Instructor: Jack S. Hale (jack.hale@uni.lu). Office hours: 1000-1100 Wednesdays, drop-in.
Please see the ACME system.
(Changed) Following the rules set out by the Vice Rectorate for Academic Affairs the course will be taught as a continuous block of 3 TUs of 45 minutes beginning at the scheduled start in the ACME system.
This four session course covers the basics of scientific programming with Python. It is aimed at people who have done some programming before, perhaps on an undergraduate course, but need a refresher before starting their Masters or Doctoral degrees at the University.
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You must bring a laptop with working WiFi access.
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We will use Etherpad, a live collaborative note taking application. Etherpad is public, please use a pseudonym and do not reveal any personal information.
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We will be using Google Colaboratory to execute Python scripts and notebooks. Please ensure that you can log on before the course starts. I will also discuss the best ways to install and use Python on your own machine.
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Please make sure you can login to wooclap.
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Important course information will be communicated via the University Moodle.
- Introduction
- Getting started
- Basics of Python
- Array computations with numpy
- Array computations with numpy (cont.)
- Plotting with matplotlib
- Tabular data manipulation with pandas
- Tabular data manipulation with pandas (cont.)
- Writing good quality and robust Python code
(Changed) A coursework will be distributed in the final class with a due date of around one month. To pass the course and receive the ECTS credits you must complete the coursework.
(Changed) A coursework will be distributed in the final class with a due date of around one month. To pass the course and receive the ECTS credits you must complete the coursework. The coursework will be assessed on the basis of code clarity as discussed in Session 2 of the course. I expect that most students will receive a good mark on the condition that they submit a clear, correct and complete piece of coursework.
This is a practical course and attendance is mandatory. A maximum of 2 TUs can be missed across the semester. Further TUs missed will require a meeting with the instructor and study programme director.
There is no retake possible - register for the course again at the next available semester.
I gratefully acknowledge the authors of the following sources:
- Python Data Science Handbook by Jake VanderPlas.
- Whirlwind Tour of Python by Jake VanderPlas.
- Programming with Python by the Software Carpentry Foundation.
- Objected-oriented Python by David Ham.