data visualization assignments

Teaching Data Visualization

As statistics educators, it is often easier to focus our teaching on methods instead of communication . And while many of us understand the value of good communication, actually teaching it is difficult and outside of our comfort zone. There has been quite a bit of work done on the science of visualization (e.g., the Grammar of Graphics by Wilkinson ). There is general consensus that teaching students to communicate using visualizations is of paramount importance (see recent blog entries: National Academies Report on Data Science and GAISE ). However, the connection between the work done on visualization methods and the teaching of data science and statistics is weaker.

When trying to incorporate data visualization into the classroom, we find ourselves asking the following questions:

• What does data visualization in a statistics or data science class even mean?
• How do you teach data visualization?
• What are successful and unsuccessful approaches to teaching visualization in the classroom?
• What types of assignments can you give for visualization assignments?
• How can visualization assignments be assessed effectively? efficiently?

Examples of Teaching Visualization

For inspiration, we look to our colleagues who are already implementing either full courses on data visualization or substantial sections of data visualization into their statistics or data science courses.

• topics include: plotly , color theory, graphical perception, design principles, hand-drawn visualizations, spatial data, visualizing uncertainty, and more.
• assignments include: reading responses, visualization in the wild, and a final project.
• readings include: key standards like Visualize This by Nathan Yau and The Visual Display of Quantitative Information by Edward Tufte. Other great resources are Wickham and Stryjewski 40 Years of Boxplots and Dear Data by Lupi and Posavec.

Nolan and Perrett’s 2016 TAS paper “Teaching and learning data visualization: ideas and assignments” provides guidance on bolstering the role of graphics in the undergraduate statistics curriculum.

Silas Bergen’s course on Data summarization and visualization (among many other aspects of the course) assigns three design tasks, representing a way of scaffolding assignments to build student competencies throughout the semester.

• Design Task #1: a single, static dashboard visualizing antibiotic data
• Design Task #2: a visualization meant to answer a series of research questions based on census data
• Design Task #3: a visualization which answers more complex research question and includes interactivity

The blog citizen-statistician provides a handful of posts which are relevant to teaching data visualization including:

• Data Visualization Course for First-Year Students
• Hand Drawn Data Visualizations
• An assignment Turning Tables into Graphs

In his STATS337 (“Readings in Applied Data Science”) course at Stanford, Hadley Wickham has his students create annotated bibliographies describing different aspects of data science. Kenneth Tay’s Annotated Bibliography on “Communication and Visualization in Data Science” provides context and resources for an instructor looking for ideas in teaching visualization.

The rich history of graphics and visualization is an additional venue to pursue with your students. Understanding how visualizations have played a role in scientific discoveries as well as understanding the evolution of different methods helps students

• Visual and Statistical Thinking by Tufte discusses two compelling examples where in one the graphic saved the day, and in the second, the graphic was terrible and led to disaster.
• The Golden Age of Statistical Graphics traces the origins of statistical graphics and how the last 150 years of graphics paved the way for the explosion in visualization theory and techniques seen recently. The Milestones Project by Friendly and Denis is a fun and interactive way for students to experience the evolution of graphical techniques.
• NYT What’s Going On in This Graph
• List of Data Viz books: https://policyviz.com/better-presentations/data-viz-resources/data-viz-books/
• Even more references to visualization courses: http://civilstat.com/2015/11/teaching-data-visualization-approaches-and-syllabi/
• Online data viz text: Fundamentals of Data Visualization
• Nathan Yau, One Dataset, Visualized 25 Ways
• Mine Çetinkaya-Rundel’s 2018 Pickard Lecture describing how powerful teaching visualization in the classroom can be: Let Them Eat Cake (First)! , slides
• Nolan and Perrett Teaching and Learning Data Visualization: Ideas and Assignments , TAS 2016.
• McNamara Key Attributes of a Modern Statistical Computing Tool , TAS , 2018.
• Modern Data Science with R by Baumer, Kaplan, and Horton devotes chapters to: data visualization, the grammar of graphics, interactive data graphics, and working with spatial data. (As a textbook, MDSR provides many great examples and end of chapter exercises.)

About this blog

Each day during the summer of 2019 we intend to add a new entry to this blog on a given topic of interest to educators teaching data science and statistics courses. Each entry is intended to provide a short overview of why it is interesting and how it can be applied to teaching. We anticipate that these introductory pieces can be digested daily in 20 or 30 minute chunks that will leave you in a position to decide whether to explore more or integrate the material into your own classes. By following along for the summer, we hope that you will develop a clearer sense for the fast moving landscape of data science. Sign up for emails at https://groups.google.com/forum/#!forum/teach-data-science (you must be logged into Google to sign up).

We always welcome comments on entries and suggestions for new ones.

• Closing 2020: A summer of ethics in data science education
• Data Sources
• Integrating ethics training into any quantitative course
• A preview of the JSM
• Social Justice & Data Science
• Engaging data science students with COVID-19 data
• Philosophical Ethics for Data Science
• Hippocratic Oath
• Data Feminism
• Bookclub on Data Science Ethics
• communication
• visualization
• collaboration
• data-wrangling

6.894 : Interactive Data Visualization

Starting Spring 2021, this class has been re-numbered as 6.859 .

Check out the Final Project Showcase »

The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Moreover, visual representations may help engage more diverse audiences in the process of analytic thinking.

By the end of this course, you should expect to be able to:

• Design, evaluate, and critique visualization designs.
• Wrangle and explore datasets through visualization using Trifacta Wrangler and Tableau .
• Understand visualization techniques and theory.
• Implement interactive data visualizations using Vega-Lite , and D3.js .
• Develop a substantial visualization project.

Schedule & Readings

Week [$index + 1] : [theme].

• Assigned Due [name]
• Required Optional . . . .

[semester] · [schedule] ([location])

Teaching Staff

[email protected]

A1: Visualization Design 5%

A2: Exploratory Data Analysis 10%

A3: White/Black Hat Visualization 15%

A4: Interactive Visualization 20%

Final Project 40%

Class Participation 5%

Readings/Exercises 5%

Individual assignments. The first three assignments are solo assignments, and should be completed without collaboration. You are encouraged to ask the instructor and/or TAs for advice during office hours, and to use Piazza to obtain answers to questions from other students.

Team projects. Team projects, of course, encourage collaboration. You are encouraged to work together on all parts of the project, and must ensure that every team member is involved in all aspects of the project (design, coding, and documentation). Although the team will receive a single grade, each team member will be asked to identify their own work product to ensure equitable divison of labor. Participation in team check-in meetings and project presentations will be evaluated on an individual basis.

Reuse of third-party material. Unless otherwise stated in an assignment, you are free to use any third-party code, whether as libraries or code fragments, and to adopt any idea you find online or in a book as long as it is publicly available and appropriately cited (see the section on code in the MIT Handbook on Academic Integrity for details).

