Here are some key points to consider when designing visuals:
- Who is your audience? What information do they need?
- What insights or messages do you want to convey?
- Consider different visualisation types and choose those best suited to your data and goals
- Use visual hierarchy, layout and formatting to guide the eye and message
- Iteratively sketch, test and refine your designs with your intended users
- Balance simplicity and clarity with including all necessary information
The design process is iterative. Start broadly and refine based on testing with intended users. Focus on conveying the most important insights as simply as possible.
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Introduction to Data Science: presented by Dr. Sotarat Thammaboosadee, ITM Mahidol and Datalent Team. This presentation is a part of Data Science Clinic no.9 organized by Data Science Thailand, 8 March 2017 at All Season Place, Bangkok, Thailand.
Data Science training in Delhi by ShapeMySkills Pvt.Ltd has proven to be the best by its many enrolled candidates. We provide you the best faculty with industry experience and learning access 24/7, study material, mock tests, and most importantly industry based projects.
For more details visit us : https://shapemyskills.in/courses/data-science/ »
or Contact us : 9873922226
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
Creating a Data Science Ecosystem for Scientific, Societal and Educational Im...Ilkay Altintas, Ph.D.
The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Electronic communication and knowledge systemsJarmo Saarikko
Using the internet for communicating research information.
Author: Jarmo Saarikko
Date: 11-Nov-2000
Event: From Research to Application - the second Nordic Forum, 11-12.5.2000, Espoo, Finland
Introduction to Data Science: presented by Dr. Sotarat Thammaboosadee, ITM Mahidol and Datalent Team. This presentation is a part of Data Science Clinic no.9 organized by Data Science Thailand, 8 March 2017 at All Season Place, Bangkok, Thailand.
Data Science training in Delhi by ShapeMySkills Pvt.Ltd has proven to be the best by its many enrolled candidates. We provide you the best faculty with industry experience and learning access 24/7, study material, mock tests, and most importantly industry based projects.
For more details visit us : https://shapemyskills.in/courses/data-science/ »
or Contact us : 9873922226
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
Creating a Data Science Ecosystem for Scientific, Societal and Educational Im...Ilkay Altintas, Ph.D.
The new era of data science is here. Our lives and society are continuously transformed by our ability to collect data in a systematic fashion and turn that into value. The opportunities created by this change also comes with challenges that push for new and innovative data management and analytical methods as well as translating these new methods to applications in many areas that impact science, society, and education. Collaboration and ability of multi-disciplinary teams to work together and communicate to bring together the best of their knowledge in business, data and computing is vital for impactful solutions. This talk will discusses a reference ecosystem and question-driven methodology, called PPODS, to make impactful data science applications in many fields with specific examples in hazards, smart cities and biomedical research.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Electronic communication and knowledge systemsJarmo Saarikko
Using the internet for communicating research information.
Author: Jarmo Saarikko
Date: 11-Nov-2000
Event: From Research to Application - the second Nordic Forum, 11-12.5.2000, Espoo, Finland
Writing an evaluation report is only a small piece of communicating the results to stakeholders. What you really want is that they engage with the data and follow through on the recommendations.
discuss about System system analysis, system design, system analyst's role, Development of System through analysis, SDLC, Case Tools of SAD, Implementation, etc.
The slides to my talk at the Berlin Design Systems Coalition meetup early 2018.
Two years ago ResearchGate established a design system to gain a consistent user experience across their products. But with its introduction it became much more complicated to handoff designs to engineering including all design system relevant information. For existing design handoff tools the design systems abstractions and underlying principles are unknown and can only be interpreted to a certain extend. This keynote will give you insights on ResearchGate’s design system architecture and their solution to create design handoffs that empower a frictionless design to engineering workflow.
