This document discusses creating data visualizations with low-cost tools. It begins by explaining the purpose of data visualization and principles for effective communication through data. Some key principles discussed include defining the question being answered, using accurate data, experimenting with visualization types, and making visualizations as simple as possible. The document then examines tools like Excel, Tableau, Power BI, and coding options for creating visualizations. It provides guidance on when Excel may or may not be suitable and highlights specialized data visualization tools and software. Other resources for learning about data visualization are also listed.
Data Visualization Design Best Practices WorkshopAmanda Makulec
Presentation shared at the #MA4Health Data Visualization workshop cofacilitated with my colleague Tahmid Chowdhury. Our aim was to empower participants with simple principles they can apply to any graph or chart to improve its effectiveness in communicating information, and to share resources on viz design relevant to global health practitioners.
Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Presented on May 7, 2015 by Amanda Makulec from JSI's Center for Health Information, Monitoring, and Evaluation to the TechChange Technology for M&E course.
The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
Real-life Data Visualization - guest lecture for McGill INSY-442Mike Deutsch
Guest lecture given to McGill University undergrad class on Business Intelligence & Analytics, April 2014. Narrative: Data Visualization defined; What *good* visualization is; Visualization in business; a final Exercise in visualizing Higher Education Research data.
Data Visualization Design Best Practices WorkshopAmanda Makulec
Presentation shared at the #MA4Health Data Visualization workshop cofacilitated with my colleague Tahmid Chowdhury. Our aim was to empower participants with simple principles they can apply to any graph or chart to improve its effectiveness in communicating information, and to share resources on viz design relevant to global health practitioners.
Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Presented on May 7, 2015 by Amanda Makulec from JSI's Center for Health Information, Monitoring, and Evaluation to the TechChange Technology for M&E course.
The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
Real-life Data Visualization - guest lecture for McGill INSY-442Mike Deutsch
Guest lecture given to McGill University undergrad class on Business Intelligence & Analytics, April 2014. Narrative: Data Visualization defined; What *good* visualization is; Visualization in business; a final Exercise in visualizing Higher Education Research data.
Visualize your data everywhere! With InfoPlanIT's Visual Analyzer state of the art Business Intelligence Application Suite, you can get control of your companies data, and put reporting, analysis, monitoring (of metrics and KPI's) into the hands of many of your employees!
Presented on May 7, 2015 to the TechChange Technology for M&E course. The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
This presentation contains an introduction of tableau software and in a particular way in Connecting to data, Visual Analytics, Dashboard and stories, Calculations, Mapping and Tableau Online & Competitors.
I recently had the pleasure of sharing some abstract thoughts, practical tips and the differences between visual analysis and data visualisation at the Data Visualisation Forum held in Auckland July 2018.
"Unlock the potential of data with our comprehensive course on mastering Excel, Power BI, Data Science, and more. From organizing and analyzing data in Excel to creating powerful visualizations in Power BI, this course covers everything you need to become a data expert. Dive into the world of data science and elevate your skills to new heights. Enroll now and start mastering data!"
Useful Links:-
https://www.attitudetallyacademy.com/functionalarea/mis-and-data-analytics
https://www.attitudetallyacademy.com/class/pythonda
https://maps.app.goo.gl/GsdeBSajnqXdBaAX6
https://maps.app.goo.gl/1SRGK48QoaPgWbcs5
Applying an intersectionality lens in data scienceData Con LA
Data Con LA 2020
Description
Data analytics is all about asking good questions and seeking informed answers that address a real need. But how do you decide which questions to ask, and whose needs are prioritized? Intersectionality, or overlapping race, gender, and other identity groupings that can result in discrimination, is a powerful framework to guide the design and execution of data science projects across industries and subject areas. An intersectionality framework challenges us to look more deeply at the structural and systemic underpinnings of the data and unpack assumptions around how the data is collected, analyzed, and presented in its context. At every stage of the data science project lifecycle, an intersectionality lens supports equitable results because it accounts for all identities within the scope of the project. In the definition and design phase, this requires attention to sources of subjectivity around the question or experiment to mitigate possible bias. During data collection, where and how data is collected determines whether the experiment is an accurate reflection of the environment and whether the representation of identities present leaves a population of people behind. Throughout the analysis, structuring and classifying data in traditional ways may not recognize the various groups that do not fall under "traditional" categories, opening the door to preconceived notions that can manifest as bias. Finally, this framework raises critical questions during the presentation and delivery phase: how is data from the experiment being framed? When presenting findings, are you subconsciously omitting negative outcomes?
*What is intersectionality?
*Opportunities for Bias in Data Science Projects
*Action Items
Speakers
Daphne Cheung, Disney, Data Scientist
Rachel Whaley, LA Tech4Good
The Art of Data Visualization in Microsoft Excel for Mac.pdfTEWMAGAZINE
As more people turn to the internet and electronic gadgets for their source of information, you can expect data to increase exponentially daily. Data is a result of sharing, collecting, and transmitting information.
