Knowledge of algorithms and programming is cornerstone for the study of data science and analytics, and fundamental for general computer applications in sciences and engineering. The course covers the fundamental concepts of algorithms, data types, control structures, file handling, classes and methods. These algorithmic tools are bridged to Python programming concepts: data types and structures, processing control flow, functions definition, file I/O, and classes and methods for object-oriented programming.
No prior knowledge at the university level is required.
The main goal of this course is studying the foundations of programming and best-practice implementations in the general-purpose Python language. The specific objectives are providing Students with the following capabilities
• Propose algorithmic solutions to basic problems in Data Science, Statistics and fundamental Mathematics.
• Construct programs appropriately using Python data structures, flow control and managing file primitives, as well as modularization units.
• Understand the foundations of object-oriented programming and use Python classes for solving problems in the aforementioned application fields.
1) Number systems, stored-program computer model & number representation.
2) Elementary data types, operations, precedences & type conversion.
3) Sequential and conditional control structures: Boolean or logical operations. Conditional or selection control structures (simple, multiple & nested).
4) Iteration control structure: for loops & while loops. Nested loops, break statement.
5) Arrays data structures & operations: Vectors & matrices. Element-wise and array-wise operations, element search and ordering methods.
6) Python data structures: Lists, Dictionaries, Sets & Tuples: Creation, operations & applications.
7) Functions in Python: Concept (reusable code), argument passing, examples, recursion.
The subject has two teaching sessions every week. The first session is lecture where theoretical concepts are introduced along with illustrative applications. The second session is practice-oriented, in which Students work in computer exercises to consolidate the subject. Every two or three sessions, individual or group evaluation activities are carried out by means of written questions or computer exercises, both based on questions or exercices stated during sessions or previously assigned as homework.
1) Practice evaluation activities 20 % (AI use level: 4)
2) Midterm (coding section + theory) 30% (AI use level: 1)
3) Group project (report + oral presentation) 25% (AI use level: 4)
4) Final (theory) 15% (AI use level: 1)
5) Class participation 10% (AI use level: 1)
Level of AI use: The following level description follows the guidelines adopted by La Salle URL and specified in the web link provided below.
Level 1: No AI.
The use of AI is not allowed in any phase of the task. The work must be carried out entirely without artificial assistance.
Level 4: AI to perform tasks, with human evaluation.
The use of AI is authorized to complete specific parts of the task. The focus is on critical analysis and human evaluation of the generated content.
https://estudy2526.salle.url.edu/pluginfile.php/5049/mod_page/content/468/Escala%20AIAS%20_eng.pdf
Learning Outcomes of this subject are:
R1. Understand core programming concepts required for code writing and debugging.
R2. Exploit Python capabilities for data creation, storage, extraction, processing and visualization.
R3. Develop practical skills for problem solving and teamwork in computer applications.
A recommended bibliography includes the following references:
• Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist (3rd ed.). O’Reilly Media.
• Python Software Foundation. (2026). The Python Tutorial. Python 3.14 documentation.
The professor may provide additional practice material.