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DAT171 Object-oriented programming in Python lp3 VT25 (7.5 hp)

Course is offered by the Department of Electrical Engineering

All course communication is in English.

The course is delivered on campus, but recorded lectures are posted in Chalmers Play (up to two business days later).

This page was last updated 2025-01-30.

Contact details

Examiner: Paolo Monti, mpaolo@chalmers.se, tel. 772 60 27
Lecturer and course responsible: Carlos Natalino, carlos.natalino@chalmers.se, 772 60 26 - prefer message on canvas - github.com/carlosnatalino

Teaching assistants:

  • Sai Vikranth Pendem
    • https://github.com/saivikranthp
  • Bingcheng Chen
    • https://github.com/chenbin234

Student representatives:

  • Vinay Mallikarjun Hiremath (MPMOB)
    • vinayh@student.chalmers.se
  • Ludvig Johansson (TKMAS)
    • ludjohan@student.chalmers.se
  • Albin Larsson (TKMAS)
    • alblars@student.chalmers.se
  • Josef Lundin (TKMAS)
    • joseflu@student.chalmers.se
  • Albin Wessman (TKMAS)
    • albinwes@student.chalmers.se

Schedule

Always check TimeEdit for updated schedule.

Backup PDF file (as of 2025-01-17): TimeEdit_DAT171_Object-oriented_programming_in_Python_2025-01-17.pdf

Consultation

Carlos is the main responsible for interacting with students. Questions are primarily answered at computer sessions and lectures. If you have questions, the easiest way to ask is through the discussion forum. You are also welcome to contact the lecturer for questions. Messages in canvas are preferred.

Questions about this syllabus can be sent here: Questions about the course syllabus

Course purpose

The aim of the course is to provide the students with good opportunities to develop a fundamental understanding of object-oriented programming, particular in Python and how to use standard libraries for development of graphical user interfaces, numerical computations and plotting.

Details about the course can be found here: https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/DAT171/?acYear=2024%2F2025

Course literature

[1] C. Horstmann: Python for everyone 3rd ed., ISBN: 978-1-119-63829-2 (optional)
[2] Python tutorial: https://docs.python.org/3.13/tutorial/
[3] Python 3 course: http://www.python-course.eu/python3_course.php
[4] NumPy & SciPy references: http://docs.scipy.org/doc/
[5] Matplotlib reference: https://matplotlib.org/stable/users/index.html
[6] PyQt 6 reference: https://zetcode.com/pyqt6/ and https://doc.qt.io/qt-6/
[7] NumPy for Matlab users: https://numpy.org/doc/stable/user/numpy-for-matlab-users.html
[8] Pandas: https://pandas.pydata.org/docs/
[9] Lecture notes: https://onu1.s2.chalmers.se/notes/ 
[10] Test-Driven Development with Python, by Harry Percival, 2nd Edition, 2017, ISBN: 978-1491958704. (optional) You can read it for free on the book website: https://www.obeythetestinggoat.com/ 

All references to chapters and sections in the course material will also refer to free online material as well for those who choose not to buy the book.

Software

We recommend you use your own computer throughout the course. The installation instructions are available here: Software installation instructions and configurations

We use the following tools:

Installation support for personal laptops are held on the first computer labs.

Course specific prerequisites

Good working knowledge of basic programming structures such as variables, conditionals, loops and functions in any programming language (for example Matlab).
Students who feel they lack sufficient knowledge in programming can have a look at https://www.learnpython.org (at least the “Learn the Basics” part).

Learning outcome

After successfully passing the course, the student should be able to independently write object oriented software using Python. Furthermore, the student should be able to read reference literature for the Python programming language as well as being able to use the SciPy/NumPy package for numerical computations and PyQt for writing graphical user interfaces.

Course design

The course is given in the form of lectures and tutored hand-in assignments.

Time budget

About 200 work hours per student, where 32 hours are spent on lectures, 48 hours of tutored computer sessions. The assignments are worth 1 hp each and should represent ~26 hours of work per student.

