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Data VisualizationPractices and examples
Luis Moneda, Data Scientist at Nubank
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About meAcademic
- MSc in Computer Science student (IME-USP)- Bachelor in Computer Engineering (Poli-USP)- Bachelor in Economics (FEA-USP)
Work & activities
- Data Scientist at Nubank (2017 - Current)- Teaching Machine Learning for MBA courses at FIA- Udacity mentor and project reviewer for data related
courses- Nubank Machine Learning meetup organizer - Kaggler (competitions and datasets)- Twitter and Blog: @lgmoneda and lgmoneda.github.io
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Outline1. Goal 2. Problems & Practices 3. Types4. Data Science
a. EDAb. PCAc. t-SNE
5. Tools 6. Example7. Resources
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Resources and References
Books- The visual display of quantitative information. Edward R.
Tufte. - Visual Explanations: Images and Quantities, Evidence and
Narrative. Edward R. Tufte. - Data Visualization, A practical Introduction. Kieran Healey
(http://socviz.co/)Links
- Pandas for plotting: https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html
- Visual Vocabulary (types of plots and what they are good for): https://journalismcourses.org/courses/DE0618/Visual-vocabulary.pdf
- Your Friendly Guide to Colors in Data Visualisation: https://blog.datawrapper.de/colorguide/
- The Python Graph Gallery: https://python-graph-gallery.com/- Fundamentals of Data Visualization:
https://serialmentor.com/dataviz/introduction.html
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Goal: efficient communication
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Goal
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Goal
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Goal
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Problems & Practices
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Problems & Practices
Problems Practices
- Bad taste- Bad data- Bad perception
- Labeling- Plot Design- Context- Honest- Self sufficiency (in terms of data!)- Right plot type
The more complex is the idea you want to communicate, more successful you would need to be in "clarity, precision, and efficiency".
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Problems & Practices
Reference: https://serialmentor.com/dataviz/introduction.html
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Problems - Scale
Reference: https://serialmentor.com/dataviz/coordinate-systems-axes.html
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Problems - Scale
Reference: https://serialmentor.com/dataviz/proportional-ink.html
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Problems - Distribution
Reference: https://serialmentor.com/dataviz/histograms-density-plots.htmlReference: https://serialmentor.com/dataviz/histograms-density-plots.html
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Problems - 3D
Reference: https://serialmentor.com/dataviz/no-3d.html
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Problems - Combining
Reference: https://serialmentor.com/dataviz/no-3d.html
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ideiasArrangements of points and lines on a page can encourage us—sometimes quite unconsciously—to make inferences about similarities, clustering, distinctions, and causal relationships that might or might not be there in the numbers. Sometimes these perceptual tendencies can be honestly harnessed to make our graphics more effective. At other times, they will tend to lead us astray, and must take care not to lean on them too much.
In short, good visualization methods offer extremely valuable tools that we should use in the process of exploring, understanding, and explaining data. But they are not some sort of magical means of seeing the world as it really is. They will not stop you from trying to fool other people if that is what you want to do; and they may not stop you from fooling yourself, either.
We will not automatically get the right answer to our questions just by looking.
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Reference: http://socviz.co/lookatdata.html#why-look-at-data
Principle: Maximize data-to-ink ratio
Six Kinds of summary boxplots. Type (c) is from Tufte
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Principle: Colors
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Examples
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Bad examples
Reference: https://chartio.com/learn/dashboards-and-charts/what-is-the-difference-between-a-pie-chart-and-a-bar-chart/
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Bad examples
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Bad examples
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Bad examples
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Bad examples
Reference: https://omegadescent.wordpress.com/2018/06/11/seaborn1/
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Does it really happen?
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Examples
Reference: all the following examples are from open Udacity projects in Github.
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Good examples!
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Examples
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Examples
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Good examples
Reference: https://omegadescent.wordpress.com/2018/06/11/seaborn1/
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Examples
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Examples
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Types
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Types - Deviation
Diverging stacked bar Surplus/deficit filled line
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Types - Correlation
Reference: (left) https://serialmentor.com/dataviz/visualizing-amounts.html (right) https://serialmentor.com/dataviz/visualizing-associations.html
Heatmap Scatterplot
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Types - RankingOrdered bar Ordered column
Reference: (left) https://serialmentor.com/dataviz/visualizing-amounts.html (right) https://serialmentor.com/dataviz/visualizing-uncertainty.html
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Types - Distribution
Population pyramid Box plot
Reference: (left) https://serialmentor.com/dataviz/histograms-density-plots.html (right) https://serialmentor.com/dataviz/boxplots-violins.html
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Types - Change overtime
Reference: https://serialmentor.com/dataviz/time-series.html
Line plot Area chart
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Types - Part-to-whole
Tree map
Reference: (left) https://serialmentor.com/dataviz/nested-proportions.html (right) https://serialmentor.com/dataviz/visualizing-proportions.html
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Types - FlowSankey
Reference: https://serialmentor.com/dataviz/nested-proportions.html
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Data Science
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Data ScienceExploratory Data Analysis
A series of hypothesis and procedures to gather evidence to them or suggest new ones.
If the problem is well defined (you know the target, it's just a matter of modeling), the hypothesis should be mostly about the validity of your data in order to feed a model.
If you're exploring a bunch of data to look for an opportunity, you need to plot them business oriented and understand how the data relates to the business.
Results and presentation
Communicating a project result - Storytelling.
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EDAHypothesis: a specific features distribution follows what I expect considering my business knowledge.
Conclusion: yes, it follows.
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EDAHypothesis: different categories from a variable have different target proportion and they differ by gender.
Conclusion: yes, different classes have different averages and for some it's gender sensitive.
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EDAHypothesis: test set distribution follows the train set one.
Conclusion: yes, it follows.
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EDA Examples
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EDAKaggle is a great place to check EDA examples!
Warnings:
- People tend to do some useless plots there to build long kernels
- Not everyone follow the best practices for plots- They try to do to fancy plots to impress the
community and get upvotes
Instacart Market Basket Analysis competition.
Reference: https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart
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EDA
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EDA
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PCA and t-SNE
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Data Science - PCAPrincipal Component Analysis
Reference: https://serialmentor.com/dataviz/visualizing-associations.html
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Data Science - t-SNEt-Distributed Stochastic Neighbor Embedding
Reference: http://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
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MNIST
Reference: https://towardsdatascience.com/visualising-high-dimensional-datasets-using-pca-and-t-sne-in-python-8ef87e7915b
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Tools
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Tools
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Notebook examples & Hands-on
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TakeawaysIt's hard to define a recipe for data visualization, but keep in mind the general idea of clarity, precision and efficiency:
- Plot for a reason, take conclusions from every plot or discard them- State a plot reason before design it, the type plays an important role- Use it to fastly frame important business reflection on the data- Don't use pie charts!- The audience should be able to understand your plot without further info! - Don't bother about tool syntax, plots are probably the most googled part of a data scientist job.