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Programming in Python (for quantitative biologists)
Learning objectives and expected learning outcomes
The course introduces students to imperative programming by referring to the Python language.
The course is divided in two parts:
- Python and its object-oriented features;
- Python libraries that can be useful in scientific computation and data analysis, in particular NumPy and pandas.
Students will acquire the ability to write and tune a program that automatizes simple computational tasks; they will be able to understand how a small piece of Python code works, to find the reasons of a malfunction and to correct it appropriately. Moreover, students will be able to use the NumPy and pandas library to analyze tabular data.
A.A. 2023/24
The course will take place in the first semester. Currently, we expect to lecture on-premises, lectures will NOT be streamed. You can find some videos from last year course in the 2020/21 web page linked below.
The course will have a total of 40 hours of lessons (Tuesdays and Thursdays) and 16 hours of lab practice (Fridays). The exact schedule will be announced during the lessons (the first lesson is on October 3).
Syllabus
- The Python programming language.
- Native data types.
- Functions, selections and iterations.
- Basic data structures: lists, tuples, dictionaries.
- Object-oriented encapsulation.
- Iterators and generators.
- Files.
- Numpy multi-dimensional arrays and matrices.
- Data manipulation and analysis with pandas.
Any Python3 book can be used to support the learning of the general part, for example J. Hunt "A Beginners Guide to Python 3 Programming" (The electronic version is free for Unimi students). NumPy and pandas have excellent online documentation.
The examination is based on laboratory exercises. A final mark (on a 30 point scale) is given, by taking into account the knowledge of the subject and tools, and the clarity of the solutions.
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Programming in Python (for quantitative biologists)
- Learning objectives and expected learning outcomes
- A.A. 2023/24
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2023/24
- Exams
- 1. October 3, 10:30 Room Jommi
- 2. October 5, 8:30 Room 109
- 3. October 10, 10:30 Room Jommi
- 4. October 12, 8:30 Room 109
- 5. October 17, 10:30 Room Jommi
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October 19, 8:30 Room 109 - 6. October 24, 10:30 Room Jommi
- 7. October 26, 8:30 Room 109
- 8. October 27, 8:30 Lab Lambda
- 9. October 31, 10:30 Room Jommi
- 10. November 2, 8:30 Room 109
- 11. November 3, 8:30 Lab Lambda
- 12. November 7, 10:30 Room Jommi
- 13. November 9, 8:30 Room 109
- 14. November 10, 8:30 Lab Lambda
- 15. November 21, 10:30 Room Jommi
- 16. November 23, 8:30 Room 109
- 17. November 24, 8:30 Lab Lambda
- 18. November 28, 10:30 Room Jommi
- 19. November 30, 8:30 Room 109
- 20. December 1, 8:30 Lab Lambda
- 21. December 5, 10:30 Room Jommi
- 22. December 12, 10:30 Room Jommi
- 23. December 14, 8:30 Room 109
- 24. December 15, 8:30 Lab Lambda
- 25. December 19, 10:30 Room Jommi
- 26. December 21, 8:30 Room 109
- 27. January 9, 10:30 Lab 306
- 28. January 12, 8:30 Lab Lambda
- Previous years
2023/24
Exams
- September 10 results Exam text
- July 30 results Exam text
- July 16 results Exam text
- June 25 results Exam text
- February 20 results Exam text
- February 6 results Exam text
- January 23 results Exam text
1. October 3, 10:30 Room Jommi
2. October 5, 8:30 Room 109
3. October 10, 10:30 Room Jommi
4. October 12, 8:30 Room 109
5. October 17, 10:30 Room Jommi
October 19, 8:30 Room 109
6. October 24, 10:30 Room Jommi
7. October 26, 8:30 Room 109
8. October 27, 8:30 Lab Lambda
9. October 31, 10:30 Room Jommi
10. November 2, 8:30 Room 109
11. November 3, 8:30 Lab Lambda
12. November 7, 10:30 Room Jommi
13. November 9, 8:30 Room 109
14. November 10, 8:30 Lab Lambda
15. November 21, 10:30 Room Jommi
16. November 23, 8:30 Room 109
17. November 24, 8:30 Lab Lambda
18. November 28, 10:30 Room Jommi
19. November 30, 8:30 Room 109
20. December 1, 8:30 Lab Lambda
21. December 5, 10:30 Room Jommi
22. December 12, 10:30 Room Jommi
23. December 14, 8:30 Room 109
24. December 15, 8:30 Lab Lambda
25. December 19, 10:30 Room Jommi
26. December 21, 8:30 Room 109
- Slides Handouts for printing
- PyMC Howell's data example The exercises are already solved, but use them to tinker with pandas and pymc.