Content

  • Basic building blocks of a Python program (variables, conditional statements, loops, libraries, functions, errors)
  • Data structures (trees, dictionaries, tuples)
  • Object Oriented programming (classes, objects, inheritance, polymorphism, abstract classes)
  • PyQt for creating graphical user interfaces for interactive programs
  • NumPy (Matrices, vectors, linear algebra)
  • SciPy (Package for numerical computations)
  • Matplotlib (Plotting)
  • Interactive Python (IPython)
  • Interact with databases

Examination

Examination form: Examination is in the form an exam where you write solutions (code) to given tasks on provided computers. These solutions are graded.

To pass on the course, the three compulsory computer assignments must have been completed and approved.

The exam will not contain anything on the GUI (5 hours would not be sufficient).

Examination dates: Check the course page for the 2024/2025 academic year: https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/DAT171/?acYear=2024%2F2025

Note! Make sure to register for the exam!

Internet access will be prevented at the exam, but the PDF version of the Python 3- , SciPy-, NumPy- and Matplotlib manuals as well as the Lecture Notes will be available on the computers. You are also allowed to bring the course book to the exam.

The exam will consist of 4-5 questions and will be determining the final grade of the course.

Maximum total is 25 points. For a passing grade 10 points is required, for grade four 15 points is required, and for grade five 20 points is required. The examination time is 5 hours.

Old exams will be available on the course homepage.

Course evaluation

The course will be evaluated at 2 occurrences throughout the course. In addition, there will be a course survey for all students at the end of the course. The course board representatives are listed in the contact details section of this syllabus.

Assignments

There will be 3 compulsory assignments, which covers most of the content of the course. The assignments will be performed in groups of two students.

In addition, there will be practice problems available that are not compulsory, but come highly recommended.

Please note dates for handing in final corrections of the assignments. 

Assignment 1:

  • Getting familiar with Python
  • Reading files
  • Using NumPy for numerics
  • Using more advanced data structures
  • Matplotlib

Assignment 2:

  • Creating a library
  • Creating classes

Assignment 3:

  • Interactive programs
  • Creating a GUI with PyQt
  • Interaction with databases

Knowledge checklist

Before the exam, please go through the checklist to know you have mastered the components of the course!

This list tries to be as detailed as possible.

  • Python language
    • Built in documentation: help(...)
    • Variables
    • pass
    • Conditionals: if elif else
    • Looping: for, while, continue, break
    • with as
    • Boolean operators: and, or, in, is, not, ==, !=, <>, <, >, >=, <=, 
    • Bit operators: >>, <<, ~, ^, |, &
    • Arithmetic operators:  +, -, *, /, %, **, //
    • Assignment operators: =, +=, -=, *=, /=, %=, **=, //=
    • Functions
    • Lambda functions / anonymous functions
    • List/set/dictionary comprehensions
    • Exceptions: try, raise, except, finally 
    • Libraries
    • Common errors
    • Documentation with PyDoc
    • *args (e.g., foo(*x) ➔ foo(x[0], x[1], x[2]) ) and **args
  • Common structures
    • Strings
    • Lists (indexing, slicing)
    • Tuples
    • Sets
    • Dictionaries, del
  • Object-oriented programming
    • Classes
    • Inheritance and polymorphism
    • Constructors and destructors
    • Abstract methods and decorators
    • Operator overloading
  • NumPy
    • Being able to look up what you need in the reference!
    • numpy.array
    • numpy.linalg
  • SciPy
    • Being able to look up what you need in the reference!
    • scipy.spatial
    • scipy.sparse.csgraph
  • Matplotlib
    • Being able to look up what you need in the reference!
    • Being able to create line and bar plots, change the axes titles, line style, markers

Changes made since the last occasion

A summary of changes made since the last occasion will appear here.

 

Course summary:

Date Details Due