Vorlesungsverzeichnis

Suchen Sie hier über ein Suchformular im Vorlesungsverzeichnis der Leuphana.

Veranstaltungen von Vasily Pozdnyakov

Lehrveranstaltungen

DATAx: Data analysis with Python (43) (Übung)

Termin:
wöchentlich | Montag | 18:00 - 18:55 | 14.10.2024 - 31.01.2025 | C 12.105 Seminarraum

Inhalt: This course provides an introduction to programming and data analysis with Python. It is explicitly tailored for students with no prior knowledge or experience in programming. In the course, students learn (i) the essential steps for performing data analysis with Python, (ii) basic programming concepts such as variables, conditions, loops, and functions, and (iii) strategies for solving simple problems using algorithmic thinking. The course is organized as an exercise and follows a blended learning approach. It combines learning data analysis and programming skills in online exercises with self-study using Jupyter Notebooks. In addition, students are supported by experienced students from higher semesters (Teaching Assistants). Regular assignments encourage students to strive and gain hands-on experience in programming and data analysis, as well as to apply the acquired knowledge to their field of study. At the end of the semester, students will work in their study group on a data-driven project where they take on different roles and learn together to extract and present insights from real data.

DATAx: Data analysis with Python (44) (Übung)

Dozent/in: Vasily Pozdnyakov

Termin:
wöchentlich | Montag | 19:00 - 19:55 | 14.10.2024 - 31.01.2025 | C 12.105 Seminarraum

Inhalt: This course provides an introduction to programming and data analysis with Python. It is explicitly tailored for students with no prior knowledge or experience in programming. In the course, students learn (i) the essential steps for performing data analysis with Python, (ii) basic programming concepts such as variables, conditions, loops, and functions, and (iii) strategies for solving simple problems using algorithmic thinking. The course is organized as an exercise and follows a blended learning approach. It combines learning data analysis and programming skills in online exercises with self-study using Jupyter Notebooks. In addition, students are supported by experienced students from higher semesters (Teaching Assistants). Regular assignments encourage students to strive and gain hands-on experience in programming and data analysis, as well as to apply the acquired knowledge to their field of study. At the end of the semester, students will work in their study group on a data-driven project where they take on different roles and learn together to extract and present insights from real data.