How to use colours for data visualization. Guest lecture for the Data Visualization class at Ateneo de Manila University.
For more info see http://earthobservatory.nasa.gov/blogs/elegantfigures/
Below are the topics covered in this tutorial:
What is Data Visualization?
What is Tableau?
Why Tableau?
Tableau Job Trends
Companies using Tableau
Who should go for Tableau?
Tableau Architecture
Tableau Visualizations
Real time Use Case
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Data Visualization Trends - Next Steps for TableauArunima Gupta
Want answers to:
- What is data visualization?
- Why is it deemed disruptive in the field of analytics?
- What is Tableau?
Come view the slide deck!
Concludes with:
- Digital strategy recommendations for Tableau to become the winner in a winner-take-all-market
Below are the topics covered in this tutorial:
What is Data Visualization?
What is Tableau?
Why Tableau?
Tableau Job Trends
Companies using Tableau
Who should go for Tableau?
Tableau Architecture
Tableau Visualizations
Real time Use Case
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Data Visualization Trends - Next Steps for TableauArunima Gupta
Want answers to:
- What is data visualization?
- Why is it deemed disruptive in the field of analytics?
- What is Tableau?
Come view the slide deck!
Concludes with:
- Digital strategy recommendations for Tableau to become the winner in a winner-take-all-market
How to Improve Data Analysis Through Visualization in TableauEdureka!
Data Visualization using Tableau will allow one to gain an edge over the other analysts and let you present the data in a much better and insightful manner. It would be easier for the learners to immediately implement it in their workplace and create a real-time dashboard for their management using one of the most sought-after tools.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Storytelling: The only way to unlock true insight from your dataBright North
Data visualisation is failing. Many businesses are relying on tools like Excel and PowerPoint to deliver an engaging data story without first establishing the plot. Bright North's latest whitepaper explains why Data Storytelling is the only way to unlock true insight from your data and lists five steps you can take to give your data story a happy ending.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
Storytelling with Data - See | Show | Tell | EngageAmit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data.
I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage.
Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
Data is only useful when your audience can understand it. One of the best ways to decipher a jumble of figures and statistics is to turn it into a visual representation. Learn how to become a data visualization pro.
This slide deck is from a workshop that took place at the UNC Chapel Hill Davis Library Research Hub.
Collecting data is now easier than it has ever been. But, as data becomes more prolific, datasets become larger and more complex. How do we find meaningful patterns in our data? How can we communicate those patterns to others? Data visualization allows us to make sense of today’s ever evolving information landscape.
This workshop will introduce the history and basic principles of data visualization. Learn about best practices and resources for making an impact with your data through compelling charts, graphs and maps.
Crime analysis mapping, intrusion detection using data miningVenkat Projects
Crime analysis mapping, intrusion detection using data mining
Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
How to Improve Data Analysis Through Visualization in TableauEdureka!
Data Visualization using Tableau will allow one to gain an edge over the other analysts and let you present the data in a much better and insightful manner. It would be easier for the learners to immediately implement it in their workplace and create a real-time dashboard for their management using one of the most sought-after tools.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Storytelling: The only way to unlock true insight from your dataBright North
Data visualisation is failing. Many businesses are relying on tools like Excel and PowerPoint to deliver an engaging data story without first establishing the plot. Bright North's latest whitepaper explains why Data Storytelling is the only way to unlock true insight from your data and lists five steps you can take to give your data story a happy ending.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
Storytelling with Data - See | Show | Tell | EngageAmit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data.
I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage.
Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
Data is only useful when your audience can understand it. One of the best ways to decipher a jumble of figures and statistics is to turn it into a visual representation. Learn how to become a data visualization pro.
This slide deck is from a workshop that took place at the UNC Chapel Hill Davis Library Research Hub.
Collecting data is now easier than it has ever been. But, as data becomes more prolific, datasets become larger and more complex. How do we find meaningful patterns in our data? How can we communicate those patterns to others? Data visualization allows us to make sense of today’s ever evolving information landscape.
This workshop will introduce the history and basic principles of data visualization. Learn about best practices and resources for making an impact with your data through compelling charts, graphs and maps.