Lateness. You have 4 slack days, which you can use as you wish for assignments 1–4. These days are to be used for minor illnesses, special occasions (such as religious holidays, interviews and sports meet events), and unexpected problems. Additional extensions will be granted only for serious medical issues with a written note from S^3 (for undergraduate students), the EECS graduate student office (38-444), or the MIT Office of Graduate Education. Late submissions not covered by a slack day will incur a penalty of 10% of the total available grade for each day of lateness. Note also that while we will endeavor to return graded work to you as soon as possible, if you use slack days you may miss a grading cycle and receive feedback in the following week.

Resubmitting Assignments. You may resubmit any assignment with a short (1 paragraph) summary of changes to potentially earn back 50% of the points lost in the original submission. Resubmissions must occur within 7 days of the original grades being released, and must use the same process as the initial submission. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

Class Participation

This course is mixes traditional lectures with more hands-on design exercises, interactive activities, and project presentations. Your class participation grade assesses your engagement across this spectrum of activites, and also considers your participation in posing and answering questions on the Piazza forum.

Reading Commentaries

Most lectures have one required and several optional readings associated with it. Lectures will assume that you have read, and are ready to discuss, the day's required reading. To facilitate the conversation, you are expected to submit a 1–2 paragraph commentary about each required reading on its nb page by noon on the day of the lecture. You may mark your commentary as "Anonymous to students" if you prefer. We will drop your two lowest commentary scores for the semester (e.g., you may choose to skip two readings without penalty).

Commentaries should not merely summarize the reading, but rather should exhibit one or more of the following:

• Critiques of arguments made in the paper.
• Analysis of implications or future directions for work discussed in lecture or readings.
• Clarification of some point or detail.
• Pose insightful questions, or answer other people's questions.

Acknowledgements

Material for this class has been adapted from classes taught by Jeffrey Heer at the University of Washington, Maneesh Agrawala at Stanford University, Hanspeter Pfister at Harvard University, Tamara Munzner at the University of British Columbia, Jessica Hullman and Nick Diakopoulos at Northwestern University, Niklas Elmqvist at the University of Maryland, College Park, Enrico Bertini at New York University, and Sheelagh Carpendale at Simon Fraser University. Thanks also to Michael Correll at Tableau Research. The class draws heavily on materials and examples found online, and we try our best to give credit by linking to the original source. Please contact us if you find materials where credit is missing or that you would rather have removed.

Data Visualization

Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data

PMAP 8921 • May 2020 Andrew Young School of Policy Studies Georgia State University

• Dr. Andrew Heiss
• 357 Andrew Young School
• [email protected]
• @andrewheiss
• Schedule an appointment

Course details

• May 11–June 1, 2020

Contacting me

E-mail and Slack are the best ways to get in contact with me. I will try to respond to all course-related e-mails and Slack messages within 24 hours ( really ), but also remember that life can be busy and chaotic for everyone (including me!), so if I don’t respond right away, don’t worry!

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Stanford Online

Introduction to data visualization with python.

CSP-XTECH26

Stanford Continuing Studies

Data visualization is crucial for communicating insights from data effectively, a skill that’s essential for data analysts, data scientists, and many other business intelligence roles. This course starts by covering visualization fundamentals using Python libraries, including Pandas, Matplotlib, and Seaborn. Students will learn how to import, clean, and prepare data, then transform that data into scatter plots, histograms, and heatmaps to tell compelling business narratives. In the process, students will also learn to discern the difference between good and bad data visualization based on objectively critiquing the quality and clarity of information conveyed. The second half of the course examines how generative AI advancements shape the future of visualization, challenging students to evaluate the effectiveness of new technologies against traditional visualization techniques coded by hand. Whether you need to differentiate insightful visualizations from misleading ones or want to improve your Python skills through hands-on practice, this comprehensive course will equip you with the tools to extract maximum value from data.

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Assignment overview

You will get the most of out this class if you:

• Engage with the readings and lecture materials
• Regularly use R

Each type of assignment in this class helps with one of these strategies.

Reflections

To encourage engagement with the course content, you’ll need to write a ≈150 word reflection about the readings and lectures for the day. That’s fairly short: there are ≈250 words on a typical double-spaced page in Microsoft Word (500 when single-spaced).

You can do a lot of different things with this memo: discuss something you learned from the course content, write about the best or worst data visualization you saw recently, connect the course content to your own work, etc. These reflections let you explore and answer some of the key questions of this course, including:

• What is truth? How is truth related to visualization?
• Why do we visualize data?
• What makes a great visualization? What makes a bad visualization?
• How do you choose which kind of visualization to use?
• What is the role of stories in presenting analysis?

The course content for each day will also include a set of questions specific to that topic. You do not have to answer all (or any) of these questions . That would be impossible. They exist to guide your thinking, that’s all.

I will grade these memos using a check system:

• ✔+: ( 11.5 points (115%) in gradebook ) Reflection shows phenomenal thought and engagement with the course content. I will not assign these often.
• ✔: ( 10 points (100%) in gradebook ) Reflection is thoughtful, well-written, and shows engagement with the course content. This is the expected level of performance.
• ✔−: ( 5 points (50%) in gradebook ) Reflection is hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

Notice that is essentially a pass/fail or completion-based system. I’m not grading your writing ability, I’m not counting the exact number of words you’re writing, and I’m not looking for encyclopedic citations of every single reading to prove that you did indeed read everything. I’m looking for thoughtful engagement, that’s all. Do good work and you’ll get a ✓.

You will turn these reflections in via iCollege. You will write them using R Markdown and they will be the first section of your daily exercises (see below).

Each class session has interactive lessons and fully annotated examples of code that teach and demonstrate how to do specific tasks in R. However, without practicing these principles and making graphics on your own, you won’t remember what you learn!

To practice working with ggplot2 and making data-based graphics, you will complete a brief set of exercises for each class session. These exercises will have 1–3 short tasks that are directly related to the topic for the day. You need to show that you made a good faith effort to work each question. The problem sets will also be graded using a check system:

• ✔+: ( 11.5 points (115%) in gradebook ) Exercises are 100% completed. Every task was attempted and answered, and most answers are correct. Knitted document is clean and easy to follow. Work is exceptional. I will not assign these often.
• ✔: ( 10 points (100%) in gradebook ) Exercises are 70–99% complete and most answers are correct. This is the expected level of performance.
• ✔−: ( 5 points (50%) in gradebook ) Exercises are less than 70% complete and/or most answers are incorrect. This indicates that you need to improve next time. I will hopefully not assign these often.

