In a traditional computer science class the goal is to write programs. Data plays a minor role, usually just as a way to confirm that the code does what it is supposed to do. In Data Science class, the whole goal is to let the data speak, not the program:

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[D]ata science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting the story to others. source

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Wolfram|Alpha is the environment of choice for beginning data scientists for three reasons:

  • the data itself has been carefully checked and organized (“curated”) for use. Beginning data scientists do not have to worry about the gathering and massaging part. It has already been done.
  • the Wolfram Programming Language is built right  into Wolfram|Alpha. There is no need to shift to a different programming environment just to apply a line or two of code.
  • the Wolfram Language really does let data scientists get significant results with just a single line of code.

Many traditional programs must start with a lot of code just to get the data in, then end with a lot more code to display it in an understandable way. Often there are just a few lines of real work code sandwiched in the middle. In Wolfram|Alpha that real-work code tends to be all that is needed.

The Wolfram Language itself is a professional tool in wide use in universities, so middle schoolers will never outgrow it, but the goal of Data Science Class is to become experts in data, not experts in programming languages. If all it takes is a line or two of code to let some data tell its story, so much the better.