Course syllabus

Course PM

DAT555 Fundamentals of program development

LP1 HT26 (7,5 hp)

The course is offered by the department of Computer Science and Engineering

Contact details

  • Examiner & course responsible: John J. Camilleri <john.j.camilleri@chalmers.se>
  • Student representatives (all TKIEK):
    1. Oscar Lissel <oscar.lissel@gmail.com>
    2. Matilda Lönegren Mikulic <mlm@live.se>
    3. Emir Sabotic <sabotic.emir@hotmail.com>
    4. Sofia Salander <sofia.salander@icloud.com>
    5. Hugo Sjövall <hugo.sjovall@gmail.com>

Schedule

The course schedule is available on TimeEdit.

The table below shows a summary of all the dates for the lectures and lab deadlines:

Week Study Week Dates Lecture 1 Exercise Lecture 2 Deadline
36 1 31/08 – 04/09 L00: Intro, OS E01: Algorithmic thinking L01: Expressions, variables, loops Lab 0
37 2 07/09 – 11/09 L02: If statements, functions E02: Brute force L03: Numbers, arithmetic
38 3 14/09 – 18/09 L04: Strings, lists, tuples E03: Data structures L05: Dictionaries, files Self practice checkpoint
39 4 21/09 – 25/09 L06: Control flow, generators E04: Files L07: Validation, exceptions
40 5 28/09 – 02/10 L08: Object-oriented 1 E05: Classes L09: Object-oriented 2 Lab 1
41 6 05/10 – 09/10 L10: Graphics E06 L11: Matrices
42 7 12/10 – 16/10 L12: Recursion, complexity E07 L13: Exam prep Lab 2
43 8 19/10 – 23/10 L14: Revision E08 L15: Revision

The lecture topics, slides, etc. will appear as modules as the course progresses.

Course literature

John M. Zelle, "Python Programming: An Introduction to Computer Science".
Franklin, Beedle & Associates.
https://mcsp.wartburg.edu/zelle/python/

In this course we support both the 3rd and 4th edition of the book.
The book is also available as an e-book.

Learning outcomes

Knowledge and understanding

  • Grasp the relation between source code, the interpreter, and the machine.
  • Choose appropriate data types and data structures for different kinds of data, depending on their performance characteristics.
  • Design algorithms to solve simple programming problems.

Competence and skill

  • Structure small programs by the use of concepts such as iterations, functions, modules, classes, and methods.
  • Structure larger programs into manageable and reusable units.
  • Form readable, descriptive and well-documented program code.
  • Use programming for basic data analysis involving large textual or numeric files.
  • Express mathematical formulas as programming language expressions and algorithms.
  • Build basic interactive programs with text-based (and graphical) user interfaces.
  • Test programs, for instance using unit testing.
  • Use programming tools such as text editor, command line interface, and IDE (integrated development environment).
  • Use standard libraries and follow best programming practices.
  • Apply basic tools and methods supporting an inclusive cooperation in group work, including JML aspects.

Judgement and approach

  • Assess the difficulty and resources needed for typical programming tasks.

Content

The course is a first introduction to programming by using the general-purpose programming language Python. It gives a comprehensive knowledge of the language, enabling the student to write code for a wide variety of tasks and to read and reuse code written by other programmers.

  • Literals, types, variables, declarations, initialization, operators, expressions and statements, scope.
  • Control statements: if, while, for, break, continue, return try, raise.
  • Exceptions and exception handling.
  • functions, parameters, arguments, method calls, local variables.
  • Classes, objects, instance and class variables/methods.
  • Simple data structures (list, dictionary, set, stack).
  • One- and two-dimensional lists.
  • Input and output.
  • Introduction to graphical interfaces.
  • Overview of file handling.
  • Text handling, strings.

Organisation

The teaching consists of lectures, group work, exercises, as well as supervision in connection to the exercises.

Examination form

To pass the course it is necessary to complete:

  • Self practice exercises (individually) which will be reviewed by a supervisor in checkpoint.
  • Two obligatory labs (in groups of up to 4) which must be submitted before the deadline and approved by a supervisor. The grading can involve automatic testing, but supervisors also manually inspect the submissions for clarity, correctness and general quality.
  • A digital exam where you will have access to a Python interpreter. Permitted aids: one handwritten A4 sheet.

Grading

Self practice and labs are pass/fail. Exam and course grade is graded on the scale U/3/4/5.

Academic honesty & AI policy

  • Copying from someone else’s solution is considered cheating.
  • Communicating your solutions to other groups is forbidden.
  • Using AI tools (ChatGPT, etc.) to write code for you is forbidden throughout the entire course. Read more about why in our AI policy.

Official course syllabus

The official course syllabus can be found here.