationships between
variables.
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15. 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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16. Dataset description
• Using this dataset to
predict whether passengers
will survive the Titanic
accident
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17. Data Summary
• Load titanic.csv
• Data description
• Names, Types, Role, Values
• Change the Columns
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18. Data Summary
• Missing values
• Using Features Statistics
Widget
• How about those missing
ratios?
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19. Preprocess (Remove or Impute columns)
• Remove columns
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20. Preprocess (Remove or Impute columns)
• Impute columns
• For Default Method
• For each column
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21. Pie chart
• Orange 3 has deprecated
Pie chat widget
• Use python script instead
• Find the output file
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22. Line chart
• Typically, trend analysis
charts are presented
together with time-based
data
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23. Distribution chart
• Used to present by sorting
frequency
• In Orange 3, both of numeric
or category data can be
presented here
• Bar chart widget is not used
much compared to others
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24. Scatter plot
• It used to observe the degree
of correlation between
features
• positive correlation
• negative correlation
• noncorrelation
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25. Box plot
• Comparing multiple
features with each other
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26. Pivot Table
• 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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27. 1. Show me top 10 data rows
• Hint: Use Data Sampler widget
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28. 2. Show me dataset info
• How many Rows?
• How many Features?
• All information like this!
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29. 3. Get a count of the number of survivors
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30. 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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31. 5. Show me sex survival rate
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32. 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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33. 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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34. 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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