Community Data Science Course (Spring 2015)/Day 1 Plan: Difference between revisions
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* Setup and Guided Exploration (until end) | * Setup and Guided Exploration (until end) | ||
== Introductions | == Introductions == | ||
* Myself | * Myself | ||
Latest revision as of 00:46, 31 March 2015
Overview[edit]
- Introductions (30m)
- Motivation/pre-lecture (10m)
- Syllabus (60m)
- Break
- Setup and Guided Exploration (until end)
Introductions[edit]
- Myself
- William
- Anissa
- Everybody: name, any relevant background, and why you are here
Motivation[edit]
Why Programming and Data Science[edit]
The question of who controls our technology, our information, and our data, is increasingly the question of who controls our experience of the world and each other. Programming is the power to define technology. It can be in, this sense, deeply empowering.
In a technological and data driven world, being able to programming and data science is a kind of literacy. Imagine a world in which everybody could read by only some people could write?
Our goal here is not turn you into the programming equivalent of novelists or journalists. Our goal is to demystify things and give you enough information to become dangerous.
Programming, you will also find — probably a little today and a lot more later on — is also enormously fun. For me, it's like meditation and problem solving. It's exactly as frustrating as a difficult puzzle and even more rewarding because your solution accomplish something else you were trying to do.
Why Python[edit]
I know a dozen programming languages and write 4-5 regularly. But Python is the right one.
Python is a fantastic language to learn[edit]
Believe it or not, compared to other programming languages:
- Python has a low "syntatic overhead".
- It's easy to get work done quickly.
- It's relatively forgiving.
Python is versatile useful for a range of applications[edit]
There are easier programming languages to learn. But Python is important because it is not a toy. In designing the curriculum for these workshops, we have tried to only teach tools that we, as professional data scientists and programmers, use ourselves and find useful.
Python is used for:
- Web applications (Instagram, Pintrest, and the Washington Post all run websites written largely in Python).
- Python can be used to extend existing applications. You can use it to script many graphical applications.
- Python is fantastic for dealing with and manipulating text.
- Python can be used to build graphical games (Frets on Fire)
- Python really shines when it comes to dealing with data and with the web.
