6 EC
Semester 2, period 4, 5
55121HSP6Y
| Owner | IIS honoursprogramma |
| Coordinator | dr. Simon Pauw |
| Part of | Instituut voor Interdisciplinaire Studies (algemeen), honoursvakken, year 1IIS honoursprogramma, |
During this course, students will 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.
|
Activity |
Hours |
|
|
Hoorcollege |
2 |
|
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Tentamen digitaal |
2 |
|
|
Werkcollege |
32 |
|
|
Self study |
132 |
|
|
Total |
168 |
(6 EC x 28 uur) |
Additional requirements for this course:
Students may be absent 4 out of 16 seminars (werkcollege)
| Item and weight | Details |
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Final grade | |
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Tentamen digitaal |
The course consists of 5 modules that are grade pass/fail. The modules are:
1 ALGORITHMS. Learn to think like a computer. Things that we intuitively know how to do, like drawing a pyramid or computing change for a payment, is hard to get a computer to do right. In this module you’ll learn how to break down such intuitive problems into steps that even a computer can understand.
2 TEXT. Natural language processing is the science of making a computer understand (something about) natural human language. You will learn how you can get a computer to understand the sentiment of tweets. Is the tone of the tweet positive or negative?
3 BIG-DATA. In this module you will learn to work with data. You will, for example, analyze weather from the Netherlands and answer questions like: When was the first heat-wave? What was the longest freezing period?
4 MONOPOLY. When playing Monopoly, a starting player's advantage seems unfair. To verify, you could play many (millions) real games, but this would take way too much time. Instead, you'll write a computer simulation. This also allows you to experiment with game adjustments to make it fair. You're doing all this for a board game, but this simulation principle applies to various scientific fields (economy, chemistry, biology...).
5 SHAKESPEARE. What is an efficient algorithm? When you want to run large simulations, analyze large dataset, or any other computationally intensive task, writing efficient algorithms could in some cases mean the difference between a run time of a couple of minutes or of weeks. The theory of computational complexity gives you a way to reason about the efficiency of algorithms and make them run (much) faster.
Each module consists of a number of assignments. Some are marked as pair assignments and can be made collaboratively, others are individual assignments and have to be made without using (partial) solutions of other students. Most modules also contain challenge assignments, those are optional.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Week/Dates/assignments
| Week 8 | Feb 17 - 19 | 1.1 - 1.3 | 1.4 - 1.5 |
| Week 9 | Feb 24 - 26 | 1.5 - 1.6 | 1.7 |
| Week 11 | Mar 10 - 12 | 2.1 - 2.2 | 2.3 - 2.4 |
| Week 12 | Mar 17 - 19 | 2.5 - 2.6 | 2.7 |
| Week 14 | Mar 10 - 12 | 3.1 |
| Week 15 | Mar 31 - Apr 2 | 3.2, 3.3 | 4.1 - 4.2 |
| Week 16 | Apr 14 - 16 | 4.3 | 4.4. |
| Week 19 | May 5 - 7 | | 5.1 - 5.2 |
| Week 20 | May 12 - 14 | 5.3 | 5.4 |
| Week 21 | May 19 - 21 | Exam! |
Course website:
In order to provide students some insight how we use the feedback of student feedback to enhance the quality of education, we decided to include the table below in all course guides.
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