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The field of data science grew out of statistics. It employs statistical methods. There is significant overlap between the disciplines, but they are distinct enough that numerous universities have seen fit to have majors, and even departments, for both. Data science is multidisciplinary. In addition to statistics, data scientists use programming to manipulate real-world data to answer questions...

Is data science mathematical enough? Is it really math? Shouldn’t it be taught as an elective or in a social science class? Doesn’t introducing students to data science interfere with them learning the mathematical canon? What is important to note is that arguing for the inclusion of data science in high school shouldn’t be misconstrued as an attack on mathematics itself. 

CLAIM: High school teachers don’t know how to teach data science. FACTS: It is true that few high school teachers are well-versed in teaching data science. That is why investing in professional development is so important. If a lack of teaching experience were dispositive, curriculum would never change. While it may be an important consideration when talking about implementing data science courses....

CLAIM: High school data science courses teach things like how to use spreadsheets, so they cannot be rigorous. FACTS: High school data science courses teach higher-order skills valued by colleges and employers, such as modeling with data, understanding probability, differentiating correlation from causation, reasoning with multiple variables, and programming in industry-standard languages.

A math equation has a clear, correct answer. How to find that answer, on the other hand, is more flexible. It’s the creative journey in math that draws many to the subject and to STEM fields. One answer. Multiple possible approaches. Ironically, the debate around math curriculum is similar.