This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
Graphs made easy with SAS ODS Graphics Designer (PAPER)Kevin Lee
Graphs can provide the visual patterns and clarities that are not apparent in tables and listings, but sometimes it takes too long to create ones. Now, The ODS Graphics Designer makes it much easier. The paper is intended for Clinical Trial SAS® programmers who are interested in creating graphs using ODS Graphics Designer. The ODS Graphics Designer is a SAS/GRAPH GUI based interactive tool. The codes in ODS Graphics Designer are based on the Graph Template Language (GTL), but SAS programmers can create graphs using its point-and-click interaction without any programming. The ODS Graphics Designer allows SAS programmers to create many kinds of graphs such as scatter plots, series plots, step plot, histogram, box and more. The paper will show how to start the ODS Graphics Designer in SAS. The paper will also show how easy to create simple or complex graphs using the designer and how to enhance graphs using other features such as legends, cell properties, plot properties and so on. The paper will demonstrate how to create GTL and template codes from designer that will also create the exact graphs in SAS programming. The setting is set up in CDISC environment, so ADaM datasets will be used as source data.
Data Visualization is widely used in industries in info-graphics design, business analytics, data analytics, advanced analytics, business intelligence dashboards, content marketing. It is the 1st part of 3 part series on data visualization. These techniques will enable you to create a good design UI/UX. It contains r codes useful for programmers to create good visual charts and depict a story to clients, customer, senior management, etc ...
When we are presented with a task our brain's natural response is to immediately begin conceptualizing a number of different approaches towards completing the task. Our mind doesn't develop a plan all at once—instead it adds new information and new tasks to the picture as the mind digests the problem over time. A mind map intuitively illustrates this process before your eyes.
Practical Influence Operations, presentation at Sofwerx Dec 2018bodaceacat
Presentation on practical responses to misinformation as part of hybrid warfare, including the use of infosec frameworks to frame attacks and responses.
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.”
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.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
2. Lab 4: your 5-7 things
Communicating with data
Really common visualisations
Quite common visualisations
Visualisation tools: Matplotlib and Tableau
Doing more: D3 and beyond
4. Engage your audience
Who are these people?
Demographics: which languages? What red flags?
What communications styles are they used to?
What channels are you using? Website, printed media, SMS?
What have you got for them?
Insights? answers? surprises?
Exploring or explaining?
Do you want to engage, persuade, inform or entertain?
5. Design rules
Storytelling:
Design for your medium (e.g paper)
Learn from the storytellers: have a beginning, middle and end
Use drill-down: summarise with visuals, but allow users to reach the data
Frame your message - why are you doing this, how did you get here
Visualisations:
Have graphical integrity (e.g. start bars at zero)
16. Choosing a Visualisation Tool
What do you want to do?
Standard visualisations, or something special?
Inputs: files (e.g. CSV) or streaming data? Maps?
Non-roman languages (Arabic, Mandarin etc)?
Interactive or static?
Where do you want to do it?
Online or offline?
Any other restrictions?
17. Excel
Limited set of visualisation types
Not interactive
Offline
Static
Not free
Relatively easy
Widely used
18. Matplotlib
Python visualisation library
Not interactive
Not the prettiest (but does have ways to
make it prettier, e.g. Seaborn)
Good for quick-and-dirty views of data
Offline + Online
Free
23. Exercise: draw a line chart in Matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
25. Exercise: draw a scatterplot
import matplotlib.pyplot as plt
import numpy as np
x = np.array([1,4,3,2,6,4,7,8])
y = np.array([3,5,4,3,7,6,4,9])
plt.scatter(x, y)
26. Add labels
fig, ax = plt.subplots()
plt.scatter(x, y)
ax.set_ylabel('This is the Y axis')
ax.set_xlabel('This is the X axis')
ax.set_title('This is the Title')
28. Do this: Import data into Tableau
Get a copy of example file “cleaned_popstats.csv”
Open Tableau Public (click on the executable)
Click on “Text File”
Select “cleaned_popstats.csv”, then “open”
Congratulations - you’ve got data into Tableau
Now click on “sheet 1” at the bottom of the page
29. Do this: add rows
From sheet1:
● You should see a “show me” box on the top RHS: click on it, then the thing
that looks like a column chart (sigh: Tableau calls it “stacked bar”)
● Drag “asylum seekers” from under “Measures” to the “rows” box
30. Do this: add columns
● Drag “origin/ returned from” from under
“dimensions” to the “columns” box
● Right-click on “origin/returned from” in the
“columns” box.
● Click “sort…”
● Click “descending”
● Click “field”
● Click “okay”
31. Do this: remove a column from the graph
Right-click on “various” in the graph.
