In this, we’ll provide a basic definition of “data science” and discuss the connotation of the term in several contexts.
Other materials
Lots of folks have opinions about what data science is. Here’s a collection of things that are worth reading (or watching).
- There was a session at JSM 2015 called “The statistics identity crisis: am I really a data scientist?”. All the talks were great; for now, two are especially relevant:
- Hilary Parker’s “Opinionated Analysis Development” is a strong argument in favor of having opinions. There’s also her paper on this topic.
- Angela Bassa talks about “Corporate Data Science”, which is useful complement to academic data science
We also touched on useful resources for learning data science. Each class session will have relevant readings; the following are useful in giving an overview about how to learn and find help.
- stackoverflow has a useful guide on how to ask a good question
- Julia Evan’s blog also has a useful guide how to ask good questions (note: hers has a cartoon!)
- (Tip: the fact that there are guides on asking questions means it isn’t always easy or obvious how to do it well. That’s fine! Learning how to ask the right questions is important, and you should practice.)
- This blog post and the follow-up disavowal are both interesting; one deals with learning to program and asking questions, and the other notes that flippant answers online are discouraging to people who want to learn but regretfully common.