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The Learning Curve: Data Science for All

Why Math Needs Data Science Reform Article preview reposted from The Learning Curve.


Almost every occupation involves interpreting and analyzing data as well as making decisions based upon that data. The modern business world was built on the spreadsheet. Medical professionals make life and death decisions comparing conflicting data. Natural scientists and social scientists wrangle data by the fistful. Politics, policy, sports, automotive technology, heck, even reading a newspaper requires data analysis skills these days.


Nonetheless, data sources are not always ethical and algorithms are not always as transparent as they initially seem. Making decisions driven by data can cause pre-existing biases to persist which leads to a lack of objectivity and can result in poor decision-making.


But the prevailing math curriculum does little to support students in developing data science vital skills. Students may make graphs in physics or have to interpret a table on a standardized test. However, they rarely have to clean data sets, make tradeoffs when choosing a data visualization, or resolve conflicting interpretations of the data in order to ensure that the outcomes are fair, accountable, and transparent.


Instead, students learn things like L'Hopital's Rule and Integration by Parts, while complaining that math has little relevance to their everyday lives.


Is it time for a change? The short answer is yes. Today’s math curriculum is not relevant for students and far more needs to be done to incorporate data science into the curriculum... Read the full article in The Learning Curve

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