Note that this is also essentially a pass/fail system. I’m not grading your coding ability, I’m not checking each line of code to make sure it produces some exact final figure, and I’m not looking for perfect. Also note that a ✓ does not require 100% completion—you will sometimes get stuck with weird errors that you can’t solve, or the demands of pandemic living might occasionally become overwhelming. I’m looking for good faith effort, that’s all. Try hard, do good work, and you’ll get a ✓.

You may (and should!) work together on the exercises, but you must turn in your own answers.

You will turn these exercises in using iCollege. You will include your reflection in the first part of the document—the rest will be your exercise tasks.

Mini projects

To give you practice with the data and design principles you’ll learn in this class, you will complete two mini projects. I will provide you with real-world data and pose one or more questions—you will make a pretty picture to answer those questions.

The mini projects will be graded using a check system:

• ✔+: ( 85 points (≈115%) in gradebook ) Project is phenomenally well-designed and uses advanced R techniques. The project uncovers an important story that is not readily apparent from just looking at the raw data. I will not assign these often.
• ✔: ( 75 points (100%) in gradebook ) Project is fine, follows most design principles, answers a question from the data, and uses R correctly. This is the expected level of performance.
• ✔−: ( 37.5 points (50%) in gradebook ) Project is missing large components, is poorly designed, does not answer a relevant question, and/or uses R incorrectly. This indicates that you need to improve next time. I will hopefully not assign these often.

Because these mini projects give you practice for the final project, I will provide you with substantial feedback on your design and code.

Final project

At the end of the course, you will demonstrate your data visualization skills by completing a final project.

Complete details for the final project (including past examples of excellent projects) are here.

There is no final exam. This project is your final exam.

The project will not be graded using a check system. Instead I will use a rubric to grade four elements of your project:

• Technical skills
• Visual design
• Truth and beauty

If you’ve engaged with the course content and completed the exercises and mini projects throughout the course, you should do just fine with the final project.

Last updated on July 31, 2021

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Assignments

Weekly reflection memo.

You’ll need to submit a one-page (500 words) reflection memo before midnight each Monday (the day before class). You can do a lot of different things with this memo: discuss something you learned from the readings, write about the best or worst data visualization you saw that week, connect the readings or projects from that week to your own work, etc. These memos essentially let you explore and answer some of the key questions of this course, including:

• What is truth? How is truth related to visualization?
• Why do we visualize data?
• What makes a great visualization? What makes a bad visualization?
• How do you choose which kind of visualization to use?
• What is the role of stories in presenting analysis?
• How can we communicate uncertainty?
• How can you lie with statistics? How do you tell the truth with statistics?
• How do you make sure people don’t think you’re lying with statistics?

These memos are also to help me see what you glean from each week’s reading so I can prepare class discussions to be most useful and interesting to you. These memos will only be graded for completion. Instructions for submitting the memo will be in the assignment page for that week.

Homework assignments

Every week, you will have a short homework assignment to give you practice using Excel and R. Instructions for each assignment will be given on the assignment page for that week. As with the reflection memo, homework is due before midnight each Monday .

In order to give you practice with the data and design principles you’ll learn in this class, I will give you three existing data visualizations to redesign.

Visualization rubric

Evaluating data graphics is hard, especially since so much of the work that goes into creating excellent visualizations is subjective. How do you know if a figure follows graphic and data design principles and communicates truth?

Over the course of the term, you will develop your own rubric for evaluating the design, aesthetics, correctness, and truth of data visualizations, based on the readings, assignments, and classroom activities you’ll do. These rubrics can take any form you want, as long as they include some approach for scoring performance and some method for quickly identifying specific areas of improvement. Where possible, try to cite the materials you draw from to create the rubric items.

Draft rubric

Draft rubrics will be due in the middle of the term (conveniently indicated in the course schedule). You’ll get feedback about these drafts from your peers and me, but they will only be graded for completion. I will grade your final rubric according to a rubric I will provide you.

Rubric test run

Before you turn in your final rubric, you’ll need to make sure it actually works. Use your improved draft rubric to evaluate any visualization. You’ll only be graded for completion.

Final project

To evaluate how well you’ve learned the course materials, you will create a visualization based on data I will provide you. I will grade these final graphics according to both your rubric and my own rubric, with equal weight (i.e. your rubric score consists of 50% of the total grade).

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This is a Repository made for Coursera Assignments, and Tutorials which includes many interesting plots such as waffle charts, folium charts, chloropeth charts etc.

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Teaching with Digital Assignments

• Types of Digital Assignments
• Classroom Considerations
• Digital Pedagogy
• Assignments by Format
• Tools and Technologies
• Digital Storytelling This link opens in a new window
• Open Educational Resources (OERs) This link opens in a new window
• Writing for Digital Assignments
• Digital Projects Toolkit This link opens in a new window
• Digital Assignment Consultation

Digital Pedagogy Librarian

Digital Assignment Formats

This page provides information about common types of digital assignments, with resources that describe common techniques and applications of technology in teaching and learning. 

There are many formats and activities available for digital assignments, many of which are transferrable among disciplines, frameworks, and assignment types, and which can incorporate a range of media. Common formats include:

Audio and Video

Web publications, data visualization, graphics, and visual projects.

• Maps and Timelines
• Annotations

Text Analysis

• Exhibits and Collections

Assignments of many formats incorporate methods of  digital storytelling  in the presentation of research and knowledge.

• Digital Assignment Guides A resource from Princeton University's McGraw Center for Teaching and Learning providing overviews and examples of common digital assignment formats.
• Assignments & Teaching Materials Links to digital assignments and related material available for reuse and repurposing in undergraduate classrooms.
• Digital Storytelling with Audio and Video An overview of audio and video projects and assignments.
• Podcasting Assignments An article from Princeton University's McGraw Center for Teaching & Learning; defines types of podcasting assignments, lists learning goals and considerations, suggests tools, and includes links to examples, rubrics, and related resources.
• Teaching Podcasting A curriculum guide from NPR, provides insight into the workload and process of podcasting assignments.
• Video Assignments An article from Princeton University's McGraw Center for Teaching & Learning; describes types of video assignments, lists learning goals and considerations, suggests tools, and includes links to examples, rubrics, and related resources.
• The Asynchronous Cookbook: Writing, Storytelling, and Publishing A book chapter providing an overview of digital writing assignments, including journals, blogs, and collaborative textbooks.
• Creating Web Assignments Overview and guide to new web assignments.
• StoryMaps An overview of StoryMaps, including examples of work completed by UD students in capstone projects.
• Teaching with Blogs Overview of blogs and considerations for blogging assignments.
• Wikis Overview of wikis and wiki-based assignments.
• Data Visualization An overview of data visualization for teaching and learning.
• Creating an Infographic Assignment Overview and guide to implementing infographic assignments.

