700.320 (23W) Seminar in Big Data, Predictive Analytics and Automation
Überblick
- Lehrende/r
- LV-Titel englisch Seminar in Big Data, Predictive Analytics and Automation
- LV-Art Vorlesung-Seminar (prüfungsimmanente LV )
- LV-Modell Blended-Learning-Lehrveranstaltung
- Online-Anteil 20%
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 19 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 11.10.2023
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The Seminar in Big Data, Predictive Analytics, and Automation is designed to provide students with a comprehensive understanding of the concepts, tools, and techniques related to big data analysis, predictive analytics, and automation. The course explores the principles and applications of these topics in various domains, equipping students with the knowledge and skills necessary to leverage data-driven insights and automate decision-making processes.
Learning Objectives:
- Understand the fundamentals of big data, including its sources, characteristics, and challenges.
- Explore the concepts and methodologies of predictive analytics and its applications in diverse industries.
- Learn about the principles and technologies behind automation and its role in enhancing efficiency and productivity.
- (optional / partial) Gain practical experience in analyzing big data sets, applying predictive models, and designing automated processes.
- (optional) Develop critical thinking and problem-solving skills through real-world case studies and hands-on exercises.
Lehrmethodik inkl. Einsatz von eLearning-Tools
This seminar will employ a blended learning approach, combining both online and face-to-face learning activities. The course will be structured as follows:
Online Learning:
- Access to an online learning platform with course materials, resources, and interactive tools.
- Engage in self-paced learning through multimedia presentations, videos, and interactive modules.
- Participate in online discussions and collaborative activities with fellow students.
In-person Sessions:
- Interactive lectures and presentations delivered by subject matter experts.
- Group discussions and case study analysis to encourage active participation and critical thinking.
- Hands-on workshops and practical exercises to apply learned concepts and tools.
Practical Assignments:
- Completion of individual and group assignments that involve data analysis, predictive modeling, and automation design.
- Feedback and guidance provided by the instructor to enhance learning outcomes.
Inhalt/e
Core Topics for the Lecture:
Introduction to Big Data:
- Definition and characteristics of big data in the context of telecommunications systems and intelligent transportation systems.
- Sources and types of data generated in these domains.
- Challenges and opportunities of handling and analyzing big data.
Data Processing and Management:
- Data collection, storage, and retrieval techniques for large-scale datasets.
- Data preprocessing and cleaning to ensure data quality.
- Data integration and fusion from various sources in telecommunications and transportation systems.
Predictive Analytics:
- Fundamentals of predictive analytics and its role in telecommunications and transportation.
- Techniques for predictive modeling and data mining.
- Application of predictive analytics in areas such as network optimization, predictive maintenance, and demand forecasting.
Machine Learning for Intelligent Transportation Systems:
- Introduction to machine learning algorithms and techniques relevant to transportation systems.
- Applications of machine learning in traffic prediction, congestion management, and intelligent routing.
- Case studies and real-world examples showcasing the effectiveness of machine learning in transportation.
Automation in Telecommunications and Transportation:
- Overview of automation technologies and their impact on telecommunications and transportation systems.
- Automation in network management, resource allocation, and service provisioning.
- Intelligent automation for traffic control, autonomous vehicles, and smart infrastructure.
Case Studies and Best Practices:
- Examination of successful big data, predictive analytics, and automation implementations in telecommunications and transportation.
- Analysis of industry-specific challenges and lessons learned.
- Discussion on emerging trends and future directions in these domains.
Ethical and Privacy Considerations:
- Ethical implications of collecting, analyzing, and using big data in telecommunications and transportation.
- Privacy concerns and strategies for ensuring data security and protection.
- Compliance with relevant regulations and standards.
These core topics cover a broad range of subjects related to big data, predictive analytics, and automation, specifically focusing on their applications in telecommunications systems and intelligent transportation systems. They provide a comprehensive overview of the key concepts, techniques, and challenges relevant to these fields and offer insights into real-world examples and best practices.
Literatur
Selected sources:
Ahmed, Khaled R., and Aboul Ella Hassanien. Deep Learning and Big Data for Intelligent Transportation. Springer International Publishing, 2021.
Waller, Matthew A., and Stanley E. Fawcett. "Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management." Journal of Business Logistics 34.2 (2013): 77-84.
Peyré, Gabriel, and Marco Cuturi. "Computational optimal transport: With applications to data science." Foundations and Trends® in Machine Learning 11.5-6 (2019): 355-607.
Veres, Matthew, and Medhat Moussa. "Deep learning for intelligent transportation systems: A survey of emerging trends." IEEE Transactions on Intelligent transportation systems 21.8 (2019): 3152-3168.
Arzo, Sisay Tadesse, et al. "A theoretical discussion and survey of network automation for IoT: Challenges and opportunity." IEEE Internet of Things Journal 8.15 (2021): 12021-12045.
Link auf weitere Informationen
More source and information will be provided in the MOODLE coursePrüfungsinformationen
Prüfungsmethode/n
Assessment Methods:Students' understanding and progress will be assessed using various methods, including:
Individual and Group Assignments:
- Practical assignments and projects to evaluate the application of learned concepts.
- Group collaboration and problem-solving skills assessment.
Presentations:
- Individual or group presentations on selected topics related to big data, predictive analytics, or automation.
Examinations:
- Periodic quizzes or exams to assess comprehension of course material.
Participation and Engagement:
- Active participation in class discussions, workshops, and online forums.
Prüfungsinhalt/e
See Chapter 0 in Moodle
Beurteilungskriterien/-maßstäbe
Written or oral exam
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 22W.1)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
-
11a.4 Ausgewählte LVen der Informationstechnik: Chip Design, Einf.in die Multimedia-Technik, Fundamentals of Image Processing, Measurement Signal Processing, Mobile Robot Programming, Systemsicherheit (
0.0h VO, VC, KS, UE / 6.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS) Absolvierung im 3., 4., 5., 6. Semester empfohlen
-
11a.4 Ausgewählte LVen der Informationstechnik: Chip Design, Einf.in die Multimedia-Technik, Fundamentals of Image Processing, Measurement Signal Processing, Mobile Robot Programming, Systemsicherheit (
0.0h VO, VC, KS, UE / 6.0 ECTS)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 17W.1)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Autonomous Systems and Robotics: Advanced
(Wahlfach)
-
2.2 Further Lectures for ASR Advanced (
2.0h VC / 4.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
2.2 Further Lectures for ASR Advanced (
2.0h VC / 4.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: ICE- Supplements
(Wahlfach)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
- 700.320 Seminar in Big Data, Predictive Analytics and Automation (2.0h VS / 4.0 ECTS)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2022/23
- 700.320 VS Seminar on Big Data, Predictive Analytics, and Automation in Telecommunications and Intelligent Transportation Systems (2.0h / 4.0ECTS)
-
Wintersemester 2021/22
- 700.320 VS Seminar on Big Data, Predictive Analytics, and Automation in Telecommunications and Intelligent Transportation Systems (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 700.320 VS Seminar on Big Data, Predictive Analytics, and Automation in Telecommunications and Intelligent Transportation Systems (2.0h / 4.0ECTS)
-
Wintersemester 2019/20
- 700.320 VS Seminar on Big Data, Predictive Analytics, and Automation in Telecommunications and Intelligent Transportation Systems (2.0h / 4.0ECTS)