Criteria for evaluating data visualization tools

There are a wide range of tools available to refine and query your data, and to create, edit, and display your visualizations. The types of tools available for building visualizations fall into four major categories: basic productivity applications, visualization software, business intelligence tools, and developer based packages.

It’s critical to select the tools that work best for your workflow. When evaluating a new data visualization tool, consider the following:

  • Sharing – Can others view and edit your visualization and analysis? The ability to share your charts and graphs with others enables collaboration for data visualization tasks.
  • Output – Can you publish visualizations to the web, create PDF documents, and embed them into other applications? The ultimate destination of your visualization will dictate tool choice. For example, if your audience is interpreting your graphs online, you may want to design them to be interactive to allow for exploration.
  • Interoperability – How easily can you connect to other data sources? For instance, does the software allow you to import diverse file types, such as .xls, CSV, .txt, or allow you to link to your databases?
  • Display types – What types of visualizations do you intend on building? Maps, networks, and text based visualizations are not available for every tool.
  • Data exploration – Do you need a tool to explore and present your data visually, or to present a data visualization? Features such as visual querying are not standard for every tool.
  • Simplicity – Are you looking to create charts and graphs quickly? Some tools require a steep learning curve, even to build a simple bar chart.
  • Persistence – Do you think that you will have to go back and revise the visualizations you create? It would be important to choose a technology that you think will be around for a while.

Dr. Kristen Sosulski develops innovative practices for higher education as the Director of Education for the NYU Stern W.R. Berkley Innovation Lab. She also teaches MBA students and executives data visualization, R programming, and operations management as an Associate Professor at NYU’s Stern School of Business.

Kristen’s passion for technology and learning sciences converges in all facets of her career, inside and outside of the classroom. Follower her on Twitter at @sosulski and learn more at http://kristensosulski.com. Stay connected and join her newsletter.

Effective practices for live online teaching

Live online teaching is an alternative to face-to-face teaching or asynchronous online teaching. It enables instructors to connect with students synchronously without the restrictions of a physical classroom. Web conferencing software can be used to deliver lectures, hold seminars, or facilitate study groups. This post provides a set of  “use cases” and best practices for live online classes. You can find more examples in The Essentials of Online Course Design and The Savvy Student’s Guide to Online Learning.

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Bring experts into the classroom with Skype

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Use Skype to extend your classroom by inviting experts to join remotely.

Many instructors invite professionals into their classrooms to add diversity to the discussion and dialogue. Introducing students to professionals in their field of study is one way to help them to see first-hand how experts approach and solve problems.

 

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The future of business intelligence: Data visualization

I was invited to speak at the Plotcon conference in early November. The conference has been described as “The world’s most visionary conference for data visualization in scientific computing, finance, business, and journalism.” It was a true honor to be part of an elite group of scholars, journalists, data scientists, and technologists.

My talk was titled “The Future of Business Intelligence: Data Visualization.” I spoke about the importance of not just models and technology, but about the key elements that hinder and aid the communication of information, and ultimately, decision making.

View my talk.

Dr. Kristen Sosulski develops innovative practices for higher education as the Director of Education for the NYU Stern W.R. Berkley Innovation Lab. She also teaches MBA students and executives data visualization, R programming, and operations management as an Associate Professor at NYU’s Stern School of Business.

Kristen’s passion for technology and learning sciences converges in all facets of her career, inside and outside of the classroom. Follower her on Twitter at @sosulski and learn more at http://kristensosulski.com.  Stay connected and join her #datavis newsletter.

Data Visualization: A select toolkit

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There are a wide range of tools available to refine your data and to create, edit, alter, and display your visualizations. These include: R, Python, HTML, plot.ly, JavaScript, Qlikview, Google’s Visualization API, Tableau, Domo, Adobe Illustrator, Excel, PowerPoint, KeyNote, and many more. In my courses, I teach three tools: Tableau, R, and Python. In addition, I expect  my students to be fluent in  basic productivity programs such as Excel and Google’s Visualization API. While there is not a one size fits all solution to visualization, the three toolkits I teach provide a solid foundation to visualize geospatial, categorical, time series, statistical, and network data as static, animated or interactive displays for the desktop, web, or for a presentation.


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Data Visualization: A select bibliography

et-line_e00b(0)_512There are many books, guides, and tutorials to help you learn data visualization.  In this post, I’m sharing a select bibliography of the 16 key readings that I use in my practice and teaching. The readings are diverse; data visualization as a field is interdisciplinary, combining many fields and specialties.  Principles, inspiration, and insights are drawn from the areas of statistics, communications, computer science, cognitive psychology, graphic design, information design and user experience design.

 

 

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Educational reasons for creating short teaching videos

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Many professors are creating their own multimedia content for their classes. Multimedia content comes in many forms, with the most popular being video content. However, the definition of content in this context is very narrow as it refers to the medium. This media centric view of content can make it difficult to separate the actual educational content from the medium itself. The educational content can be described as what is the professor trying to demonstrate, model, or explain to the students.

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