Maps and Timelines

• Digital Timelines Overview of digital timelines and types of timeline assignments.
• Creating Digital Stories with TimelineJS A detailed description of digital timeline assignment design, implementation, and assessment.
• The Asynchronous Cookbook: Mapping Activities A chapter providing an overview and steps to develop geographical and concept mapping assignments.
• Teaching Source Annotation in Digital Spaces Suggestions for teaching annotation of digital text, images, and videos.
• Social Annotation Activities Suggested uses of social annotation for learning in STEM, business, social sciences, humanities, law, and more. This article is from a product website, but provides useful insights and information
• Collaborative Annotation Overview of collaborative annotation activities and tools.
• Video Annotation Suggested uses of video annotation for teaching and learning. This article is from a product website, but provides useful insights and information.
• Text Analysis An overview of Voyant for text analysis from SDSU.
• Text Analysis Assignments An overview of key considerations and relevant tools for text analysis assignments.
• Text Mining Methods and Tools A guide from University of Delaware detailing the process of a text mining project.

Digital Exhibits and Collections

• Teaching with Online Exhibits An article from Princeton University's McGraw Center for Teaching & Learning addressing the use of digital exhibits as capstone assignments and as curricular models.
• ePortfolios: Theory and Practice A detailed overview of ePortfolios as digital collections and reflective assignments.
• Teaching with Omeka Overview of digital archive assignments built with Omeka, including considerations and learning outcomes.
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Top 15 Data Visualization Frameworks

Data Visualization Frameworks are known as tools and libraries that can assist analysts, data scientists, and decision-makers in transforming raw data into meaningful visuals. Such frameworks provide all sorts of things, starting with a basic chart and graphical representation of data and going up to full interactive dashboards. In this article, we will be discussing the Top 15 Data Visualization Frameworks and their characteristics and use cases. 

Table of Content

1. D3.js 

2. chart.js , 3. tableau , 4. matplotlib and seaborn , 5. plotly , 7. bokeh ,  8. ggplot2 , 9. power bi , 10. qlikview , 11. fusioncharts ,  12. echarts , 13. google charts , 14. apache superset , 15. grafana .

D3.js , or Data-Driven Documents, is an open-source library for creating interactive graphics in a browser with the use of JavaScript. They rely on SVG , HTML, and CSS, though they offer developers a high level of control over visually produced outputs. 

Key Features of D3.js 

• Flexibility and Customization : D3. It is a very flexible language for controlling visual attributes and results in impressive graphics. 
• Interactivity: a perspective that can help with zoomable representations, panning, and real-time changes. 
• Data Binding : Performs data binding to DOM elements at an optimal rate; allows for the creation of intricate graphics. 

Applications of D3.js 

• Custom dashboards 
• Data-driven storytelling 
• Real-time data monitoring systems 

Chart.js is a language-based JavaScript library that enables users to make basic and versatile charts. Therefore, it is best suited for simple, small-scale projects as well as cases that need a snap-shot style of creation. 

Key Features of Chart.js 

• Simplicity : It is easy to learn and install as well; it has few configurations needed during its formation. 
• Responsive Design: Adapts to the size of the display screen The drop-down list of preserve or change parameters adapts to the size of the display screen. 
• Variety of Charts: Have line, bar, radar, pie, polar area, bubble, and scatter charts. 

Applications of Chart.js 

• Simple visualizations of the Web applications 
• Educational tools 
• Interactive reports 

Tableau is probably the most popular and widely used BI tool that has proven to be powerful, coupled with an easy-to-use graphical user interface. It can be used to create all forms of visualization without the need to write a single line of code.

 Key Features of Tableau

•  Drag-and-Drop Interface : If you need to develop more complicated graphics, then this application precisely washes up your work. 
• Integration: Has some input/output capabilities with spreadsheets and databases and can also connect with cloud storage services. 
• Dashboards: Interactive dashboards can help users create special work spaces that allow for detailed analysis of the gathered data. 

Applications of Tableau

• Business intelligence 
• Data analysis for decision-making 
• Executive dashboards
• Matplotlib is a powerful library to create graphs, either static, animated, or interactive. Seaborn is an interface for Matplotlib used for creating visually pleasing statistical graphics. 

 Key Features of Matplotlib

• Versatility: It is possible to come up with a number of plots and charts, simplifying the execution of an enhanced, comprehensive, and all-encompassing PC program. 
• Customization: Very flexible to allow precise control of the graphics to appear in plots. 
• Integration: Compatible with most of the Python libraries and data structures, like NumPy and Pandas.
• Statistical Plots: An ideal tool when it comes to the creation of highly elaborate statistical data graphical interfaces. 
• Themes: Comes with a beautiful default style and color schemes that apply to the application. 
• Ease of Use: Abstracting to higher layers makes it simpler to produce frequent visions. 

Applications of  Matplotlib

•  Academic research 
• Data science projects 
• Exploratory data analysis 

Plotly is a graphing library; it is open source and facilitates the generation of an interactive plot. It includes support for more than one language, for example, Python , R , and nowadays the most famous JavaScript. 

 Key Features of Plotly

•  Interactivity: Enhances graphic output through the creation of highly interactive plots fit to be integrated into web apps. 
• Cross-Language Support: Integrated with more than one programming paradigm. 
• 3D Plots: When it comes to supporting 3D visualization, they are in its favor. 

Applications of Plotly

•  Interactive data exploration 
• Web-based data applications 
• Scientific research 

6. Highcharts 

Highcharts is an open-source charting solution created in JavaScript that provides users with the opportunity to build many different attractive and interactive charts for web applications. 

 Key Features of Highcharts

• Interactive Charts: Highcharts allows features like zooming, panning on the chart, and real-time updates of the chart. This, coupled with the ability of users to drill down and/or filter down, specifies certain aspects of the charts. 
• Wide Range of Chart Types: The program offers all the necessary chart types, such as line, spline, area, column, bar, pie, and scatter chart. Due to the flexibility of this network, it is ideal for use in several different data visualization functions. 
• Export Options: It is possible to export charts in Highcharts with the use of options where one can export the charts in formats like png, jpg, pdf, and svg, among others. This feature is beneficial when it comes to sharing, especially when printing charts. 

Applications of Highcharts

• Business Dashboards
• Reporting Tools

Bokeh is a web-based interactive visual tool built for Python that helps in producing modern web browser-based applications. It enables one to create live plots, dashboards, and even data applications. 

Key Features of Bokeh

• Interactive Plots: Bokeh allows the creation of interactive plots, which allow users to zoom in and out of plots or select certain regions of interest within plots within a web browser. Some of the supported features include zooming, panning, and hovering. 
• High-Performance: It is a feature that has been incorporated into the architecture of the library in the form of the capability to work with big data.
• Customizable: Bokeh is very flexible; it allows for complex layouts and offers a lot of options to style the visualization to fit a given task. 

Applications of Bokeh

• Data Exploration
• Scientific Visualizations
• Custom Web Applications

It is a package that offers tools for creating data’s visualizations in the R language according to the grammar of graphics. It gives a logical framework for constructing a large variety of graphics. 