A quick reference on designing data visualizations that delight and leverage best practices from the design world to ensure your data is presented in meaningful, usable, fun ways.
Visualize your data everywhere! With InfoPlanIT's Visual Analyzer state of the art Business Intelligence Application Suite, you can get control of your companies data, and put reporting, analysis, monitoring (of metrics and KPI's) into the hands of many of your employees!
Presented on May 7, 2015 to the TechChange Technology for M&E course. The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
This presentation contains an introduction of tableau software and in a particular way in Connecting to data, Visual Analytics, Dashboard and stories, Calculations, Mapping and Tableau Online & Competitors.
I recently had the pleasure of sharing some abstract thoughts, practical tips and the differences between visual analysis and data visualisation at the Data Visualisation Forum held in Auckland July 2018.
"Unlock the potential of data with our comprehensive course on mastering Excel, Power BI, Data Science, and more. From organizing and analyzing data in Excel to creating powerful visualizations in Power BI, this course covers everything you need to become a data expert. Dive into the world of data science and elevate your skills to new heights. Enroll now and start mastering data!"
Useful Links:-
https://www.attitudetallyacademy.com/functionalarea/mis-and-data-analytics
https://www.attitudetallyacademy.com/class/pythonda
https://maps.app.goo.gl/GsdeBSajnqXdBaAX6
https://maps.app.goo.gl/1SRGK48QoaPgWbcs5
Applying an intersectionality lens in data scienceData Con LA
Data Con LA 2020
Description
Data analytics is all about asking good questions and seeking informed answers that address a real need. But how do you decide which questions to ask, and whose needs are prioritized? Intersectionality, or overlapping race, gender, and other identity groupings that can result in discrimination, is a powerful framework to guide the design and execution of data science projects across industries and subject areas. An intersectionality framework challenges us to look more deeply at the structural and systemic underpinnings of the data and unpack assumptions around how the data is collected, analyzed, and presented in its context. At every stage of the data science project lifecycle, an intersectionality lens supports equitable results because it accounts for all identities within the scope of the project. In the definition and design phase, this requires attention to sources of subjectivity around the question or experiment to mitigate possible bias. During data collection, where and how data is collected determines whether the experiment is an accurate reflection of the environment and whether the representation of identities present leaves a population of people behind. Throughout the analysis, structuring and classifying data in traditional ways may not recognize the various groups that do not fall under "traditional" categories, opening the door to preconceived notions that can manifest as bias. Finally, this framework raises critical questions during the presentation and delivery phase: how is data from the experiment being framed? When presenting findings, are you subconsciously omitting negative outcomes?
*What is intersectionality?
*Opportunities for Bias in Data Science Projects
*Action Items
Speakers
Daphne Cheung, Disney, Data Scientist
Rachel Whaley, LA Tech4Good
The Art of Data Visualization in Microsoft Excel for Mac.pdfTEWMAGAZINE
As more people turn to the internet and electronic gadgets for their source of information, you can expect data to increase exponentially daily. Data is a result of sharing, collecting, and transmitting information.
A quick reference on designing data visualizations that delight and leverage best practices from the design world to ensure your data is presented in meaningful, usable, fun ways.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
4. Can be found on the course
page!
Our Objectives
What’s the Purpose of Your
Visualization?
Eight Principles of
Communicating Through
Data
Choosing a Visualization
Is Excel Right for Your
Needs?
Data Visualization
Specialists
Other Options
INTRODUCTION
7. How Can We Highlight What’s Important?
From Children
Now’s “2009
California Report
Card,” showing the
relationship between
race and preschool
enrollment
INTRODUCTION
9. Are You Exploring the Data?
Clearly a trend here! Maybe one here?
PURPOSE OF VISUALIZATION
10. Are You Formatting it For Decision Making?
Are you
presenting a
neutral case so
your audience—
maybe your own
staff members—
can use the info
to make their
own decision?
PURPOSE OF VISUALIZATION
11. Or Are You Telling a Story?
From LSC FY 2017 Budget Request
PURPOSE OF VISUALIZATION
13. 1. Define What Question You’re Answering
“Is the organization
improving on this
metric?”
“How do these
demographics
compare to last year?”
“Are these results
unusual?”
EIGHT PRINCIPLES
14. Don’t just pull data off the
internet without being sure
of their source.
Include the source in your
visualization.
Don’t combine data from
different sources into one
data set.
2. Use Accurate Data
98.7% of all facts
on the Internet
are completely
accurate
Source: The Internet
EIGHT PRINCIPLES
15. 3. Experiment With Ways to Answer
Given your question
and your data, what
are different ways to
visualize the
answer?
• What kind of
visualization?
• Over what time
period?
• Graphing pure
numbers vs.
percentage vs.
percent change?
