The document discusses the importance of data visualization in data science, highlighting its role at various stages of the data science process and how it aids in understanding data and engaging audiences. It covers visual encoding techniques, biases in visualization, and the tools available for creating effective visualizations. Additionally, it outlines design principles and narrative structures essential for conveying insights through data.

1. Data Visualization in
Data Science
Maloy Manna
biguru.wordpress.com linkedin.com/in/maloy twitter.com/itsmaloy

2. Synopsis
Having data is not enough. Adding context to data is essential to understand the
data, find patterns and engage audiences. Data visualization is a key element of data
science, the interdisciplinary field which deals with finding insights from data.
• In this webinar, we explore the roles of data visualization at different stages of
the data science process, and why it is essential.
• We also look at how data is encoded visually with shape, size, color and other
variables and also the basic principles of visual encoding can be applied to build
better visualizations.
• We cover narratives, types of bias and maps.
• Finally we look at how various tools – both open source and off-the-shelf
software that’s used in data science to build effective data visualizations.

3. Speaker profile
Maloy Manna
Project Manager - Engineering
AXA Data Innovation Lab
• Over 14 years experience building data driven products and services
• Previous organizations: Thomson Reuters, Saama, Infosys, TCS
biguru.wordpress.com linkedin.com/in/maloy twitter.com/itsmaloy

4. Contents
 Defining Data visualization
 Data science process
 Data visualization
 Visual encoding of data
 Narrative structures
 Dataviz Technology & Tools

5. Defining Data visualization
• Visual display of quantitative information
• Mapping data to visual elements
• Encoding data with size, shape, color...
• Storytelling / narrative elements

6. Defining Data Visualization
Exploratory
• Find insights
• Conversation between data and “you”
Explanatory
• Present insights

7. Data science project life-cycle
• Acquire data
• Prepare data
• Analysis &
Modeling
• Evaluation &
Interpretation
• Deployment
• Operations &
Optimization

8. Data science process
Data Wrangling
EDA:
Exploratory
Data Analysis
Data Visualization
ExplanatoryExploratory
Source: Computational Information Design | Ben Fry

9. Exploratory data visualization
Data analysis approaches:
Classical:
Problem > Data > Model > Analysis > Conclusions
EDA: [Exploratory Data Analysis]
Problem > Data > Analysis > Model > Conclusions
Bayesian:
Problem > Data > Model > Prior distribution > Analysis > Conclusions
EDA = approach, not a set of techniques

10. Exploratory data visualization
Statistical approaches:
• Quantitative
• Hypothesis testing
• Analysis of variance (ANOVA)
• Point estimates and confidence intervals
• Least squares regression
• Graphical
• Scatter plots
• Histograms
• Probability plots
• Residual plots
• Box plots
• Block plots

11. Exploratory data visualization
Graphical
• Scatter plots
• Histograms
• Probability plots
• Residual plots
• Box plots
• Block plots

12. Exploratory data visualization
Graphical analysis procedures:
• Testing assumptions
• Model selection
• Model validation
• Estimator selection
• Relationship identification
• Factor effect determination
• Outlier detection
MUST USE for deriving insights from data

13. Exploratory data analysis
Anscombe's quartet
N=11
Mean of X = 9.0
Mean of Y = 7.5
Intercept = 3
Slope = 0.5
Residual standard deviation = 1.237
Correlation = 0.816

14. Exploratory data analysis

15. Explanatory data visualization
 Design
 Engineering
 Journalism

16. Explanatory data visualization
Visualization is both an art and science
• Harry Beck's subway map of London

17. Visual encoding of data
Data Types
• Quantitative
• Continuous, Discrete
• Categorical
• Nominal, Ordered, Interval

18. Visual encoding of data
Categorical scales and graph design

19. Visual encoding of data
Bandwidth of our senses: [Tor Norretranders]

20. Visual encoding of data
Data → visual display elements
• Position x
• Position y
• Retinal variables
• Size, Orientation (ordered data)
• Color Hue, Shape (nominal data)
• Animation

21. Visual encoding of data
Ranking visual display elements (framework):
1. Position along a common-scale e.g. scatter plots
2. Position on identical but non-aligned scales
E.g. multiple scatter plots
3. Length e.g. bar chart
4. Angle & Slope e.g. pie-chart
5. Area e.g. bubbles
6. Volume, density & color saturation e.g. heat-map
7. Color hue e.g. highlights
Ref. Graphical Perception & graphical methods for analyzing scientific data – William
Cleveland & Robert McGill (1985)

22. Design principles
 Choose the right type of chart
• Trends / Change over time → Line charts
• Distributions → Histograms
• Summary Information → Table
• Relationships → Scatter Plots
 Get it right in black & white (before adding color)
 Prefer 2D to 3D for statistical charts
 Use color to highlight
 Avoid rainbow palette
 Avoid chartjunk : “less is more”
 Try to have a high data-ink ratio

23. Design principles
 Choose the right type of chart
Ranking
Time-series Deviation
Correlation Nominal comparison

24. Narrative structures
Data Journalism
Traditional journalism Data journalism
• Data around narrative • Narrative around data
• Linear flow • Complex, often non-linear flow
• Physical static media • Online interactive media

25. Narrative structures

26. Narrative structures

27. Narrative structures
Bias (and ethics: Don’t lie with data)
Bar-charts must have a zero-baseline
 Present data in its context

28. Narrative structures
Bias: Misleading with data
 Selective presentation with line-charts • Author Bias
• Data Bias
• Reader Bias

29. Narrative structures
Bias and Errors (statistics):
• Selection bias e.g. in sampling
• Omitted-variable bias
Errors:
• Hypothesis testing
• Null Hypothesis = default/no-effect state
Null Hypothesis H0 Valid Invalid
Reject Type I error
• False positive
Correct inference
• True positive
Accept Correct inference
• True negative
Type II error
• False negative

30. Narrative structures
Storytelling:
 Visual narratives have moved from author-driven to viewer-
driven with use of highly interactive media for data visualization
Author driven Viewer driven
Strong ordering Exploratory
Heavy messaging Ability to ask questions
Need for clarity and speed Build own story
Author-driven Viewer-driven

31. DataViz Technologies & Tools
Off-the-shelf:
 Tableau, Qlikview
Tools:
 Predefined charts: Raw, Chartio, Plotly
 Google fusion tables, Excel, Gephi
Code & Javascript libraries:
 R ggplot2, ggvis, rCharts + shiny(interactive apps)
 Python matplotlib,
 D3.js, Dimple.js, Leaflet, Rickshaw (use JSON data)
 Linux gnuplot

32. DataViz Technologies & Tools
Tableau data viz

33. DataViz Technologies & Tools
Chart in R ggplot2

34. References
Visual display of Quantitative Information: Edward Tufte http://goo.gl/qb5ej
Exploratory Data Analysis: John Tukey http://goo.gl/tV57HP
Data Science Life cycle : Maloy Manna
http://www.datasciencecentral.com/profiles/blogs/the-data-science-project-lifecycle
Selecting right graph for your message: Stephen Few
www.perceptualedge.com/articles/ie/the_right_graph.pdf
Practical rules for using color in charts: Stephen Few
www.perceptualedge.com/articles/visual.../rules_for_using_color.pdf
OpenIntro Statistics: https://www.openintro.org/stat/
Misleading with statistics: Eric Portelance
https://medium.com/i-data/misleading-with-statistics-c63780efa928
Computational Information Design: Ben Fry
http://benfry.com/phd/dissertation-050312b-acrobat.pdf