Key Features of ggplot2

• Grammar of Graphics: ggplot2 enables users to build visualizations using the principles of grammar, so users can achieve elaborated and personalized plots. 
• Customization: This package allows users to fine-tune every parameter, as it shows in the provided package that many aspects related to a plot can be modified. 
• integration: ggplot2 works well in combination with many other packages in R, so it is possible to manipulate the data and analyze it in the same environment. 

Applications of ggplot 2: 

• Statistical Analysis
• Research Publications

This service from Microsoft is a business analytics tool that allows users to create multiple interactive dashboards. It aids organizations in the analysis of data as well as in spreading insights. 

Key Features of Power BI

• Integration: While using Power BI, users can import data from Excel, SQL Server, the cloud, and other related services. 
• Interactive Reports: It also has tools for developing areas with various interactions to create more interactive reports and dashboards, and it also allows users to explore data easily with features such as drag and drop. 
• Data Sharing : Accessibility here implies that Power BI makes it easier for people in different organizations to share insights and even work together based on the results they get from the analyses. 

Applications of Power BI

• Business Analytics
• Data-Driven Decision-Making

QlikView, on the other hand, is a business discovery tool where BI is made available to business users in organizations. In a nutshell, it makes it easy to perform analytics quickly and with flexibility. 

Key Features of QlikView : 

• In-Memory Processing: The business logic of QlikView is based on in-memory processing, which allows for analyzing and accessing the data in real time. 
• Associative Model: It implies that in the associative model, the exploration of data is not imposed by queries, but users are able to navigate and make connections on their own. 
• Interactive Visualizations: QlikView implements various types of active visualizations to encourage the client to intervene with the information. 

Applications of QlikView: 

• Data Discovery
• Dashboards and Reporting

The FusionCharts JavaScript library is a comprehensive solution to creating over 90+ charts, maps, and other appealing graphical representations of data. 

 Key Features of FusionCharts : 

• Variety of Charts: FusionCharts has over 90+ charts and maps that are comprehensive to the different kinds of requirements in data representation. 
• Customization: The library is very flexible in respect to which aspects of a chart can be modified and changed to meet the needs of the user. 
• Cross-Platform: FusionCharts works on all the OS and devices used nowadays, and compatibility is maintained along with responsiveness. 

Applications of FusionCharts : 

ECharts is an interactive and powerful library for charting and visualization in the browser. It is meant to develop highly specific and engaging forms of visualization. 

Key Features of ECharts: 

• Rich Chart Types: The chart types that ECharts provides are: line chart, bar chart, pie chart, scatter chart, radar chart, and so on, so they can be versatile for different occasions. 
• Highly Interactive: Some of the interactions are zooming interactions, panning interactions, and tooltip interactions, which are helpful in the library. 
• Customization: ECharts is very flexible, and users can style the visualizations based on their preferences to meet their requirements. 

Applications of ECharts: 

• Data Visualization for Web Applications
• Data Analysis 

Google Charts is a rather utilitarian tool, which, however, just like any part of the Google civilization, is rather effective in its application. It is well-suited to function with web applications. 

Key Features of Google Charts : 

• Easy to Use: Google Charts are rather easy to use with web applications; no additional configuration is needed. 
• Variety of Charts : Some of them include line, bar, pie charts, scatter charts, and many more charts that are available in the library. 
• Cross-Platform: Google Charts can be used on any platform, including desktops and mobile devices, which makes it more responsive. 

Applications of Google Charts : 

• Web-Based Data Visualization: Google Charts is an application used in the creation of gadgets that display real-time data in web applications. 
• Interactive Reports: Reporting tools are also incorporated into the library, where they retrieve and display data analysis in the form of interactivity reports. 
• Dashboards: Google Charts is used for designing dashboards that contain indicators of performance and metrics.

Apache Superset is yet another open-source tool used for analyzing and visualizing data. It enables the users to design versatile and engaging interfaces and views for the built dashboards and data graphics. 

Key Features of Apache Superset : 

• Interactive Dashboards: Superset supports creating visual and engaging self-service dashboards, thus making data querying dynamic. 
• SQL Editor: It has a live SQL editor for writing SQL queries and using SQL queries to create further visuals. 
• Data Source Connectivity: Superset can interact with various forms of data, is rather flexible, and can be integrated with others. 

Applications of Apache Superset : 

Grafana is a free open-source data visualization and monitoring tool. They offer current and historical time-series data in the form of flexible and dynamic, real-time, and user-friendly dashboards. 

Key Features of Grafana : 

• Interactive Dashboards: In the case of Grafana, this is an environment that supports designing highly communicative and even highly personalized dashboards. 
• Data Source Connectivity: An advantage of Grafana is that it works with various data sources, such as databases, cloud services, and more monitors. 
• Alerting: There are features such as the alert and notification systems to keep the users aware of critical incidents. 

Applications of Grafana : 

• Monitoring Systems

Conclusion 

Data visualization frameworks are essential components of any organization’s data analysis toolkit. With the help of the strengths of various frameworks such as D3.js, Chart.js, Tableau, Matplotlib, Seaborn, and Plotly, data professionals are able to weave stories that help them make effective decisions. The latter results from the diversification of requirements within the project, as well as the characteristics of the data obtained and the required degree of interactivity or personalization.

Data Visualization Frameworks – FAQ’s

What is a data visualization framework.

A data visualization framework can be described as an instrument or a set of tools that enable the conversion of the data for presentation in the form of charts, graphs, and dashboards. These frameworks help data analysts as well as scientists convey insights easily. 

How do I choose the right data visualization framework?

Some of the considerations include the nature of the data, interactivity, simplicity, level of customization, and compatibility with other languages and tools. 

Are there free data visualization frameworks available?

Yes, there are free and open source data visualization frameworks that are available, and one of them is D3.js, Chart.Pandas, while the major data visualization libraries are Js, Matplotlib, Seaborn, and Plotly. These tools possess great versatility, and the client can easily adapt most of them to his needs. 

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Assignment overview

You will get the most of out this class if you:

• Engage with the readings and lecture materials
• Regularly use R

Each type of assignment in this class helps with one of these strategies.

Reflections

To encourage engagement with the course content, you’ll need to write a ≈150 word reflection about the readings and lectures for the session That’s fairly short: there are ≈250 words on a typical double-spaced page in Microsoft Word (500 when single-spaced).

You can do a lot of different things with this memo: discuss something you learned from the course content, write about the best or worst data visualization you saw recently, connect the course content to your own work, etc. These reflections let you explore and answer some of the key questions of this course, including:

• What is truth? How is truth related to visualization?
• Why do we visualize data?
• What makes a great visualization? What makes a bad visualization?
• How do you choose which kind of visualization to use?
• What is the role of stories in presenting analysis?