EIGHT PRINCIPLES
16. 1. Position along a scale
2. Length
3. Slope/ Direction
4. Angle
4. Go with Cognitive Research
Cleveland and McGill, in a seminal paper, created a hierarchical
chart by what is most easily and accurately understood.
10
5
0
5. Area
6. Volume
7. Curvature
EIGHT PRINCIPLES
17. 34
35
36
37
38
39
40
2000 2001 2002 2003 2004 2005 2006 2007
5. Faithfully Represent Your Data
0
5
10
15
20
25
30
35
40
45
2000
2001
2002
2003
2004
2005
2006
2007
In particular, treat your axes with respect.
EIGHT PRINCIPLES
23. Line Charts
Likely your
best choice
to show a
trend,
especially
over time.
Legal Aid Justice Center, 2015, Angela Ciolfi
CHOOSING YOUR VISUALIZATION
24. Bar charts are a classic for a reason—they’re often (usually?)
the best way to communicate data that isn’t right for a line
chart.
Bar Charts
Utah Legal Service’s performance reports
CHOOSING YOUR VISUALIZATION
25. There is virtually
nothing a pie chart
can do that a bar
chart can’t do better.
It’s reasonable as a
graphic way to show
two or maybe three
percentages of a
whole.
Pie Charts
UC Davis Student Demographics
CHOOSING YOUR VISUALIZATION
26. There’s nothing wrong with a simple table of numbers,
especially when communicating with a more sophisticated
audience.
Tables
CHOOSING YOUR VISUALIZATION
27. Scatter plots or bubble charts can effectively show the trend
of a lot of different data points.
Plots
From Microsoft Power BI bubble chart tutorial
CHOOSING YOUR VISUALIZATION
28. Maps can be a
powerful way to
represent data
geographically.
Maps
From PLOS article, Naming and Shaming for Conservation:
Evidence from the Brazilian Amazon, Elías Cisneros ,Sophie
Lian Zhou, Jan Börner
CHOOSING YOUR VISUALIZATION
29. A nifty data visualization that’s unfamiliar to most is very
unlikely to help you achieve your goal: Communicating your
point through data.
What About More Interesting Formats?
CHOOSING YOUR VISUALIZATION
31. Microsoft Excel Part of the Microsoft Office
Suite. $0-$30 per license for
nonprofits on TechSoup.
Installed on Windows, Mac,
or online.
WILL EXCEL WORK FOR YOU?
32. Nearly any kind of static
chart in any format is
possible … if you know
how to find it.
Highly Customizable
WILL EXCEL WORK FOR YOU?
33. Scatterplot
Many Types of New Charts
Bubble Chart
Box and Whiskers
Histogram
WILL EXCEL WORK FOR YOU?
34. You want to just do one basic chart.
You want to do a fair amount of exploration of data
over time.
Your data is coming from several different sources
(especially sources other than Excel).
You want to continuously refresh an online
visualization (like a dashboard).
You want the user to be able to easily interact with the
visualization online.
When Would You Go Beyond Excel?
WILL EXCEL WORK FOR YOU?
36. Infogr.am
A reasonable possibility for creating good
looking charts based on 30+ chart
templates. Free to publish publicly online.
$19/month to download charts or make data
private. Online.
SPECIALISTS
37. Tableau
Download at www.tableau.com
Explore data, create good looking charts,
and share charts and dashboards online.
Free for one data source (which must be
made public). Otherwise $58 per license for
nonprofits on TechSoup. Installed on
Windows or Mac.
SPECIALISTS
38. Tableau has a lot of functionality to allow you to create robust
shared dashboards.
Tableau (cont.)
SPECIALISTS
39. Microsoft Power BI
Microsoft’s Power BI, an online cloud tool, is
quite comparable to Tableau. Free for data
visualization type use.
SPECIALISTS
40. Microsoft Power BI (cont.)
Power BI
also
provides
robust
shared
dashboards.
SPECIALISTS
SPECIALISTS
41. Also Consider…
There are a
number of
comparable
tools: Plot.ly,
Periscope,
Qlikview,
and many
more
Plot.ly
SPECIALISTS
SPECIALISTS
44. Illustration Software
Illustrator Photoshop
Serious creative license requires serious
design software. Illustrator and Photoshop
from Adobe’s Creative Suite are available
for a discount at TechSoup.
OTHER OPTIONS
OTHER OPTIONS
46. If you’re considering complex online visualizations, there are
a number of charting coding libraries—such as D3 or Vega—
to speed up the process.
Online Coding Libraries
Vega
SPECIALISTS
OTHER OPTIONS
48. Stephanie Evergreen http://stephanieevergreen.com/
Enormously practical and useful info on data visualization—a
blog and two books.
Data Viz Tools http://dataviz.tools/
Want way, way, way more options? Here’s a usefully
structured list of 100s of tools to handle many different
aspects of data visualization.
Other Resources