This presenations provides an outlook of what we anticipate with the structured data hub: to create linkable datasets, enhance the use of provenance, add quality flags to data, answer new questions and finally, borrow from and provide to public sources such as dbpedia
Graph Database is the new paradigm of Big Data.
New insights are discovered in the connected data.
Fabricating Big Data into connected data is the cutting edge technology.
Graph database is the driver for sustainable growth in the Era of Big Data.
Graph Data is already prevailing among the global leading companies.
Graph Database will pass the dawn of standards.
The most widely adopted method will be the Hybrid Database.
Each company needs to prepare for the wave of change.
AgenGraph will support your business with superior capabilities.
For more information, please visit www.bitnine.net
1) Definition of Data visualization-Representation and prese.docxcuddietheresa
1) Definition of Data visualization-
Representation and presentation of data to facilitate understanding(Kirk, 2019, p. 29)
Data visualization is the representation of data or information in a graph, chart, or other visual format. It communicates relationships of the data with images. This is important because it allows trends and patterns to be more easily seen. With the rise of big data upon us, we need to be able to interpret increasingly larger batches of data. (Import.io, 2019)
The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. (Nediger, 2020)
2) Key Components of Data Visualization-
Following are the Key components of Data Visualization,
a) Visual Representation- This will involve making decisions on how you will like to portray the data collected.This can be in the various forms,
Charts - Bar charts, Line charts, Pie charts
Maps
Table - Pivot table
Summarization Bar - Can be used in financial application when you want to see summary of the amount spent in a specific month/year/day.
b) Presentation- Presentation of the data is how do we package up the final product/Data graph.
c) Facilitate Understanding- Making it easy to understand for the audience who will be reading and consuming this data.
3) What techniques do I hope to learn from this course?
I would like to learn as many data visualization tools (Example- Tableau, SAS Business Intelligence, Google Data Studio) as I can which will help me analyze massive data and make data driven decisions to improve my company's business/processes.
Would like to learn some techniques like how can we make a data graph dynamic this will help to automate reporting when the data is refreshed real time.
Reference-
Kirk, A. (2019).
Data visualisation: A handbook for data driven design
. SAGE Publications.
Import.io. (2019, October 28).
What is Data Visualization and Why Is It Important?
Import.io.
https://www.import.io/post/what-is-data-visualization/
Nediger, M. (2020, June 05).
What is Data Visualization?(Definition, Examples, Best Practices)
Nediger.
https://venngage.com/blog/data-visualization/#1
I need to comment on this
.
Dataviz presentation at ThingsKamp2015 IstanbulCédric Lombion
Dataviz presentation at ThingsKamp2015 Istanbul. Intended for newcomers to information visualisation, with the test of the first prototype of a dataviz card game.
This presenations provides an outlook of what we anticipate with the structured data hub: to create linkable datasets, enhance the use of provenance, add quality flags to data, answer new questions and finally, borrow from and provide to public sources such as dbpedia
Graph Database is the new paradigm of Big Data.
New insights are discovered in the connected data.
Fabricating Big Data into connected data is the cutting edge technology.
Graph database is the driver for sustainable growth in the Era of Big Data.
Graph Data is already prevailing among the global leading companies.
Graph Database will pass the dawn of standards.
The most widely adopted method will be the Hybrid Database.
Each company needs to prepare for the wave of change.
AgenGraph will support your business with superior capabilities.
For more information, please visit www.bitnine.net
1) Definition of Data visualization-Representation and prese.docxcuddietheresa
1) Definition of Data visualization-
Representation and presentation of data to facilitate understanding(Kirk, 2019, p. 29)
Data visualization is the representation of data or information in a graph, chart, or other visual format. It communicates relationships of the data with images. This is important because it allows trends and patterns to be more easily seen. With the rise of big data upon us, we need to be able to interpret increasingly larger batches of data. (Import.io, 2019)
The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. (Nediger, 2020)
2) Key Components of Data Visualization-
Following are the Key components of Data Visualization,
a) Visual Representation- This will involve making decisions on how you will like to portray the data collected.This can be in the various forms,
Charts - Bar charts, Line charts, Pie charts
Maps
Table - Pivot table
Summarization Bar - Can be used in financial application when you want to see summary of the amount spent in a specific month/year/day.
b) Presentation- Presentation of the data is how do we package up the final product/Data graph.
c) Facilitate Understanding- Making it easy to understand for the audience who will be reading and consuming this data.
3) What techniques do I hope to learn from this course?