The course content for each session will also include a set of questions specific to that topic. You do not have to answer all (or any) of these questions . That would be impossible. They exist to guide your thinking, that’s all.

I will grade these memos using a check system:

• ✔+: ( 11.5 points (115%) in gradebook ) Reflection shows phenomenal thought and engagement with the course content. I will not assign these often.
• ✔: ( 10 points (100%) in gradebook ) Reflection is thoughtful, well-written, and shows engagement with the course content. This is the expected level of performance.
• ✔−: ( 5 points (50%) in gradebook ) Reflection is hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

Notice that is essentially a pass/fail or completion-based system. I’m not grading your writing ability, I’m not counting the exact number of words you’re writing, and I’m not looking for encyclopedic citations of every single reading to prove that you did indeed read everything. I’m looking for thoughtful engagement, that’s all. Do good work and you’ll get a ✓.

You will turn these reflections in via iCollege. You will write them using R Markdown and they will be the first section of your daily exercises (see below).

Each class session has interactive lessons and fully annotated examples of code that teach and demonstrate how to do specific tasks in R. However, without practicing these principles and making graphics on your own, you won’t remember what you learn!

To practice working with {ggplot2} and making data-based graphics, you will complete a brief set of exercises for each class session. These exercises will have 1–3 short tasks that are directly related to the topic for the session. You need to show that you made a good faith effort to work each question. The problem sets will also be graded using a check system:

• ✔+: ( 11.5 points (115%) in gradebook ) Exercises are 100% completed. Every task was attempted and answered, and most answers are correct. Knitted document is clean and easy to follow. Work is exceptional. I will not assign these often.
• ✔: ( 10 points (100%) in gradebook ) Exercises are 70–99% complete and most answers are correct. This is the expected level of performance.
• ✔−: ( 5 points (50%) in gradebook ) Exercises are less than 70% complete and/or most answers are incorrect. This indicates that you need to improve next time. I will hopefully not assign these often.

Note that this is also essentially a pass/fail system. I’m not grading your coding ability, I’m not checking each line of code to make sure it produces some exact final figure, and I’m not looking for perfect. Also note that a ✓ does not require 100% completion—you will sometimes get stuck with weird errors that you can’t solve, or the demands of pandemic living might occasionally become overwhelming. I’m looking for good faith effort, that’s all. Try hard, do good work, and you’ll get a ✓.

You may (and should!) work together on the exercises, but you must turn in your own answers.

You will turn these exercises in using iCollege. You will include your reflection in the first part of the document—the rest will be your exercise tasks.

Mini projects

To give you practice with the data and design principles you’ll learn in this class, you will complete two mini projects. I will provide you with real-world data and pose one or more questions—you will make a pretty picture to answer those questions.

The mini projects will be graded using a check system:

• ✔+: ( 85 points (≈115%) in gradebook ) Project is phenomenally well-designed and uses advanced R techniques. The project uncovers an important story that is not readily apparent from just looking at the raw data. I will not assign these often.
• ✔: ( 75 points (100%) in gradebook ) Project is fine, follows most design principles, answers a question from the data, and uses R correctly. This is the expected level of performance.
• ✔−: ( 37.5 points (50%) in gradebook ) Project is missing large components, is poorly designed, does not answer a relevant question, and/or uses R incorrectly. This indicates that you need to improve next time. I will hopefully not assign these often.

Because these mini projects give you practice for the final project, I will provide you with substantial feedback on your design and code.

Final project

At the end of the course, you will demonstrate your data visualization skills by completing a final project.

Complete details for the final project (including past examples of excellent projects) are here.

There is no final exam. This project is your final exam.

The project will not be graded using a check system. Instead I will use a rubric to grade four elements of your project:

• Technical skills
• Visual design
• Truth and beauty

If you’ve engaged with the course content and completed the exercises and mini projects throughout the course, you should do just fine with the final project.

Data Storytelling for Business Understanding and Progress: Visualization, Dashboarding, and Beyond

Data can be gathered and shared from virtually any source, but mastering the art of interpreting, communicating, and leveraging this information is essential for fostering business growth, understanding, and advancement. At our recent Data & Analytics Interest Group event on July 25th, we delved into techniques for creating compelling data presentations. The focus was on transforming raw data into powerful narratives that maximize impact and effectively engage audiences.

Jack Beckwith, the founder and Creative Director of DataFace, shared his insights on how DataFace operates at the intersection of data science, journalism, and information design. Jack detailed his team’s unique approach to data sharing, which involves exploring various media outlets, design aggregators like Pinterest, and data visualization blogs. He also emphasized the importance of being mindful of the impact data can have on groups or individuals and the need to consider the risks and rewards that data can bring to a business.

During the member spotlight, individuals from UW Health, Rockwell Automation, and Brunswick shared valuable insights. Stacy Rasmussen, VP of Business Relationship Management (BRM) at UW Health, discussed how BRM is both an art and a science, much like storytelling. She highlighted how BRM focuses on strengthening partnerships with operational leaders, acting as a bridge between business and Information Services. With the end goal in mind, they work backward to craft the desired outcomes and visuals. They aim to standardize approaches and deliver consistent experiences across the organization by leveraging well-researched and reliable data.

Finally, our attendees had the opportunity to discuss and elaborate on the knowledge shared during guided breakout discussions. A heartfelt thank you to all the presenters and attendees who made this event a resounding success! For those who couldn’t join us, or who want to revisit these incredible presentations, members can access the event recording on UWEBC+ .

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Data Analytics & Visualization: Using Excel and Python [Free Analysis Course] - TechCracked

Unlocking Insights through Data: Mastering Analytics and Visualization for In-Demand Tech Proficiency

This course includes :

• 17 hours on-demand video
• Assignments
• 35 downloadable resources
• Access on mobile and TV
• Full lifetime access
• Certificate of completion

What you'll learn

• Real-world use cases of Python and its versatility.
• Installation of Python on both Mac and Windows operating systems.
• Fundamentals of programming with Python, including variables and data types.
• Working with various operators in Python to perform operations.
• Fundamental concepts and importance of statistics in various fields.
• How to use statistics for effective data analysis and decision-making.
• Introduction to Python for statistical analysis, including data manipulation and visualization.

Description

Embark on a transformative journey into the dynamic realm of Data Analytics and Visualization, where you will acquire essential and highly sought-after tech skills. This comprehensive course is meticulously designed to empower you with proficiency in key tools and methodologies, including Python programming, Excel, statistical analysis, data analysis, and data visualization. These skills are crucial for anyone aiming to excel in the rapidly evolving field of data science and analytics.

Key Learning Objectives:

- Gain extensive hands-on experience in Python, a powerful and versatile programming language widely used for data analysis and manipulation across various industries. Python's simplicity and readability make it an ideal choice for both beginners and experienced programmers.

