Category Archives: Worth Reading

Reading Recommendation: Big Data

The rise of big data can be traced back through history. Viktor Mayer-Schönberger and Kenneth Cukier chronicle its evolution and describe its current state in Big Data: A Revolution that Will Transform How We Live, Work, and Think. I couldn’t put it down!

One defining aspect of big data is its focus on “what” data say. In other words, big data reveals trends and patterns, but it does not explain why they appear or occur. Mayer-Schönberger and Cukier make this observation about correlation and causation:

[i]n a big data world…we won’t have to be fixated on causality; instead we can discover patterns and correlations in the data that offer us novel and invaluable insights. The correlations may not tell us precisely why something is happening, but they alert us that it is happening.

How does this point impact how you understand big data and its impact?

 

Source: Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data: A Revolution that Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt, 2013.

Image: “Sunrise Sky Blue Sunlight Clouds Dawn Horizon” by PublicDomainPictures, on Pixabay. CC0 Public Domain.

Reading Recommendation: Predictive Analytics

When used to make predictions, data can be quite powerful! A common example is the story of the retailer Target’s prediction of a customer’s pregnancy. When the company sent coupons for baby products to a teen, her father complained. However, it turned out that she was indeed pregnant. Such stories can be impressive and concerning. In addition to learning trends and patterns from data, data can lead to new information. In the case of Target and the teen, the store did not just know what the teen bought. Those data suggested more information: her pregnancy. As Eric Siegel writes:

[t]his isn’t a case of mishandling, leaking, or stealing data. Rather, it is the generation of new data, the indirect discovery of unvolunteered truths about people. Organizations predict these powerful insights from existing innocuous data, as if creating them out of thin air.

To understand how predictive analytics work, Siegel provides a wealth of examples and in-depth explanations in Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Understanding how organizations glean information from data and use that information helps us understand marketing and decisionmaking today. It also helps us manage our personal data.

 

Source: Siegel, Eric. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken, New Jersey: John Wiley & Sons, 2013.

Image: “Women Grocery Shopping.jpg” by Bill Branson (Photographer), on Wikimedia Commons. Public Domain. 

Reading Recommendation: Diary of a Citizen Scientist

One way for you and your students to get your feet wet with data is citizen science. Citizen science endeavors involve collecting data, which make such projects great activities for applying data literacy skills. In fact, citizen science is one of our themes for the second year of this project, starting in the fall!

For ideas to embark on a citizen science project, check out the book, Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World by Sharman Apt Russell. Russell writes about her project to study the Western red-bellied tiger beetle by the Gila River (pictured above) in southwestern New Mexico. This book is mix of a diary, environmental messages, and how-to guide for being a citizen scientist. Her work will inspire you to dive into a citizen science project. Not only will you learn about Russell’s research but also about other citizen science initiatives, like Galaxy Zoo and Project FeederWatch.

Russell chronicles her successes and challenges, as well as reflects on her motivation for doing citizen science, in the book:

We all want to be part of something larger. We want to be part of a family, a community, a cause. We want to be part of something meaningful. Studies show that long-term happiness depends on this engagement. I personally want to advance conservation policy. I want to do real science. I want to learn more science.

It’s inspiring! This book is in the style of nature writing with both personal reflections and scientific information. Russell weaves stories and tips in with descriptions of her experiences. Reading her account makes a citizen science project seem manageable and provides a great introduction to citizen science.

 

Source: Russell, Sharman Apt. Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World. Corvallis, OR: Oregon State University Press, 2014.

Image: Middle Fork of the Gila River, SW New Mexico” by Joe Burgess, on Wikipedia. Public Domain.

Reading Recommendation: Stat-Spotting

In the first year of this project, we have focused on the themes of statistical literacy, data as argument, and data visualization. One book that supported our understanding of statistics and data in the wild is Stat-Spotting: A Field Guide to Identifying Dubious Data by Joel Best.

