Vorlesungsverzeichnis
Suchen Sie hier über ein Suchformular im Vorlesungsverzeichnis der Leuphana.
Veranstaltungen von Dr. Harry Rana
Lehrveranstaltungen
How Automobile Works: Past, Present and Future (FSL) (SBP) (Seminar)
Dozent/in: Harry Rana
Termin:
14-täglich | Montag | 12:15 - 15:45 | 13.10.2025 - 26.01.2026 | C 12.108 Seminarraum
Inhalt: 1. The Past: Evolution of Automobiles In this module, we will explore the origins of automobiles and trace their development through key historical milestones: • Early Innovations: From steam-powered vehicles to the Benz Patent-Motorwagen, the world's first automobile. • Mass Production Revolution: The impact of Henry Ford's assembly line and the Model T on making cars accessible to the masses. • Technological Advancements: Innovations in engine technology, chassis design, and safety features during the early to mid-20th century. 2. The Present: Modern Automotive Technology This module focuses on the current state of automotive technology and the innovations that define modern vehicles: • Engine and Powertrain Systems: Evolution from internal combustion engines to hybrid and electric powertrains. • Safety and Driver Assistance Systems: Advances in safety technologies such as ABS, airbags, collision warning systems, and adaptive cruise control. • Connectivity and Infotainment: Integration of GPS navigation, smartphone connectivity, and entertainment systems. 3. The Future: Emerging Trends and Technologies Looking ahead, this module explores the cutting-edge innovations and trends that are shaping the future of automobiles: • Electric Vehicles (EVs): The rise of EVs, advancements in battery technology, and infrastructure development. • Autonomous Driving: Development of self-driving cars, AI-powered navigation systems, and regulatory challenges. • Sustainability and Environmental Impact: Efforts towards reducing carbon emissions, sustainable materials, and smart urban mobility solutions. • Innovative Designs: Concepts like flying cars, urban mobility solutions, and the integration of AI in vehicles. 4. Practical Applications and Case Studies In this hands-on module, participants will engage in practical demonstrations and case studies to deepen their understanding: • Hands-on Workshops: Explore the basic mechanics of automobiles, including engine components, drivetrain systems, and vehicle diagnostics. • Case Studies: Analyse real-world examples of automotive innovations and their impact on industry trends. • Project Work: Collaborate on projects that propose solutions to current challenges in automotive engineering and design. 5. Conclusion: Looking Ahead • Reflection and Discussion: Review the transformative journey of automobiles and the societal impacts of technological advancements. • Ethical Considerations: Address ethical dilemmas in autonomous driving, data privacy, and sustainability. • Career Pathways: Explore career opportunities in automotive engineering, design, research, and entrepreneurship.
How People Work in Industrial Environment: Decoding Behavioural Patterns (FSL) (SBP) (Vorlesung)
Dozent/in: Harry Rana
Termin:
14-täglich | Montag | 12:15 - 15:45 | 20.10.2025 - 02.02.2026 | C 12.112 Seminarraum
Inhalt: Understanding people’s behavioural patterns is paramount for young graduates entering the realm of industrial employment. To ensure their success in distinct industrial roles and overall professional growth, person must prioritize expanding their knowledge and competencies in decoding such knowledge. This aspect of their training and development is crucial for them to effectively navigate the complexities of managing teams and working within organizational structures. In this ever-evolving industrial era, the role of young graduates is undergoing a rapid transformation. They are now expected to act as directors responsible for implementing new technology and practices. Consequently, they often find themselves at the forefront of organizational change, making it essential for them to possess a deep understanding of organizational processes, group behaviour, and organizational structure. This knowledge and skill set are crucial for graduates to effectively navigate the complexities of managing teams and driving transformation within their organizations.
DATAx: Data analysis with Python (3) (Übung)
Dozent/in: Harry Rana
Termin:
wöchentlich | Freitag | 16:00 - 16:55 | 13.10.2025 - 30.01.2026 | C 16.203 Seminarraum
Inhalt: This exercise introduces programming and data analysis using the Python programming language. It is specifically designed for students with no prior programming knowledge or experience. During the course, students will learn: - Basic programming concepts, such as variables, conditions, loops and functions. - Effective use of large language models (LLMs) in chat AI. - The essential steps for performing data analysis with Python. With the help of ready-made Jupyter notebooks, instructors from different disciplines supervise students' first practical experience of using Python in Jupyter notebooks, including sessions on data analysis and machine learning. Regular assignments encourage students to gain hands-on experience in programming and data analysis, and to apply their newfound knowledge to their field of study. By the end of the semester, students will work in a study group on a data-driven project, taking on different roles and learning to extract and present insights from real data together. Throughout the exercise, experienced and dedicated tutors (Teaching Assistants) are available to support students on campus. The languages of instruction in the tutorials are German and English.
DATAx: Data analysis with Python (4) (Übung)
Dozent/in: Harry Rana
Termin:
wöchentlich | Freitag | 17:00 - 17:55 | 13.10.2025 - 30.01.2026 | C 16.203 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.