- Learn to leverage Python libraries such as Pandas and NumPy for efficient data handling and manipulation. These libraries are essential for performing complex data operations, transforming raw data into meaningful insights, and conducting comprehensive data analyses.

- Develop advanced skills in Excel, exploring its robust features for data organization, analysis, and visualization. Excel remains a cornerstone tool for data professionals due to its versatility and powerful functions.

- Harness the power of Excel functions and formulas to extract insights from complex datasets. You'll learn how to use pivot tables, advanced charting techniques, and data modeling to uncover hidden patterns and trends.

- Acquire a solid foundation in statistical concepts and techniques essential for making informed decisions based on data. Understanding statistical principles is crucial for interpreting data accurately and deriving actionable insights.

- Apply statistical methods to interpret and draw meaningful conclusions from data sets. You'll learn techniques such as hypothesis testing, regression analysis, and probability distributions to analyze and understand data thoroughly.

- Explore the entire data analysis process, from data cleaning and preprocessing to exploratory data analysis (EDA) and feature engineering. These steps are vital for preparing data for advanced analysis and machine learning applications.

- Learn how to identify patterns, outliers, and trends within datasets, enabling you to extract valuable insights. You'll use statistical and graphical methods to explore data sets and uncover significant findings.

- Master the art of presenting data visually through a variety of visualization tools and techniques. Effective data visualization is key to communicating insights clearly and persuasively.

- Use industry-standard tools like Matplotlib and Seaborn to create compelling and informative data visualizations. These libraries allow you to generate a wide range of plots and charts to effectively present your data findings.

Upon completion of this course, you will possess a well-rounded skill set in data analytics and visualization, equipping you to tackle real-world challenges and contribute meaningfully to data-driven decision-making in any professional setting. You will be able to analyze and visualize data with confidence, providing valuable insights that can drive strategic decisions. Join us on this journey to become a proficient and sought-after tech professional in the field of data analytics and visualization, and take your career to new heights.

Who this course is for:

• Beginners with no prior programming experience.
• Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.
• Anyone interested in automating tasks or data analysis.
• Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.
• Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.
• Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.

Course is FREE for Limited Time Only!

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Nasa shares its spacex crew-10 assignments for space station mission.

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Nasa headquarters.

As part of NASA’s SpaceX Crew-10 mission, four crew members are preparing to launch for a long-duration stay aboard the International Space Station.

NASA astronauts Commander Anne McClain and Pilot Nichole Ayers , JAXA (Japan Aerospace Exploration Agency) astronaut Mission Specialist Takuya Onishi, and Roscosmos cosmonaut Mission Specialist Kirill Peskov will join astronauts at the orbiting laboratory no earlier than February 2025.

The flight is the 10th crew rotation with SpaceX to the station as part of NASA’s Commercial Crew Program. While aboard, the international crew will conduct scientific investigations and technology demonstrations to help prepare humans for future missions and benefit people on Earth.

Selected by NASA as an astronaut in 2013, this will be McClain’s second spaceflight. A colonel in the U.S. Army, she earned her bachelor’s degree in Mechanical Engineering from the U.S. Military Academy at West Point, New York, and holds master’s degrees in Aerospace Engineering, International Security, and Strategic Studies. The Spokane, Washington, native was an instructor pilot in the OH-58D Kiowa Warrior helicopter and is a graduate of the U.S. Naval Test Pilot School in Patuxent River, Maryland. McClain has more than 2,300 flight hours in 24 rotary and fixed-wing aircraft, including more than 800 in combat, and was a member of the U.S. Women’s National Rugby Team. On her first spaceflight, McClain spent 204 days as a flight engineer during Expeditions 58 and 59 and was the lead on two spacewalks, totaling 13 hours and 8 minutes. Since then, she has served in various roles, including branch chief and space station assistant to the chief of NASA’s Astronaut Office.

Ayers is a major in the U.S. Air Force and the first member of NASA’s 2021 astronaut class named to a crew. The Colorado native graduated from the Air Force Academy in Colorado Springs with a bachelor’s degree in Mathematics and a minor in Russian, where she was a member of the academy’s varsity volleyball team. She later earned a master’s in Computational and Applied Mathematics from Rice University in Houston. Ayers served as an instructor pilot and mission commander in the T-38 ADAIR and F-22 Raptor, leading multinational and multiservice missions worldwide. She has more than 1,400 total flight hours, including more than 200 in combat.

With 113 days in space, this mission also will mark Onishi’s second trip to the space station. After being selected by JAXA in 2009, he flew as a flight engineer for Expeditions 48 and 49 became the first Japanese astronaut to robotically capture the Cygnus spacecraft. He also constructed a new experimental environment aboard Kibo, the station’s Japanese experiment module. Since his spaceflight, Onishi became certified as a JAXA flight director, leading the team responsible for operating Kibo from JAXA Mission Control in Tsukuba, Japan. He holds a bachelor’s degree in Aeronautics and Astronautics from the University of Tokyo and was a pilot for All Nippon Airways, flying more than 3,700 flight hours in the Boeing 767.

NASA’s SpaceX Crew-10 mission also will be Peskov’s first spaceflight. Before his selection as a cosmonaut in 2018, he earned a degree in Engineering from the Ulyanovsk Civil Aviation School and was a co-pilot on the Boeing 757 and 767 aircraft for airlines Nordwind and Ikar. Assigned as a test-cosmonaut in 2020, he has additional experience in skydiving, zero-gravity training, scuba diving, and wilderness survival.

For more than two decades, people have lived and worked continuously aboard the  International Space Station , advancing scientific knowledge and demonstrating new technologies, making research breakthroughs not possible on Earth. The station is a critical testbed for NASA to understand and overcome the challenges of long-duration spaceflight and to expand commercial opportunities in low Earth orbit. As commercial companies focus on providing human space transportation services and destinations as part of a robust  low Earth orbit economy , NASA’s Artemis campaign is underway at the Moon, where the agency is preparing for future human exploration of Mars.

Find more information on NASA’s Commercial Crew Program at:

https://www.nasa.gov/commercialcrew

Joshua Finch / Claire O’Shea Headquarters, Washington 202-358-1100 [email protected] / claire.a.o’[email protected]

Raegan Scharfetter Johnson Space Center, Houston 281-910-4989 [email protected]

Related Terms

• Commercial Crew
• Anne C. McClain
• International Space Station (ISS)
• ISS Research
• Kennedy Space Center
• Nichole Ayers

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The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.

In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. Students will learn how to design and build interactive visualizations for the web, using the D3.js (Data-Driven Documents) framework.

In addition to class discussions, students will complete visualization design and data analysis assignments, as well as a final project. Students will share the results of their final project as both an interactive website and a short presentation.