Statistics are formed from data. As Best writes, “[e]very statistic is the result of specific measurement choices.” Keeping this idea in mind is important when interpreting statistics that you encounter. Statistics are representations of data. They have been created to summarize data.

Best’s advice is easy to put into practice whenever you see a statistic. He writes:

…it is always a good idea to pause for a second and ask yourself: How could they know that? How could they measure that? Such questions are particularly important when the statistic claims to measure activities that people might prefer to keep secret. How can we tally, say, the number of illegal immigrants, or money spent on illicit drugs? Oftentimes, even a moment’s thought can reveal that an apparently solid statistic must rest on some pretty squishy measurement decisions.

Asking those questions is one way to be a more critical consumer of statistics. Try it!

 

Source: Best, Joel. Stat-Spotting: A Field Guide to Identifying Dubious Data, 2nd ed. Berkeley, CA: University of California Press, 2013.

Image: “Percent Characters Null Rate Symbol Percentage” by geralt, on Pixabay. CC0 Public Domain.

Reading Recommendation: What Stays in Vegas

One industry that uses personal data from customers is gaming. Through loyalty programs, casinos can glean information about people to customize advertising and services. Adam Tanner describes this practice in What Stays in Vegas: The World of Personal Data–Lifeblood of Big Bussiness–and the End of Privacy as We Know It:

Boosted by vast banks of computers, Caesars today know the names of the vast majority of their clients, exactly how much they spend, where they like to spend it, how often they come, and many other characteristics. They even know exactly where many of their customers are at a given moment–whether they are sitting at a specific Wheel of Fortune slot machine or playing blackjack in the wee hours of the morning. They gather all these details with the consent of those who choose to participate in their loyalty program.

Loyalty programs supply your personal data to the companies with which you sign up for them. This book made me think twice about signing up for and using loyalty programs, despite their benefits, because they require giving up so much information about my habits. I had no idea!

In What Stays in Vegas, Tanner also brings up ethical issues, such as the justifications that commercial companies have for tracking people. He questions where the line between creepy and useful is. Tanner proposes that consumers should be able to see what data that private companies have and that privacy policies should be provided consistently and recognizably. Check out his appendix for actionable ways to control your personal data, such as using an email address that does not identify you by name for communications from commercial companies and signing up for the Do Not Call Registry.

What are ways that you limit your personal data sharing? Do you participate in loyalty programs?

 

Source: Tanner, Adam. What Stays in Vegas: The World of Personal Data–Lifeblood of Big Bussiness–and the End of Privacy as We Know It. New York: PublicAffairs, a Member of the Perseus Book Group, 2014.

Image: “A view of the card tables inside the casino” by Kym Koch Thompson, on Wikipedia. CC BY 2.0. 

Reading Recommendation: Data and Goliath

Where are your data stored, and who has control of your data?

The answer to this question is not always straightforward. We don’t always know whose eyes are on our data. For example, cell phone data reside on servers of private companies. A lot of information can be gleaned from data, from your location to your relationships.

Bruce Schneier writes about surveillance via data in Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. For anyone curious about what data that companies and the government keep and monitor, it is a fascinating read.

One of Schneier’s points is about security and privacy, which pertain to data. Access to data, like cell phone logs, can reduce privacy but support security. He writes:   

[o]ften the debate is characterized as “security versus privacy.” This simplistic view requires us to make some kind of fundamental trade-off between the two: in order to become secure, we must sacrifice our privacy and subject ourselves to surveillance. And if we want some level of privacy, we must recognize that we must sacrifice some security in order to get it.

However, this contrast between security and privacy might not be necessary. Schneier goes on to point out that:

[i]t’s a false trade-off. First, some security measures require people to give up privacy, but others don’t impinge on privacy at all: door locks, tall fences, guards, reinforced cockpit doors on airplanes. When we have no privacy, we feel exposed and vulnerable; we feel less secure. Similarly, if our personal spaces and records are not secure, we have less privacy. The Fourth Amendment of the US Constitution talks about ‘the right of the people to be secure in the persons, houses, papers, and effects’… . Its authors recognized that privacy is fundamental to the security of the individual.

