most recent course or lecture is listed first
| Course | Lecture hours per week | Description |
|---|---|---|
| WINTER SEMESTER 2024/25 | ||
| Introduction to Data Science and Data Engineering (Lecture) | 4 | The lecture initially covers the fundamentals of computer architecture and in-depth concepts of programming in C, including data types, pointer arithmetic, memory management, and the use of the struct data type. Subsequently, it delves into Python basics, encompassing data types, control structures, memory management, and performance comparisons with C. The course concludes with an introduction to advanced Python concepts such as NumPy operations and provides an overview of machine learning with scikit-learn. |
| Exercises for Introduction to Data Science and Data Engineering | 2 | Practical exercises related to the above lecture |
| Introduction to Algorithmic Mathematics and Computer Science (Lecture) | 4 | The lecture brings together topics from both mathematics and computer science. It begins with an exploration of computer architecture basics and detailed C programming concepts, touching upon topics like data types, pointer operations, memory handling, and utilization of the struct data type. Following this, it moves on to foundational Python elements, covering data types, control flows, memory management, and performance contrasts with C. The course wraps up by introducing advanced Python features, including NumPy functionalities, and offers a glimpse into machine learning using scikit-learn. |
| Exercises for Introduction to Algorithmic Mathematics and Computer Science | 2 | Practical exercises related to the above lecture |
| SUMMER SEMESTER 2024 | ||
| Bioinformatic Analyses (Lecture) | 2 | The lecture series offers a comprehensive journey through molecular biology and bioinformatics. It covers fundamental molecular biology concepts, bioinformatic analyses, and advanced topics like short read data alignment and variant calling. Special emphasis is given to the ICGC MMMLseq Project focusing on molecular mechanisms in malignant lymphomas, along with discussions on cancer drivers, tumor evolution, and statistical methods. Additionally, students are introduced to enrichment analysis and data visualization techniques, supported by detailed slide presentations for each topic. |
| Adaptation Course Programming in Java | 1.5 | The course covers the imperative basics of Java. |
| Course Programming in Java | 3 | This course deepens programming skills from Informatics 2 through team projects on advanced Java topics like threads, networking, testing, and optimization. |
| WINTER SEMESTER 2023/24 | ||
| Introduction to Data Science and Data Engineering (Lecture) | 4 | The lecture initially covers the fundamentals of computer architecture and in-depth concepts of programming in C, including data types, pointer arithmetic, memory management, and the use of the struct data type. Subsequently, it delves into Python basics, encompassing data types, control structures, memory management, and performance comparisons with C. The course concludes with an introduction to advanced Python concepts such as NumPy operations and provides an overview of machine learning with scikit-learn. |
| Exercises for Introduction to Data Science and Data Engineering | 2 | Practical exercises related to the above lecture |
| Introduction to Algorithmic Mathematics and Computer Science (Lecture) | 4 | The lecture brings together topics from both mathematics and computer science. It begins with an exploration of computer architecture basics and detailed C programming concepts, touching upon topics like data types, pointer operations, memory handling, and utilization of the struct data type. Following this, it moves on to foundational Python elements, covering data types, control flows, memory management, and performance contrasts with C. The course wraps up by introducing advanced Python features, including NumPy functionalities, and offers a glimpse into machine learning using scikit-learn. |
| Exercises for Introduction to Algorithmic Mathematics and Computer Science | 2 | Practical exercises related to the above lecture |