I would like to learn as many data visualization tools (Example- Tableau, SAS Business Intelligence, Google Data Studio) as I can which will help me analyze massive data and make data driven decisions to improve my company's business/processes.
Would like to learn some techniques like how can we make a data graph dynamic this will help to automate reporting when the data is refreshed real time.
Reference-
Kirk, A. (2019).
Data visualisation: A handbook for data driven design
. SAGE Publications.
Import.io. (2019, October 28).
What is Data Visualization and Why Is It Important?
Import.io.
https://www.import.io/post/what-is-data-visualization/
Nediger, M. (2020, June 05).
What is Data Visualization?(Definition, Examples, Best Practices)
Nediger.
https://venngage.com/blog/data-visualization/#1
I need to comment on this
.
Dataviz presentation at ThingsKamp2015 IstanbulCédric Lombion
Dataviz presentation at ThingsKamp2015 Istanbul. Intended for newcomers to information visualisation, with the test of the first prototype of a dataviz card game.
Towards a rebirth of data science (by Data Fellas)Andy Petrella
Nowadays, Data Science is buzzing all over the place.
But what is a, so-called, Data Scientist?
Some will argue that a Data Scientist is a person able to report and present insights in a data set. Others will say that a Data Scientist can handle a high throughput of values and expose them in services. Yet another definition includes the capacity to create meaningful visualizations on the data.
However, we enter an age where velocity is a key. Not only the velocity of your data is high, but the time to market is shortened. Hence, the time separating the moment you receive a set of data and the time you’ll be able to deliver added value is crucial.
In this talk, we’ll review the legacy Data Science methodologies, what it meant in terms of delivered work and results.
Afterwards, we’ll slightly move towards different concepts, techniques and tools that Data Scientists will have to learn and appropriate in order to accomplish their tasks in the age of Big Data.
The dissertation is closed by exposing the Data Fellas view on a solution to the challenges, specially thanks to the Spark Notebook and the Shar3 product we develop.
Data visualization is crucial to understanding the big data being generated by apps and services. Data visualization toolkits such as D3.js and charting toolkits are immensely popular but it remains difficult to create meaningful dashboards or usable analytics tools or clear data visualizations. This talk will discuss data visualization principles, present best practices, showcase excellent visualizations in practice, and share useful tips and mistakes learned.
Semantic Days ’13 and potential conference crashesAndré Torkveen
When planning a conference it’s essential to arrange the preparations so that your event doesn’t end up in a timing conflict with potentially competing events. Since our [Semantic Days] conference targets an audience who are engaged within the much wider ‘information management’ space, «juxtaposed» subjects also need to be checked out.
Analysis insight about a Flyball dog competition team's performanceroli9797
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Towards a rebirth of data science (by Data Fellas)Andy Petrella
Nowadays, Data Science is buzzing all over the place.
But what is a, so-called, Data Scientist?
Some will argue that a Data Scientist is a person able to report and present insights in a data set. Others will say that a Data Scientist can handle a high throughput of values and expose them in services. Yet another definition includes the capacity to create meaningful visualizations on the data.
However, we enter an age where velocity is a key. Not only the velocity of your data is high, but the time to market is shortened. Hence, the time separating the moment you receive a set of data and the time you’ll be able to deliver added value is crucial.
In this talk, we’ll review the legacy Data Science methodologies, what it meant in terms of delivered work and results.
Afterwards, we’ll slightly move towards different concepts, techniques and tools that Data Scientists will have to learn and appropriate in order to accomplish their tasks in the age of Big Data.
The dissertation is closed by exposing the Data Fellas view on a solution to the challenges, specially thanks to the Spark Notebook and the Shar3 product we develop.
Data visualization is crucial to understanding the big data being generated by apps and services. Data visualization toolkits such as D3.js and charting toolkits are immensely popular but it remains difficult to create meaningful dashboards or usable analytics tools or clear data visualizations. This talk will discuss data visualization principles, present best practices, showcase excellent visualizations in practice, and share useful tips and mistakes learned.
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When planning a conference it’s essential to arrange the preparations so that your event doesn’t end up in a timing conflict with potentially competing events. Since our [Semantic Days] conference targets an audience who are engaged within the much wider ‘information management’ space, «juxtaposed» subjects also need to be checked out.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
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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)
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It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
1. Introduction to Information Visualization
Robert Putnam
putnam@bu.edu
Introduction to Information Visualization - Fall 2013
2. Outline
Introduction to Information Visualization - Fall 2013
Introduction / Definition
History
Examples
Workflow / Pipeline
Software overview
Hands-on exercises
Resources
3. “Sci vis” versus “Info vis”
Introduction to Information Visualization - Fall 2013
*Adapted from The ParaView
Tutorial, Moreland
• Visualization: converting raw data to a form that is
viewable and understandable to humans.
