LEARNING OUTCOMES

The course is intended to refresh fundamental concepts in programming, data structures and algorithms using the Python language and introduce students to the use of the numpy library.

AIMS AND LEARNING OUTCOMES

The student should acquire the skills to create simple applications using the Python language, the ability to empirically evaluate their performance and to choose the best data structures, among those available in the language, to carry out certain tasks. In particular, the student should be able to

• use the online documentation on Python and some of its libraries

• analyze pieces of programs (written in Python) and understand how they work

• modify, run and test small programs

• starting from an informal specification of a problem, design and write (parts of) programs that solve the problem

TEACHING METHODS

Teaching takes place over 12 weeks, each week normally consists of 4 hours of classroom lessons (lessons may be recorded on MS Teams, for the benefit of foreign students abroad) and at most 1 weekly home assignment related to the topics covered so far in the course.

It is assumed that students have a laptop with anaconda3 installed. Assistance to install anaconda3 and to use Jupyter Notebook will be provided at the start of the course.

Home assignments can be done in group (of at most 3 students) and must be uploaded on aulaweb at specified deadlines.

SYLLABUS/CONTENT

• Language constructs: expressions, assignments, conditionals, iteration, functions, recursion, exceptions, list comprehension.

• Data structures: lists/sequences, dictionaries, sets

• Algorithmic techniques: divide-and-conquer, list processing, memoization, staging

• Numpy library: arrays,linear algebra operations

RECOMMENDED READING/BIBLIOGRAPHY

• https://docs.python.org/3/ online documentation

• https://greenteapress.com/wp/think-python-2e/ free book (pdf/html)

Further material (including video recordings) will be made available on aulaweb.