Click “Exclude”
Watch the graph scale to the new biggest value
(Democratic Republic of the Congo)
32. Do this: add colours
● Drag “Year” to color (under “Marks”)
The green is okay, but not easy to read:
● Click the little triangle that appears when
you hover over the new “Year” colours
box
● Click “Edit colors”
● Click “stepped color”
● Click the bar under “Palette”; click “Red
Blue Diverging” then “okay”
35. Drawing a Chord Diagram in D3: Online
Use the terminal window
cd to the directory containing file 4.3_d3_chord_online.html
In the terminal window, type:
python -m http.server 8899 &
Then go to http://0.0.0.0:8899/4.3_d3_chord_online.html in your browser
And hover your mouse over the circle edges...
(to exit, type control-c)
36. Drawing a Chord Diagram in D3: Offline
Copy your d3.js.zip file and the 4.4_d3_chord_offline.html file into a directory
Unzip d3.js.zip
In the directory your code is in, type:
python -m http.server 8899 &
Then go to http://0.0.0.0:8899/4.4_d3_chord_offline.html in your browser
And hover your mouse over the circle edges...
(to exit, type control-c)
41. Exercise: Design your visuals
Use pen and paper (or post-its) to design visualisations and dashboards for your
project
Editor's Notes
Red flags: things you really need to be careful of. For example: using orange and green for an Irish audience.
See http://extremepresentation.typepad.com/blog/files/choosing_a_good_chart.pdf for advice on choosing the right chart type..
Sometimes it’s appropriate to throw data and graphs at the user, and let them work it out on their own, but usually drill-down and a story is the answer.
See Alberto Cairo’s work for more details.
All these examples are in Tableau Gallery here: https://public.tableau.com/profile/sara.terp#!/vizhome/DS_session_example/exampledashboard and in Tableau Desktop here: https://github.com/bodacea/inafu6513/blob/master/Notebooks/4.2 Tableau_example.twb
You can put visualisations and text together in a dashboard. You can also link these visualisations, so that when you click on a point in one visualisation, data relevant to that point is shown in another.
This is india’s government employee attendance dashboard. If you look at the bottom of Tableau, there’s a dashboard button.
Other visualisation types you’ll see a lot include Chord diagram, Sankey diagram, Choropleth, symbol map
To create this chart: copied the asylum seeker data into an excel spreadsheet. Created a pivot table from the data. Created a chart from the pivot table. Did lots of fussing around to get chart elements in the right place/ filtered. Still more fussing needed to get e.g. the legend right.
The image is one of the Piktochart templates. Infographics aren’t immediate outputs from python code, but can be very powerful.
NB: You only need the %matplotlib inline in an ipython notebook.
plt.subplots creates a plot on your drawing ‘canvas’. It’s what we usually use to create a group of plots. Here, we’re using it to get at the axes (ax) for this visualisation.
Data file is here is you get stuck: https://github.com/bodacea/inafu6513/blob/master/example_data/cleaned_popstats.csv
Don’t panic when “asylum seekers” becomes “SUM(Asylum seekers)” in the Rows box. This is normal.
At this point, you’re probably wondering why the columns are so small
Okay, “Various” is huge, and not helping the graph much. Let’s remove it.
The green is okay, but not easy to read.
Also available at https://public.tableau.com/profile/publish/DS_session_example/Sheet1#!/publish-confirm
This starts a webserver on your PC, that you can then go to and look at your D3 file in.
NB I changed the channel to 8899 because the standard (8888) clashes with the ipython notebook server.
Chord example is from http://bl.ocks.org/mbostock/4062006
CORRECTION: the space in the filename was making your code crash. Try again!
The difference between the two files is in where the D3 code comes from.
In the online version, the D3 code is at //d3js.org/d3.v3.min.js (the d3.v3.min.js file is a version of d3.js with all the whitespace and comments taken out to make it smaller);
In the offline version, the D3 code is on your pc, in file d3.js the d3 directory.
Bugz: you might get an error message saying “Address already in use”. This is bad and naughty, but to quickly fix it, try:
in the terminal window, type “ps -a”
Note the number at the left of the line that looks like “7064 ttys000 0:00.16 python -m http.server 8899”
type “kill -9 7064” (or whatever your number was).
Note: this is a very powerful command: make sure you’re using the right number, or you might accidentally shut down your machine…