5 Must-See TED Talks on Data Visualization!

Data visualization is crucial in understanding data and identifying hidden connections that matter. Below are 5 TED talks on data visualization you don’t want to miss!

1. Hans Rosling: The best stats you’ve ever seen

Han Rosling, cofounder of the Gapminder Foundation, developed the Trendalyzer software that converts international statistics – such as life expectancy and child mortality rate – into innovative, interactive graphics. The statistics guru is a strong advocate for public access to data and the development of tools that make it accessible and usable for all.  In this classic talk, Rosling highlights the importance of data in debunking myths about the gap between developed countries and the so-called “developing world.” Even though the talk was filmed 10 years ago, it still carries very important and relevant messages.

Watch more of Rosling’s TED talks here.

2. David McCandless: The beauty of data visualization

In this visually captivating talk, data journalist David McCandless suggests that data visualization is a quick solution to our current problem of information overload. Visualizations allow us to see the hidden patterns, identify connections that matter, and tell stories with data. To McCandless, “even when the information is terrible, the visual can be quite beautiful”; this is a controversial claim, however, since the main goal of data visualization should be to communicate information effectively through graphical means.

3. Dave Troy: Social maps that reveal a city’s intersections – and separations

A serial entrepreneur and data-viz fan, Dave Troy takes a people-focused approach to data visualization. Troy has been mapping tweets among city dwellers, revealing what connects communities and what separates them – above and beyond demographic factors such as race or ethnicity. He compares a city to a “giant high school cafeteria” and suggests that we see “how everybody arranged themselves in a seating chart”, arguing that “maybe it’s time to shake up the seating chart a little bit” to reshape our cities.

4. Eric Berlow & Sean Gourley: Mapping ideas worth spreading

An ecologist and a physicist, Eric Berlow and Sean Gourley, collaborate in this presentation to create stunning 3D visualizations demonstrating the interconnectedness of ideas. Taking 4,000 TEDx talks from 147 countries representing 50 languages, they explore their “meme-omes” – the mathematical structures that underlie the ideas behind these talks – and discover similarities between seemingly unconnected topics. Berlow and Gourley also broke down complex themes into multiple more specific ones, seeing what topics resonated with viewers and what kind of audience looked at what topic. To Gourley, mapping ideas in this way will help us “to see what’s being said, to see what’s not being said, and to be a little bit more human and, hopefully, a little smarter.”

5. Manuel Lima: A visual history of human knowledge

Founder of VisualComplexity.com Manuel Lima, described by Wired Magazine as “the man who turns data into art,” explains the visual metaphor shift from the tree to the network as “a new lens to understand the world around us.” Lima argues that the tree – an important tool to map everything from genealogy to systems of law to Darwin’s “Tree of Life” – is being replaced by a new metaphor – the network. Rigid structures are evolving into interdependent systems, and networks emerge to embody the nonlinearity, decentralization, interconnectedness, and multiplicity of ideas and knowledge. The shift in visual metaphor also represents a new way of thinking – one that is critical for us to solve many complex problems we are facing.

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Meet Yujing Cao, DASIL’s new data scientist!

This year, DASIL welcomes a new member of our staff, Yujing Cao, who will be serving as the new data scientist. In her position at DASIL, Yujing will bring her expertise in data analysis and visualization to further expand DASIL’s capability to help students and faculty members integrate data analysis into research and classroom work.  In today’s big data era, enormous quantities of data are available, and Yujing will help Grinnell students and faculty explore them.

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Yujing Cao is excited about joining DASIL and bringing a new level of data analysis to faculty research and teaching!

Originally from China, Yujing got her bachelor degree in Statistics from Anhui University. Her passion for data science led her to a PhD program in Statistics at the University of Texas at Dallas, where she obtained her degree in 2016. Her research was on graphical modeling of biological pathways in genomic studies. She is also interested in network analysis, machine learning, and trying different tools for data visualization. In her spare time, she enjoys reading, hiking, and exercising.

Yujing was excited about the position at Grinnell because of her strong interests in teaching and in data visualization. As she puts it:

“I wanted to look for a position which provides opportunities to create interesting data visualizations along with other data analysis work. I love using graphs to tell stories behind different data sets.

Working environment is another factor that led to my decision to come to Grinnell.  I strongly resonate with the core values of a liberal arts education. At Grinnell College, I can work in an academic environment helping faculty and students while promoting the use of data in research and learning.

Yujing also discusses a number of skills crucial to succeed in the field of data science. Data science is an interdisciplinary field requiring knowledge from mathematics, statistics, data mining and machine learning. Statistical knowledge and knowledge from other fields can help form good questions and seek direction, while programming skills (e.g. joining data sets and visualizing data) are needed for implementing our ideas. To be a good data scientist, you should possess strong programming and analytical skills.”

According to Yujing, “One of the most important qualities for any data scientist is curiosity. Curiosity encourages us to dig in and make interesting discoveries about data. Also, good communication skills can make a great data scientist. You should be able to clearly articulate your results and the implications of your findings to others, including other data scientists and people who don’t share a similar background.”

Her tip for students interested in a career in data science is to keep an open mind to learn from different disciplines and sharpen your programming skills.  In addition, a student who is interested in being a data scientist should take advantage of any opportunities to get hands-on projects that use real data.”

Faculty or students interested in meeting with Yujing should drop by DASIL(ARH 130) or her office (Goodnow 103) or contact her via email at caoyujin@grinnell.edu for an appointment.

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Access to Research Includes Access to Data!

In February 2013, the Director of the White House Office of Science and Technology Policy (OSTP) issued a memorandum to all agency and department heads entitled, “Increasing Access to the Results of Federally Funded Scientific Research”.

The memo directed federal agencies that award more than $100 million in research grants to develop plans for increasing public access to peer-reviewed scientific publications. It also requires researchers to better account for and manage the digital data resulting from their federally funded research. (At the same time, the OSTP directive acknowledges that access to some data needs to be controlled to protect human privacy, confidentiality of business secrets, intellectual property interests, and other reasons.)

The OSTP recognizes that research data are valuable and need to be preserved. Increased public access to data – along with better access to the published literature – is fundamental to research, and permits

  • more thorough critiques of theories and interpretations, including replication of research results,
  • scholarly innovation that builds on past work, and
  • practical application of scholarly discoveries.

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