• Interactive Data Visualization for the Web, 2nd Edition . Scott Murray, O'Reilly Press. ( Read Online! ) Code examples available on GitHub .

Learning Goals & Objectives

• An understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.
• Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, networks, text and cartography.
• Practical experience building and evaluating visualization systems.

Schedule & Readings

• REQUIRED Chapter 1: Information Visualization, in Readings in Information Visualization . Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999.
• REQUIRED Design and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
• Optional Decision to Launch the Challenger, in Visual Explanations . Edward Tufte. (See also a critique of Tufte's argument .)
• REQUIRED Chapters 1, 2 & 3 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• Optional Learn JS Data: Data Cleaning, Manipulation, and Wrangling in JavaScript.
• REQUIRED The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman. Proc. IEEE Conference on Visual Languages, 1996.
• REQUIRED Chapters 4 & 5 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte, Diane Tang, and Pat Hanrahan. IEEE Transactions on Visualization and Computer Graphics, 2002.
• Optional Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe & Jeffrey Heer. IEEE Transactions on Visualization and Computer Graphics, 22(1), 649-658, 2016.
• Optional Exploratory Data Analysis, NIST Engineering Statistics Handbook.
• REQUIRED Chapters 6, 7 & 8 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• REQUIRED Introducing d3-scale. Michael Bostock. 2015.
• REQUIRED Chapter 3: The Power of Representation, in Things That Make Us Smart . Don Norman.
• Optional A Tour through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky. ACM Queue, 8(5). 2010.
• REQUIRED Chapter 9 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• REQUIRED D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky & Jeffrey Heer. InfoVis 2011.
• Optional Vega Lite: A Grammar of Interactive Graphics. K. Wongsuphasawat, D. Moritz, A. Satyanarayan & J. Heer. OpenVis Conf 2017.
• REQUIRED Chapters 10, 11 & 12 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• REQUIRED Interactive Dynamics for Visual Analysis. Jeffrey Heer & Ben Shneiderman. 2012.
• Optional The Death of Interactive Infographics? Dominikus Baur. 2017.
• Optional In Defense of Interactive Graphics. Gregor Aisch. 2017.
• REQUIRED Perception in Visualization. Christopher Healey.
• REQUIRED 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
• Optional Graphical Perception: Theory, Experimentation and the Application to the Development of Graphical Models. William S. Cleveland, Robert McGill. J. Am. Stat. Assoc. 79(387):531-554, 1984.
• REQUIRED Color Use Guidelines for Data Representation. Cynthia Brewer. Proc. Section on Statistical Graphics, American Statistical Association, pp. 55-60, 1999. Color Scheme Explorer .
• REQUIRED D3 color scales: Sequential scales , Category scales , d3-scale-chromatic .
• Optional Colorgorical: Creating Discriminable and Preferable Color Palettes for Information Visualization. Connor Gramazio, David Laidlaw & Karen Schloss. IEEE Transactions on Visualization and Computer Graphics. 2017.
• REQUIRED Visualizing Algorithms. Mike Bostock. 2014.
• REQUIRED The Visual Uncertainty Experience. Jessica Hullman. OpenVis Conf 2016.
• REQUIRED Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Michael Correll, Michael Gleicher. IEEE InfoVis 2014.
• Optional Visual Semiotics & Uncertainty Visualization: An Empirical Study. Alan MacEachren, Robert Roth, James O'Brien, Bonan Li, Derek Swingley, Mark Gahegan. IEEE InfoVis 2012.
• REQUIRED Narrative Visualization: Telling Stories with Data. Edward Segel & Jeffrey Heer. InfoVis 2010.
• REQUIRED Reinventing Explanation. Michael Nielsen, 2014.
• Optional So You Think You Can Scroll. Jim Vallandingham. OpenVis Conf 2015. (Slides, code)
• Optional Budget Forecasts, Compared With Reality. NY Times, February 2010.
• Optional How Mariano Rivera Dominates Hitters. NY Times, June 2010.
• Optional Gapminder Human Development Trends 2005.
• REQUIRED Chapter 14 in Interactive Data Visualization for the Web, 2nd Edition . Scott Murray.
• REQUIRED Chapter 11: The Cartogram: Value-by-Area Mapping, in Cartography: Thematic Map Design . Dent.
• REQUIRED Animated Transitions in Statistical Data Graphics. Jeffrey Heer & George Robertson. IEEE InfoVis 2007.
• REQUIRED Easing Functions Cheat Sheet.
• Optional Effectiveness of Animation in Trend Visualization. George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, & John Stasko. InfoVis 2008.
• REQUIRED Squarified Treemaps. Mark Bruls, Kees Huizing & Jarke van Wijk. Eurographics Data Visualization 2000.
• REQUIRED d3-hierarchy. Michael Bostock.
• REQUIRED d3-force. Michael Bostock.
• Optional Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009.
• Optional Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
• Optional Use the Force! Michael Bostock. 2011. ( Video )
• REQUIRED Chapter 10: Information Visualization for Search Interfaces, in Search User Interfaces . Marti Hearst. 2009.
• REQUIRED Chapter 11: Information Visualization for Text Analysis, in Search User Interfaces . Marti Hearst. 2009.
• Optional Mapping Text with Phrase Nets. Frank van Ham, Martin Wattenberg & Fernanda Viégas. InfoVis 2009.
• Optional A Nested Model for Visualization Design and Validation. Tamara Munzner. InfoVis 2009
• Optional Design Study Methodology: Reflections from the Trenches and the Stacks. Michael Sedlmair, Miriah Meyer & Tamara Munzner. InfoVis 2012.

Assignments

• Class Participation 10%
• Assignment 1: Visualization Design 10%
• Assignment 2: Exploratory Data Analysis 15%
• Assignment 3: Interactive Visualization 25%
• Final Project 40%

Late Policy: We will deduct 10% for each day an assignment is late.

Plagiarism Policy: Assignments should consist primarily of original work. Building off of others' work—including 3rd party libraries, public source code examples, and design ideas—is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.

Class Participation

It is important to attend the lectures and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings.

Class participation includes both in-class participation as well as participation in the discussion on Canvas . Up through week 7, all enrolled students are required to submit at least 1 substantive discussion post per week related to the course readings. Each student has 1 pass for skipping comments. Links to the Canvas discussion for weeks 0-7 are included in the schedule above.

• Critiques of arguments made in the papers
• Analysis of implications or future directions for work discussed in lecture or readings
• Clarification of some point or detail presented in the class
• Insightful questions about the readings or answers to other people's questions
• Links to web resources or examples that pertain to a lecture or reading

See the resources page for visualization tools, data sets, and related web sites.

Questions should be posted on Canvas . If you have a private question, email the instructors at cse442@cs or come to office hours.

Data Viz: Salvador Perez's solo home run

Take a look at Salvador Perez's solo home run through data visualization

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