Scientific Programming 1
Welcome to this programming course! In the weeks ahead, you’ll use the Python programming language while learning to solve scientific problems from several fields of science. This course is intended for students who have no experience in programming at all. It comprises four modules, wherein you learn about the Python language, but foremost about strategies you can use to solve complex computational problems.
If you have practical matters that you would like to discuss, always send an e-mail to the staff via firstname.lastname@example.org. We will answer within a couple of days, if not hours.
Your entry to the course is the sidebar, where you can leaf through all modules (levels) that you have to complete.
- Read more of the syllabus, below.
- Install Python.
- Choose one of the level 1 modules (Algorithms or Numbers) to get started!
This course assumes no prior programming experience. If you have already done a course in Python, or if you have extensive experience in another programming language, this course might not be your best option—but we’re happy to refer you to other courses if you’d like!
Other than that, some modules assume high school mathematics or physics, but many do not. If you feel overwhelmed, don’t hesitate to contact the course staff! We can explain the course’s philosophy and requirements, and make recommendations on how to approach problems.
This really is a beginner’s course. We will teach you the basics of Python programming as well as several different ways of solving computational problems. After this course, we envision that you:
- can transform the description of a simple algorithm into working code by combining basic program elements
- can apply several scientific programming techniques from different areas of study
- can use a couple of libraries in your program and know how to find and read documentation on other libraries
- can make your programs simpler and easier to read by employing a few standard tactics
- can trace and fix several common programming errors
When you have finished one modules for each level, you might take up Scientific Programming 2, in which we will teach you most of the remaining parts of Python, enabling you to read and contribute to other’s projects, or start your own!
In this course you’ll mostly work on assignments independently. But you’re not on your own! We’re here to help. There are two ways you can get help:
- Online lab-sessions: We have created an online classroom on wonder.me. In this classroom you can get help from us (and from your fellow students). We will be available at the office-hours mentioned here below. But you can log into the classroom at any time. So also outside of the office hours, you can use the classroom to meet up with other students.
- You can also ask questions on Ed, an online discussion platform. You can use this to sign up link: sign up for the Scientific Programming forum. Try to formulate your question clearly. Use code fragments to illustrate the problem. But, never copy your entire code here (this would make it too tempting for your fellow students to copy your code).
There are four moments in the week that there is help available on wonder.me:
|09:00 - 12:00||✓||✓|
|14:00 - 17:00||✓||✓|
Passing the course
The course’s final result will be “pass” or “fail”, which means that no grades are assigned. You pass the course by:
- submitting sufficient coursework
- passing the exam
Sufficient coursework means submitting a working solution to each problem from at least four modules of your own choosing, keeping in mind that you need to finish one module per level.
You may not re-submit (variations of) solutions that you wrote for any other course’s problems. In case you have done similar assignments before, discuss with the course staff whether this is the right course for you.
In light of the Covid-19 measures, the deadline schema has been revised. To make the schedule a bit lighter and allow everyone to take the week of March 22 - 26 off, we made level 3 an optional part of the programme. You can decide to still do level 3, in which case follow the dates in parentheses (). Otherwise follow the new dates.
For people following the 4-week schedule: if you skip level 3, please start Scientific Programming already Feb 18 this way you’ll be able to finish before March 19.
Deadlines for each level are listed below. Only by agreement in advance is it possible to extend these deadlines. Send an e-mail detailing your plans to the course staff at email@example.com and we will consider your proposal.
|Finish course in:||4 weeks||8 weeks||16 weeks|
|Level 1||Fri 05 Feb 2021||Wed 10 Feb 2021||Fri 19 Feb 2021|
|Level 2||Thu 11 Feb 2021||Tue 23 Feb 2021||Fri 12 Mar 2021|
|(Level 3)||(Wed 17 Feb 2021)||(Mon 08 Mar 2021)||(Fri 16 Apr 2021)|
|Level 4||Wed 17 Feb 2021/(Tue 23 Feb 2021)||Mon 08 Mar 2021/(Fri 19 Mar 2021)||Fri 16 Apr 2021/(Mon 17 May 2021)|
Note: if you would also like to take Scientific Programming 2 in the first semester, you will need to do the 8-week schedule. If you’d also like to take Data Processing in the first semester, you will need to do the 4-week schedule, in order to leave enough time!
There are multiple opportunities to take the exam.
The following dates are reserved for exams:
- Thu 11 Mar 2021
- Fri 26 Mar 2021
- Wed 19 May 2021
- Thu 20 May 2021
You can use the link below to book a time slot for the exam. Only book a slot when you’ve finished all the required modules of a course!
If none of the available slots is convenient for you, please send us an email: firstname.lastname@example.org.
It can happen that your exam is a couple of weeks after you finished the last assignment. Please, feel free to already make start with the next course (Scientific Programming 2)! Do send an email to let us know.
Programming is like writing. You can gradually learn to write programs that are more beautiful, functional, short, elegant or simple. To learn this, you’ll need some feedback, and it’s mostly up to you to get it. You can show your programs in class to fellow students or your teacher; you can post a fragment of your code on Ed and ask for advice on improving; or you can send the staff an e-mail and we’ll have a look (this might take a while though!).