More generally, our goal shouldn’t be to find an acceptable trade-off between security and privacy, because we can and should maintain both together.

Schneier’s book is illuminating for considering personal data management (one of the themes for the upcoming second year of our project in 2016-2017!) in light of data use by commercial companies and government. Schneier takes a philosophical approach to discussing data, security, and privacy. He concludes with useful tips for protecting your data. Read Data and Goliath for some great food for thought!


Source: Schneier, Bruce. Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. New York: W.W. Norton & Company, 2015.

Image: “People Lens White Eye Large” by skitterphoto.com, on Pexels. CC0 Public Domain. 

Reading recommendation: Everydata

Looking for something to read? Are you seeking to brush up on data literacy basics?

Everydata: The Misinformation Hidden in the Little Data You Consume Everyday by John H. Johnson and Mike Gluck is a nice introduction to developing critical thinking skills for data. It is full of bite-sized examples from everyday life, as Fast Company‘s review points out. At the end of each chapter, there is a handful of tips on how to apply the topics in the chapter.

For example, Johnson and Gluck shed light on self-reported data:

How many times did you eat junk food last week?

How much TV did you watch last month?

How fast were you really driving?

When you ask people for information about themselves, you run the risk of getting flawed data. People aren’t always honest. We have all sorts of biases. Our memories are far from perfect. With self-reported data, you’re assuming that “8” on a scale of 1 to 10 is the same for all people (it’s not). And you’re counting on people to have an objective understanding of their behavior (they don’t). (p. 20-1)

Johnson and Gluck acknowledge that “[s]elf-reported data isn’t always bad…. It’s just one more thing to watch out for, if you’re going to be a smart consumer of data.” This salient point is easy to keep in mind when looking at sources with students, reading the newspaper, browsing the web, listening to the radio on the way home from work, etc.

Everydata isn’t about the math; it’s about understanding the data and numbers that you encounter. Take a look at it for more practical tips like that one!

 

Source: Johnson, John H., and Mike Gluck. Everydata: The Misinformation Hidden in the Little Data You Consume Every DayBrookline, MA: Bibliomotion, 2016.

Image: “Photo 45717” by Dom J, on Pexels. CC0 License.

Reading recommendation: The Internet of Us

With summer fast approaching, here’s a book suggestion!

I just finished The Internet of Us: Knowing More and Understanding Less in the Age of Big Data. It offers an interesting commentary on how people interact with information and big data.

Author Michael Patrick Lynch takes a philosophical approach to issues in the information age.  He writes about the difference between knowing and understanding. Have you ever been concerned about big data’s focus on the “what,” rather than the “why?” And how people say that sometimes the “what” is enough for understanding trends? Lynch recognizes this concern. He points out issues with this practice of only considering what is happening, of looking at correlations only.

Lynch asserts that three aspects compose big data:

  1. the volume of data,
  2. analysis of that data,
  3. and uses of that data by big companies.

He also discusses the dangers of decreased privacy owing to the creation of data through our activities and the use of it by companies.

Data analysis is impossible without context, according to Lynch. This point feeds his conclusion that knowing how parts connect with the whole is key to being a responsible “knower.” People need to see how information that they find online fits with their broader knowledge and the world. Seeing this bigger picture allows them to be creative. As he writes:

…our digital form of life tends to put more stock in some kinds of knowing than others. Google-knowing has become so fast, easy and productive that it tends to swamp the value of other ways of knowing like understanding. And that leads to our subtly devaluing these other ways of knowing without our even noticing that we are doing so–which in turn can mean we lose motivation to know in these ways, to think that the data just speaks for itself. And that’s a problem–in the same way that our love affair with the automobile can be a problem. It leads us to overvalue one way to get to where we want to go, and as a result we lose sight of the fact that we can reach our destinations in other ways–ways that have significant value all their own. (p. 179-80)

The Internet of Us shows both the pros and cons of technology and big data. It is not an anti-technology book. Instead, Lynch raises awareness of modern practices. Lynch’s distinction between knowing by searching online and actually developing skills is something that’d we’d all do well to remember. For those of you who are looking for inspiration — and points to make when students wonder why they have to learn something when they can just find the information online — this book is for you!

