| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Data Science | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | No | Numerical |  |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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A program is an algorithm that can be directly executed by a computer. Learning to program therefore encompasses two complementary skills: (1) constructing algorithms; (2) coding an algorithm as a program. This course focuses on both aspects. We will use the programming language Python. In particular, this course has the following goals:
- The student can write simple imperative programs in Python. In particular, he/she can utilize primitive types, strings, lists, iteration, conditions, procedures and functions.
- The student understands the importance of precise syntax and semantics.
- The student is able to reason about programs and can debug programs.
- The student is familiar with the notion of an algorithm, can devise algorithms (for simple problems), and can reason over algorithms.
- The student is familiar with the principles of computational thinking and can apply these.
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Lecture ✔
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Self-study assignment ✔
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Small group session ✔
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Period 1 Credits 5,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 30 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The permanent evaluation (30% of end score) can not be redone. |
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| Compulsory textbooks (bookshop) |
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Intro to Python for computer science and data science,Paul Deitel and Harvey Deitel,first edition,Pearson,9780135404676 |
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| 1st year Master Bioinformatics | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Bioinformatics - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
A program is an algorithm that can be directly executed by a computer. Learning to program therefore encompasses two complementary skills: (1) constructing algorithms; (2) coding an algorithm as a program. This course focuses on both aspects. We will use the programming language Python. In particular, this course has the following goals:
- The student can write simple imperative programs in Python. In particular, he/she can utilize primitive types, strings, lists, iteration, conditions, procedures and functions.
- The student understands the importance of precise syntax and semantics.
- The student is able to reason about programs and can debug programs.
- The student is familiar with the notion of an algorithm, can devise algorithms (for simple problems), and can reason over algorithms.
- The student is familiar with the principles of computational thinking and can apply these.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 30 % |
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The permanent evaluation (30% of end score) can not be redone. |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Intro to Python for computer science and data science,Paul Deitel and Harvey Deitel,first edition,Pearson,9780135404676 |
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|
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| 1st year Master Biostatistics | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Quantitative Epidemiology | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
A program is an algorithm that can be directly executed by a computer. Learning to program therefore encompasses two complementary skills: (1) constructing algorithms; (2) coding an algorithm as a program. This course focuses on both aspects. We will use the programming language Python. In particular, this course has the following goals:
- The student can write simple imperative programs in Python. In particular, he/she can utilize primitive types, strings, lists, iteration, conditions, procedures and functions.
- The student understands the importance of precise syntax and semantics.
- The student is able to reason about programs and can debug programs.
- The student is familiar with the notion of an algorithm, can devise algorithms (for simple problems), and can reason over algorithms.
- The student is familiar with the principles of computational thinking and can apply these.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 30 % |
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The permanent evaluation (30% of end score) can not be redone. |
|
|
|
|
|
| Compulsory textbooks (bookshop) |
| |
Intro to Python for computer science and data science,Paul Deitel and Harvey Deitel,first edition,Pearson,9780135404676 |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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