No books are required for this course. Embedded in some of the modules are parts of the book Think Python by Allen Downey. If you’d like, you can read the remainder of the book on its website.
Doing your own work
This course’s philosophy on academic honesty is best stated as “be reasonable.” The course recognizes that interactions with classmates and others can facilitate mastery of the course’s material. However, there remains a line between enlisting the help of another and submitting the work of another. This policy characterizes both sides of that line.
The essence of all work that you submit to this course must be your own. Collaboration on problem sets is not permitted except to the extent that you may ask classmates and others for help so long as that help does not reduce to another doing your work for you. Generally speaking, when asking for help, you may show your code to others, but you may not view theirs, so long as you and they respect this policy’s other constraints. Collaboration on the course’s test and quiz is not permitted at all.
Below are rules of thumb that (inexhaustively) characterize acts that the course considers reasonable and not reasonable. If in doubt as to whether some act is reasonable, do not commit it until you solicit and receive approval in writing from the course’s heads. Acts considered not reasonable by the course are handled harshly.
Communicating with classmates about problem sets’ problems in English (or some other spoken language).
Discussing the course’s material with others in order to understand it better.
Helping a classmate identify a bug in his or her code at office hours, elsewhere, or even online, as by viewing, compiling, or running his or her code, even on your own computer.
Incorporating a few lines of code that you find online or elsewhere into your own code, provided that those lines are not themselves solutions to assigned problems and that you cite the lines’ origins.
Reviewing past semesters’ quizzes and solutions thereto.
Sending or showing code that you’ve written to someone, possibly a classmate, so that he or she might help you identify and fix a bug.
Sharing a few lines of your own code online so that others might help you identify and fix a bug.
Turning to the course’s heads for help or receiving help from the course’s heads during the quiz or test.
Turning to the web or elsewhere for instruction beyond the course’s own, for references, and for solutions to technical difficulties, but not for outright solutions to problem set’s problems or your own final project.
Whiteboarding solutions to problem sets with others using diagrams or pseudocode but not actual code.
Working with (and even paying) a tutor to help you with the course, provided the tutor does not do your work for you.
Accessing a solution to some problem prior to (re-)submitting your own.
Asking a classmate to see his or her solution to a problem set’s problem before (re-)submitting your own.
Decompiling, de-obfuscating, or disassembling the staff’s solutions to problem sets.
Failing to cite (as with comments) the origins of code or techniques that you discover outside of the course’s own lessons and integrate into your own work, even while respecting this policy’s other constraints.
Giving or showing to a classmate a solution to a problem set’s problem when it is he or she, and not you, who is struggling to solve it.
Looking at another individual’s work during the test or quiz.
Paying or offering to pay an individual for work that you may submit as (part of) your own.
Providing or making available solutions to problem sets to individuals who might take this course in the future.
Searching for or soliciting outright solutions to problem sets online or elsewhere.
Splitting a problem set’s workload with another individual and combining your work.
Submitting (after possibly modifying) the work of another individual beyond the few lines allowed herein.
Submitting the same or similar work to this course that you have submitted or will submit to another.
Submitting work to this course that you intend to use outside of the course (e.g., for a job) without prior approval from the course’s heads.
Turning to humans (besides the course’s heads) for help or receiving help from humans (besides the course’s heads) during the quiz or test.
Viewing another’s solution to a problem set’s problem and basing your own solution on it.
This course has been designed by Martijn Stegeman and Ivo van Vulpen.
It is partially based on many great programming resources that have been published as Open Courseware under a Creative Commons license. The resulting work itself is also published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Feel free to re-use! If you would like to use the work commercially, please send an e-mail for arranging a license.
We have had lots of help from students as well as teaching assistants who tried the course or added ideas of their own. We especially thank:
- Jelle van Assema (assignments and checkpy)
- Marianne de Heer Kloots (revisions and testing)
- Maarten Inja (DNA assignment)
- Simon Pauw (revisions)
- Quinten Post (translations)
- Marleen Rijksen (revisions)
- Huub Rutjes (films)
- Vera Schild (test automation)
- Luca Verhees (artwork “semester of code”)
We have used materials from the following sources:
- 6.189 A Gentle Introduction to Programming Using Python by Sarina Canelake at MIT http://ocw.mit.edu
- 6.00 Introduction to Computer Science and Programming, Fall 2008 by Eric Grimson and John Guttag at MIT http://ocw.mit.edu
- CS50 Introduction to Computer Science I by David Malan at Harvard http://cs50.tv/
- 6.0001 Introduction to Computer Science and Programming in Python by Ana Bell, Eric Grimson and John Guttag at MIT http://ocw.mit.edu
- Think Python by Allen B. Downey http://greenteapress.com/wp/think-python/
Scientific Programming 1
This repository contains the website contents for Scientific Programming 1. Scientific Programming 1 helps you learn the very basics of Python and provides a number of interesting case studies from various fields of research.