Course coordinator: Roberta Sinatra, SinatraR@ceu.edu
Brief introduction to the course
This course will provide a comprehensive, fast - paced introduction to Scientific Python. The course will run with theoretical classes, hands - on sessions and tutorials. We expect you to come to lectures and labs, ask questions when you get stuck, and develop a proje ct taking advantage of tutorials . The course will have an intensive schedule , taking place mostly during the first month of the term.
The goals of the course
The overarching goal is to equip student s with enough programming experience to start working in any area of computation and data - intensive research. This course will lay a foundation from which new tools and techniques can be explored.
Further information, such as assessment deadlines, office hours, contact details etc. will be given during the course. The instructor reserves the right to modify this syllabus as deemed necessary any time during the term. Any modifications to the syllabus will be discussed with students during a class period. Students are responsible for information given in class.
Bill Mark Lutz, Learning Python, O’Reilly (2013 ) – Also a vailable for free online
Bill Lubanovic, Introducing Python, O’Reilly (2014)
Wes McKinney, Python for Data Analysis, O’Reilly (2013)
Online resources and documentation provided during classes
In short: don't do it! You may work with friends to help guide problem solving or consult stack overflow or similar to work out a solution, but copying — from friends, previous students, or the Internet — is strictly prohibited. If caught cheating, you will fail this course. Ask questions in recitation and at office hours . If you're really stuck and can't get help, write as much code as you can and write comments within your code explaining where you're stuck.