• Scientific visualization: specifically concerned
with data that has a well-defined representation in
2D or 3D space (e.g., from simulation mesh or
scanner).
4. Information visualization
Introduction to Information Visualization - Fall 2013
• Information visualization: concerned with data that
does not have a well-defined representation in 2D or
3D space (i.e., “abstract data”).
5. Pre-history
Introduction to Information Visualization - Fall 2013
Selected figures
– William Playfair (1821) – line, bar charts, etc.
– Charles Joseph Minard (1869) – Napoleon’s march, etc.
– Jacques Bertin (1967) – “semiology of graphics”
– John Tukey (1977) – “exploratory data analysis”
– Edward Tufte (1983) – statistical graphics standards/practices
1985 NSF Workshop on Scientific Visualization
1990: S.K.Card, et al. Readings in Information
Visualization: Using Vision to Think
11. Additional Examples
Introduction to Information Visualization - Fall 2013
NY Times words, words, numbers
Visual Complexity (from book by Manuel Lima)
50 examples (from June 2009, somewhat dated)
D3 Gallery
12. Visualization components
Introduction to Information Visualization - Fall 2013
Color
Size
Texture
Proximity
Annotation
Interactivity
– Selection / Filtering
– Zoom
– Animation
13. Info vis workflow / pipeline*
Introduction to Information Visualization - Fall 2013
Acquire
Parse
Filter
Mine
Represent
Refine
Interact
* Adapted from Fry, Visualizing Data
14. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Acquire
[p. 7, Fry, Visualizing Data]
15. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Parse
[p. 8, Fry, Visualizing Data]
16. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Filter/Mine
[p. 10, Fry, Visualizing Data]
17. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Represent
[p. 10, Fry, Visualizing Data]
18. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Refine
[p. 12, Fry, Visualizing Data]
19. Info vis workflow / pipeline
Introduction to Information Visualization - Fall 2013
Interact
Demo
[p. 12, Fry, Visualizing Data]
20. Visualization software
Introduction to Information Visualization - Fall 2013
Host language (C/C++/Java/Python) plus OpenGL
Stat/math package with graphics
– R
– MATLAB
Special-purpose info viz software
– Earth mapping, biological network visualization, etc.
Browser-enabled graphics/info viz packages
– Google Charts
– Processing / Processing.js
– D3
– Java + Flash (becoming rarer)
21. Hands-on
Introduction to Information Visualization - Fall 2013
HTML intro*
Google charts
D3
*Enabling software:
- JavaScript: “the language** of the web”
- JSON: JavaScript Object Notation
- SVG: Scalable Vector Graphics
- CSS: Cascading Style Sheets
**currently
22. Resources
Books
– Visual Complexity, Mapping Patterns of Information , Manuel Lima
– The Visual Display of Quantitative Information, Edward Tufte
– Information Visualization: Beyond the Horizon, Chaomei Chen
– JavaScript: The Definitive Guide, David Flanagan
– Getting Started with D3, Mike Dewar
– Visualizing Data, Ben Fry
– Interactive Data Visualization for the Web, Scott Murray
Websites
– http://processingjs.org/
– http://d3js.org/, https://github.com/mbostock/d3/wiki/API-Reference
– http://code.google.com/apis/ajax/playground/
– http://www.edwardtufte.com/tufte/
– http://www.visualcomplexity.com/
– http://www.webdesignerdepot.com/2009/06/50-great-examples-of-data-visualization/
Introduction to Information Visualization - Fall 2013
23. Resources
Web sites (cont.)
– http://fellinlovewithdata.com/
– http://infosthetics.com/
– http://visual.ly/
Conferences
– 17th International Conference: Information Visualisation, July 15-18 2013,
London
– IEEE VIS 2013, October 13-18, Atlanta
Groups
– d3-js (Google)
– Greater Boston useR Group (R Programming Language)
– Local meetups (see www.meetup.com)
Introduction to Information Visualization - Fall 2013
24. Questions?
Tutorial survey:
- http://scv.bu.edu/survey/tutorial_evaluation.html
Introduction to Information Visualization - Fall 2013