 

Source: Lynch, Michael Patrick. The Internet of Us: Knowing More and Understanding Less in the Age of Big Data. New York: Liveright Publishing Corporation, 2016.

Image: “Photo of Holloways Beach, QLD, Australia” by Alexander Khimushin, on Wikipedia. CC BY-SA 3.0.

Why care about data literacy? Check out these slides

Our team member Jennifer Colby, a teacher librarian at Huron High School in Ann Arbor, put together an informative presentation called “What is Data Literacy: Getting our students from data to knowledge.” In it, she expresses the importance of data literacy in classroom learning and gives four salient reasons, quoted from her slides:

  1. To develop literacy skills
  2. To develop standardized test taking skills
  3. To address state and national standards
  4. To develop informed citizens

Her slides highlight examples of how to approach data, statistics, and visualizations. Data literacy applies to all content areas. Take a look at the infographics on Romeo and Juliet — they give plot insights through their visual representations of events.

In the big picture, data literacy helps students with “understanding,” “extracting,” and “presenting” data. With so much data in school and everyday life, these competencies are key.

Keep her points in mind to incorporate data literacy into your instruction.

Holistically Integrating “Real Math” into the Curriculum

Calculus Image

“Andrew Hacker, a professor of both mathematics and political science at Queens University has a new book out, The Math Myth: And Other STEM Delusions, which makes the case that the inclusion of algebra and calculus in high school curriculum discourages students from learning mathematics, and displaces much more practical mathematical instruction about statistical and risk literacy, which he calls “Statistics for Citizenship.””

Andrew Hacker has an intriguing idea that the high school math curriculum needs to be radically re-examined.   And, no … He’s not talking about Common Core!  What if we didn’t teach Algebra II and Calculus in high school and instead taught, “Statistics for Citizenship?”  Would there be less math anxiety for students?  Would lessening the requirements for some professional training , like EMT training, open professional doors and expand the workforce?  Would students be more statistically literate and better citizens?  Would we finally be able to answer the age-old, high school question, “When am I going to use calculus in real life?”  (And just to be clear … I LOVED math in high school and took every math class that I could!).

Hacker is advocating that teaching statistical literacy explicitly in a classroom devoted to these concepts is better than dedicating part of the day to teaching advanced math.  Statistical literacy is one of the main concepts of the grant this year.  Overall, I’ve really struggled with looking at how teaching data literacy can be holistically integrated into the curriculum.  I want our work to be useful and successful so implementing it is always on my mind

School librarians present an interesting model for holistic integration of any curricular change.  While some teachers think that librarians are only working with English or Social Studies teachers, school librarians can work with any teacher — Some work with Health teachers as students create posters representing good health practices.  Others work with science fair participants to create solid ideas around the practical application of scientific concepts.  The list goes on and on.  As one of our Library Development Officers used to say to potential donors, “The Library is for Everybody.”  Having a separate statistical literacy class flies in the face of having students see how any information literacy concept is integral to the rest of their work as they encounter the practical application of these ideas throughout their day.

Dedicating part of the day to statistical literacy is intriguing to me.  Having a class like this occupy some of the day’s “real estate” would send a signal that statistics for citizenship is important for our children … Yet … I always wonder about the holistic aspect of separating out overriding conceptual ideas into their own place in the curriculum.  I struggle with this separation in teaching information literacy too — Why can’t info lit be more grounded into the rest of the curriculum, especially at the college level?  I’m not sure that I know the answers to these questions.  Maybe we could find a thematic approach — Students could have a separate class AND apply statistical literacy ideas in science, English, government, and other classes.  Is that asking too much?

Image:  “Calculus” by fitriahandayani on Flickr. CC-BY-2.0. https://flic.kr/p/9yZ1rp