Ateneo - UPO

The course offers a basic introduction to Computer Science and Data Science applied to the biomedical field. The syllabus guides students through fundamental concepts of how data is represented and processed by hardware, as well as how operating systems and networks facilitate information management and exchange. By gaining an initial understanding of programming principles and algorithmic logic, students will learn to efficiently process and interpret biomedical data. Python is introduced as the programming language and will be used to perform essential operations for managing, cleaning, and visualizing data from experimental or observational studies.

Lecture materials (slides, exercises, sample datasets).

The mission of this course is to provide students with the foundational knowledge and technical skills required to navigate the intersection of computer science and biomedical research. Students will explore the core pillars of computing—including hardware architecture, operating systems, and networking—to understand how these systems support modern biological data management. A central objective is to introduce the fundamental principles of Data Science through the use of Python and Interactive Notebooks, that will be executed in the Google Colab environment. The course aims to foster a practical ability to retrieve, represent, and process data, while emphasizing best practices for reporting and visualizing scientific findings in a clear and reproducible manner.

This course is designed for beginners and assumes no prior background in programming or computer science. Students should possess basic computer literacy (managing files and using a web browser) and a comfortable grasp of high-school level algebra and statistics to understand data trends. A general familiarity with biomedical concepts is helpful, as all examples and projects will be grounded in biological data contexts.

Lectures, Classroom exercises

The Course material is available in the Moodle platform. Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The final evaluation will be conducted jointly with the Data Management module. The exam assesses competencies through a mandatory written exam and an optional project submission. The written exam takes place on the scheduled exam date and consists of a quiz with questions of varying difficulty corresponding to different point values, for a total maximum score of 28. Top marks can be achieved if, after passing the written exam with a score of at least 26, students inform the instructors of their intention to also complete a project in Python or R (student's choice).

18–20 points guarantees a passing grade and demonstrates a minimal understanding of data management theory and computer science.
21–23 points demonstrates intermediate proficiency in the principles of data management and computer science.
24–26 points demonstrates the ability to apply principles of data management, computer science, and programming.
27–28 points demonstrates the ability to apply data management, computer science, and programming principles in an optimized manner.
29–30 points is reserved for students who, having passed the written exam with a minimum score of 26, wish to develop a comprehensive project in Python or R to be agreed upon with the instructors. The project includes the analysis of a dataset assigned by the instructors.
Honors (30 e Lode) is reserved for those who, in addition to achieving top scores on both the written exam and the project, present an impeccable project that demonstrates complete independence in the development and analysis of the provided data.

1. Computer Architecture & Data Representation 1a. The Hardware Layer: How the CPU, RAM, and Storage interact during data processing. 1b. Binary & Logic: How biological information (like DNA sequences) is translated into 0s and 1s. 1c. Memory Management: The difference between volatile memory (RAM) and permanent storage (SSD/Cloud). 2. Systems & Networking 2a. Operating Systems (OS): The role of the kernel and file systems in organizing research data. 2b. The Internet & APIs: How "The Cloud" works. Understanding how to request data from remote biomedical servers (like NCBI) using the HTTP protocol. 2c. Virtual Environments: Introduction to Jupyter Notebooks and Google Colab as research workstations. 3: Programming Logic with Python 3a. Foundations: Variables, data types (Integers, Floats, Strings), and basic arithmetic. 3b. Control Flow: Using if statements for decision-making and for loops to process lists of patient records.3c. Functional Thinking: Writing basic functions to make code reusable across different datasets. 4. Data Manipulation & Matrix Analysis 4a. NumPy: Understanding "Vectorization"—why math is faster when done on entire arrays at once. 4b. Pandas: Learning the "DataFrame" structure. How to load, filter, and group tabular clinical data. 4c. Data Cleaning: Handling missing values (NaN) and correcting sensor errors in experimental results. 5. Data Science & Visualization 5a. Exploratory Data Analysis: Using .describe() and .groupby() to find trends in biological populations. 5b. Visualization with Seaborn/Matplotlib: Creating histograms, scatter plots, and boxplots. 5c. Reproducible Reporting: Best practices for documenting code and results so other scientists can verify the findings.

Knowledge and Understanding: Understand the fundamental principles of data representation in computer hardware and the logical foundations for transforming raw biomedical variables into structured formats. Applying Knowledge and Understanding: Perform essential data cleaning and transformations on biomedical datasets using Python, including the ability to filter experimental errors and calculate basic statistical aggregates. Making Judgements: Evaluate the quality of biological data by identifying common issues like outliers or missing values, and select the appropriate standard procedures for handling them. Communication Skills: Clearly present analytical findings through appropriate scientific visualizations (such as bar charts and scatter plots) and document analytical steps in Jupyter Notebooks to ensure the study is reproducible. Learning Skills: Develop initial autonomy in navigating modern digital environments, such as Python libraries and cloud-based platforms, for the management of biomedical databases.