Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies
Course (ROC) on Data Science and Engineering Systems and Technologies
(IV, 6 ECTS, 4 SWS)
Big Data (BD) and Machine Learning (ML) are key drivers underlying the current wave of innovation in artificial intelligence and data science. Indeed, these drivers have had a profound impact on both the economy and the sciences. This course targets research-oriented students who aim to pursue a PhD in Big Data Management or Data Science and Engineering Systems and Technologies. Upon completion of this course, students will have learned about contemporary research methodology, gained both theoretical and practical skills in data management and big data technologies, and be attuned to today’s major research challenges in scalable data management and processing. The course is designed to principally impart technical skills (30%), method skills (40%), systems skills (10%), and social skills (20%).
The central focus of this module is on contemporary research methodology (CRM), data management technologies, and related research challenges. After an initial presentation on CRM, including scientific reading, writing, and presenting, in subsequent lectures, students will read about foundational data management methods/technologies and offer a presentation, which will then be followed by an instructor led presentation addressing related advanced topics.
Topics of discussion, include data storage and indexing, specification and compilation of data analysis programs, query optimization and self-tuning, adaptive methods, processing data science pipelines as well as responsible data management. The course will also include a lab component, where students analyze and evaluate discussed methods, technologies, and settings in a methodical and scientific way.
Computer science topics addressed in TU Berlin modules in the Bachelor’s curriculum, particularly, both ISDA (Information Systems and Data Analysis) and DBPRA (Practical Database Systems Lab) or their equivalents, as well as good programming skills in C, Java, and SQL are all required. Additionally, an undergraduate course in linear algebra, probability, and statistics. This course will be offered in English. Thus, fluency in English is also required.
The portfolio exam (worth 100 points) is comprised of three parts: (i) technology presentation (30 points), (ii) experimentation presentation (30 points), and (iii) a final report (40 points). The final grade will be computed according to the Grading Table 2 of Faculty IV, according to German law, § 47 (2) AllgStuPO TU Berlin.
Prior to the start of the first lecture, students must register themselves in the DIMA Course Registration Tool: www.dima.tu-berlin.de . In addition, students must register both in ISIS (the course organization tool) -and- QISPOS (the TU Berlin Examination Management Tool) within the first six weeks of the current semester. The maximum number of participants is restricted to six due to limited mentoring capacity.