Course Description
The course aims to introduce the fundamental concepts of modern data science, providing both a basic theoretical understanding and practical experience. Initially, it will emphasize the acquisition of basic theoretical skills through the combination of knowledge from algebra (vectors and matrices), calculus (functions minimization), and probability (distributions). The course will then progress to basic algorithms and analysis techniques such as machine learning, data pre-processing, and system development. In the latter part of the course, the focus will shift to applications in various fields, including time-series analysis, image understanding, text analysis, and graph modeling.
Each week, a single topic will be covered, with three hours dedicated to traditional lectures and one hour reserved for hands-on sessions. These sessions will not only provide an initial discussion of each week's topic but also involve students actively through follow-up assignments and subsequent report writing. To facilitate the practical aspect of the course, we will utilize freely available resources, particularly Python libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow. Additionally, we will leverage free computational resources available through platforms like Google Colab. This approach ensures that students have access to the necessary tools to successfully engage with the course material, fostering both their theoretical understanding and practical skills.
By the end of the course, students will be well-equipped with the knowledge and experience to apply basic data science principles and techniques in various scenarios, preparing them for advanced studies or careers in the field.
Goals
The proposed “Applied Data Science” course will provide an overview of the essential tools, techniques, and principles of data science. The course aims to introduce students to the subject of data analysis as it relates to computer science. This objective will be pursued by exposing students both to theoretical concepts of the subject and to the practical application of different methods in the analysis of factual data. The course is designed to equip 4th-year undergraduate students with a solid foundation in data science, preparing them for more advanced studies or professional work in the field. The curriculum includes a balanced mix of lectures and hands-on sessions to consolidate and apply the newly gained knowledge. By the end of the course, students will have a comprehensive understanding of data science fundamentals and be able to apply their knowledge in various practical contexts
ECTS
6
Prerequisites
CS-119, CS-150, CS-217
List of courses (from academic year 2024-25)
A+
A-
The courses of the Computer Science Department are designated with the letters "CS" followed by three decimal digits. The first digit denotes the year of study during which students are expected to enroll in the course.
First Digit
Advised Year of Enrollment
1,2,3,4
First, Second, Third and Fourth year
5,6
Graduate courses
7,8,9
Specialized topics
Code
Computer Science Area
A1
Computer architecture and microelectronics
A2
Computer systems, parallel and high performance computing
A3
Computer security and distributed systems
A4
Computer networks, mobile computing, and telecommunications
B1
Algorithms and systems analysis
B2
Databases, information and knowledge management
B3
Software engineering and programming languages
B4
Artificial Intelligence and machine learning
C1
Signal processing and analysis
C2
Computer vision and robotics
C3
Computer graphics and human-computer interaction
C4
Βioinformatics, medical informatics, and computational neuroscience
The following pages contain tables (one for each course category) summarizing courses offered by the undergraduate studies program of the Computer Science Department at the University of Crete. Courses with code-names beginning with "MATH" or "PHYS" are taught by the Mathematics Department and Physics Department respectively at the University of Crete.