- Tags:: 📚Books
- Author::
- Liked::
- Link::
- Source date::
- Finished date::
- Cover::
Why did I want to read it?
What did I get out of it?
A counterpoint to Down With Data Science:
so let a definition from Wikipedia (https://en.wikipedia.org/wiki/Data_science), paraphrased, suffice: Data science is the extraction of knowledge from data. (p. 38)
This idea of data as a wilderness is one of the most compelling reasons for using the term data science instead of any of its counterparts. To get real truth and useful answers from data, we must use the scientific method, or in our case, the data scientific method:
- Ask a question.
- State a hypothesis about the answer to the question.
- Make a testable prediction that would provide evidence in favor of the hypothesis if correct.
- Test the prediction via an experiment involving data.
- Draw the appropriate conclus/ions through analyses of experimental results. In this way, data scientists are merely doing what scientists have been doing for centuries, albeit in a digital world. Today, some of our greatest explorers spend their time in virtual worlds, and we can gain powerful knowledge without ever leaving our computers. (p. 44)