Principles Of Computer Science 2

Second Year / First Semester Course L-2 - 1049261

(A. A. 2023/2024)


The course aim to introduce computational thinking and the algorithmic approach to solving problems correctly and efficiently. Algorithms are ubiquitous in bioinformatics and are often at the interface of computer science and biology. Well established algorithmic techniques will be studied as well as ways to encode them in a computer program using python.

We will introduce the algorithmic approach and the theory of algorithms for studying correctness and efficiency, understanding what makes a good algorithm and how to classify them.

We will study characteristic algorithmic techniques and the relate-d computational ideas that are relevant to the field of biology and how to select the most suitable to solve a given task. Topics covered include

  • Searching algorithms
  • Divide-and-Conquer algorithms
  • Clustering and Tree-based algorithms

We will work with Python and how to write a computer program encoding a given algorithm. We will work with Amazon's AWS and how to use cloud resources to efficiently execute our python programs on large datasets.


All classes take place in Classroom B, Ortopedia (CU016, E01PR1L00)

Time Schedule

  • Monday 16:00 - 19:00
  • Thursday 14:00 - 16:00

Contact & Discussions

All announcements and discussions will be carried out through Google Classroom viel2n4


A total of five assignments will be handed over. These assignments are done by each student individually. Clearly you should discuss with other students of the course about the assignments. However, you must understand well your solutions and the final writeup must be yours and written in isolation. In addition, even though you may discuss about how you could implement an algorithm, what type of libraries to use, and so on, the final code must be yours. You may also consult the internet for information, as long as it does not reveal the solution. If a question asks you to design and implement an algorithm for a problem, it's fine if you find information about how to resolve a problem with character encoding, for example, but it is not fine if you search for the code or the algorithm for the problem you are being asked. For the projects, you can talk with other students of the course about questions on the programming language, libraries, some API issue, and so on, but both the solutions and the programming must be yours. If we find out that you have violated the policy and you have copied in any way you will automatically fail. If you have any doubts about whether something is allowed or not, ask the instructor.

Lecture Material

Coding Material

The material related to python that was presented in class is available from as an open-source repository in GitHub.


  1. NEIL C. JONES AND PAVEL A. PEVZNER: An Introduction to Bioinformatics Algorithms. A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England, 2004.
  2. JOHN M. ZELLE: Python Programming: An Introduction to Computer Science (Third Edition)
  3. Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek WilczyƄski: Biopython Tutorial and Cookbook

Previous Years