In This Issue:
Beyond Mathematics: The Importance of Understanding Data
More and more data is generated every day. Even more is expected in the future as the internet of things takes hold, and everything from driverless cars, to kitchen appliances, to industrial equipment, to health care monitoring may be hooked up to the internet, into which sensors will constantly feed data. AI (artificial intelligence) is becoming more prominent, enabling more widespread application of sophisticated algorithms for predictive analytics that can make use of the vast quantities of available data.
Not only is there so much more data, as well as more sophisticated techniques for using it, but there are also ongoing reminders of data’s prominence. We hear about data for monitoring the Covid-19 virus outbreaks, for determining pandemic restrictions, and for studying everything from treatments, to vaccines, to prevention. Aside from the pandemic, we are often reminded about data’s pervasiveness when we hear about privacy related matters, such as asking who owns your data and how do large organizations, especially the big tech companies, use your personal information.
So, the ever increasing explosion of data seems endless. As a result, more and more organizations are striving to be data driven. And, not only are businesses calling for greater use of data in decision making, but the role of data in the high school math curriculum is being reconsidered. For example, in the “Future of Everything/Education Section” of the November 13, 2020 Wall Street Journal, an article by Yoree Koh was titled “Math Class for Real Life” with a subtitle saying, “Fewer rote calculations, more data literacy and applications beyond school.” The article discusses proposals for revamping the high school math curriculum by replacing algebra II with more real world classes like data science. In my view, these proposed changes reflect data’s tremendously increasing prominence in today’s modern world.
Buried in the article, however, is one sentence so important that I am highlighting it in bold. The sentence is: “But, learning mathematics is different from understanding data.” This is a difference I relate to. I studied mathematics in college and my corporate work experience includes a stint as that era’s version of what today is called a data scientist. I have strong skills in analyzing and making sense out of data. Thus, my background gives me a grasp of how data is used and what kinds of things it can do, as well as the role mathematics can play as a foundation for understanding more advanced data analysis techniques.
I see a huge difference between learning mathematics and understanding data. Computers can learn to do mathematics, but computers still aren’t able to fully understand data. That’s why AI and data science benefit from including human input. Understanding what the data really means can be vital for making data driven decisions that really do prove useful. It can be easy to make flawed decisions when looking at data if there is not a solid understanding of what that data means or how it fits into the real world.
For example, it is important to pay attention to whether the data is afflicted with biases. The word biases has become associated with a particular problem that can occur with AI, usually where there is much input data from white males and not enough from females or non-Caucasians . However, the problem of biases is not unique to AI. Bias is can be a problem with any kind of data analysis.
Regardless of what kind of data is being looked at or what analysis techniques are used, bias can occur if the data being studied does not properly match the goal of the study. If smaller subgroups are of interest, it is important to include enough data from those subgroups. This is the case whether there’s interest in minorities that are not white males, or interest in any kind of specialized smaller subgroup being studied, whether it is people, geographic areas, businesses, machine parts or whatever. To avoid bias, the data must be representative of whatever groups the researcher expects to study. For the most part, dealing with this is not an issue of learning mathematics. It is more of an issue of understanding the data.
Business people and high school students alike can benefit from a greater understanding of this and other potential pitfalls of using data. So, I see the value of offering high school classes that expose students to what it takes to be data driven. Most students would benefit from learning to be more data literate.
However, I don’t agree that data science should replace algebra II for all students. Some high school students should have the option of taking both algebra II and data science. This is the case because some students will aspire to high tech careers where advanced mathematical knowledge is required. Such high tech opportunities will play a vital role in advancing the technologies of the future. So, preparation for a more technical track should remain available in high school, and students for whom this is suitable should not be denied the option.
Yet, the Wall Street Journal article is so right that data literacy is an area that should be offered in high school. Students would benefit from greater data literacy. Likewise, many business people today would also benefit from greater skill at understanding and using data. Regardless of how the high school math curriculum evolves, as the role of data keeps expanding, the ability to understand data is crucial. That’s why better data literacy is important, both for high school students and for companies striving to be more data driven.
If you’d like help with becoming more data literate, just contact us.
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