Crime analysis mapping, intrusion detection using data miningVenkat Projects
Crime analysis mapping, intrusion detection using data mining
Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
Data-Driven Color Palettes for Categorical Mapsnacis_slides
NACIS 2016 Presentation
Luc Guillemot, UC Berkeley
David O'Sullivan, UC Berkeley
How can colors be used to unravel spatiotemporal patterns in a multivariate geographical space? Perceptually consistent color spaces such as L*a*b* or L*c*h* are well defined, but their use in qualitative cartography is still relatively rare. Furthermore, qualitative color palettes are often randomly selected and do not relate the distance between colors to degrees of difference between categories depicted on the map. This study presents a tool allowing to select colors and automatically connect them to a multivariate space. It is applied to a geodemographic map of the San Francisco Bay Area where colors for 15 clusters can be algorithmically selected to reflect similarities between clusters in the attribute space or to maximize contrast between spatially contiguous clusters. This study shows that careful consideration of a color palette and its relation to the mapped data space can assist in the visualization of complex spatiotemporal patterns.
Our eyes have different responses to the different coloured patterns. Therefore, it is crucial to get a different colour map for kinds of seismic interpretation tasks as well as other fields of science such as astronomical interpretations.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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).
6. COLOR PERCEPTION
A constant increase in brightness is not perceived as linear,
and this response is different for red, green, and blue. Look
for tools and color palettes that describe colors in a
perceptual color space, like CIE L*C*h or Munsell.
7. A color scale should vary consistently across the entire range of values, so
that each step is equivalent, regardless of its position on the scale.
A palette should also minimize errors from the color shifts introduced by
nearby areas of differing color or lightness, a phenomenon known as
simultaneous contrast.
8. Combine a linear, proportional change in lightness with a simultaneous
change in hue and saturation to achieve the perfect palette for
sequential data.
SEQUENTIAL DATA
9. For divergent data, use palettes that are composed of two sequential
palettes merged with a neutral color.
This type of palette works because it takes advantage of pre attentive
processing: our visual systems can discriminate the different colors quickly
and without conscious thought.
DIVERGENT DATA
10. A sequential palette that varies uniformly in lightness will still be readable by
someone with color deficient vision (or a black and white print), regardless
of the hue. But a divergent palette with matched lightness can be difficult or
impossible to parse if the viewer can’t distinguish the hues.
COLOR DEFICIENCY CAUTION
11. Instead of representing proportional relationships, color is used to separate
areas into distinct categories. The palette should consist of colors as distinct
from one another as possible. Due to the limits of perception, especially
simultaneous contrast, the maximum number of categories that can be
displayed is about 12
CATEGORICAL/QUALITATIVE DATA
12. If you need to display double-digit categories, it’s best to group similar
classes together.
>12 CATEGORIES
A grouped color scheme allows the
USGS to simultaneously show 16 different
land cover classes in a single map of the
area surrounding Portland, Oregon.
21. Color spots against a light gray or muted field
highlight and italicize data.
A magnetogram is a map of magnetic
fields, in this case on the surface of the
Sun. A divergent palette suits this data
because the north polarity (red) and
south polarity (blue) are both
measurements of the same quantity
(magnetism), just with opposite signs.
SDO HMI image adapted from the Solar
Data Analysis Center.
22. Use colors found in nature, especially those on the
lighter side.
The subtle colors in this bathymetric map
of Crater Lake are a direct descendent of
the palettes created by Eduard Imhof.
Map courtesy National Park Service
Harper’s Ferry Center.
23. The unnatural colors of the rainbow palette (left) are often difficult for novice viewers to interpret. A
more naturalistic palette for phytoplankton(more or less a type of ocean vegetation) trends from dark
blue for barren ocean, through turquoise, green, and yellow for increasing concentrations of the tiny
plants and algae.
24. Pure, bright or very strong colors have loud, unbearable effects when
they stand unrelieved over large areas adjacent to each other, but
extraordinary effects can be achieved when they are used sparingly on
or between dull background tones.
Screenshot from the BBC’s traffic
accident visualization
http://www.bbc.co.uk/news/
uk-15975724
25. Large area background or base-colors should
do their work most quietly, allowing the smaller,
bright areas to stand out most vividly, if the
former are muted, grayish or neutral.
DO NOT DO THIS