Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies

Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies (IV, 9 ECTS, 6 SWS) (pdf [1])

Learning Outcomes

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, including scientific reading, writing, presenting, prototyping and experimental design, 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 (20%), method skills (40%), systems skills (20%), and social skills (20%).

Content

The central focus of this module is on contemporary research methodology (CRM), data management technologies, and current research challenges. After an initial presentation on CRM, including scientific reading, writing, presenting, prototyping and experimental design, 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.

In an accompanying lab component, students will prototype and evaluate discussed methods, technologies, and settings in a methodical and scientific way, and produce a scientific report on their findings.

Workload and Credit Points

The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning MethodsThis Integrated Course (Integrierte Veranstaltung, IV) consists of: (i) lectures on key concepts, (ii) discussions, (iii) student lead presentations (including literature search), and (iv) a systems research project including (1) system setup, (2) prototyping, (3) experimental design, and (4) performance evaluation as well as (v) creating a presentation and report on the findings. Active participation and contributions to all parts of this course are essential.Requirements for participation and examinationDesirable prerequisites for participation in the  courses: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. Knowledge of master's level coursework in database technology (DBT) and advanced information management (AIM) is necessary. This course will be offered in English. Thus, fluency in English is also required.

Module completion

Grading: graded

Type of exam: Portfolio examination 100 points in total

Language: English

Test description:

The portfolio exam (worth 100 points) is comprised of four parts: (i) technology presentation (20 points), (ii) a quiz on database technology and research methodology (30 points), (iii) performance evaluation presentation (20 points),and (iv) a final report (30 points) will be computed according to the Grading Table 2 of Faculty IV, according to German law, § 47 (2) AllgStuPO TU Berlin.

Duration of the Module

This module can be completed in one semester.

Maximum Number of Participants

The maximum capacity of students is 8

Registration Procedures

Prior to the start of the first lecture, students must register themselves in the DIMA Course Registration Tool: www.dima.tu-berlin.de [2]. 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.

Recommended literature:

Readings in Database Systems, 5th Edition, Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker, editors, http://www.redbook.io/ [3]

Raj Jain: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling (Wiley Professional Computing), 1991

Various Research Papers, made available during the first lecture.

Assigned Degree Programs

This module is used in the following modulelists:

Computer Engineering (Master of Science) StuPO 2015

Modullisten der Semester: WS 2020/21

Computer Science (Informatik) (Master of Science) StuPO 2015

Modullisten der Semester: WS 2020/21

Elektrotechnik (Master of Science) StuPO 2015

Modullisten der Semester: WS 2020/21

Informatik (Bachelor of Science) StuPO 2015

Modullisten der Semester: WS 2020/21

Information Systems Management (Wirtschaftsinformatik) (Master of Science) StuPO 2017

Modullisten der Semester: WS 2020/21

Miscellaneous

This course targets research-oriented Bachelor’s and Master’s students interested in focusing on Database Systems and Information Management in Computer Science (Major: System Engineering), Computer Engineering (Major: Information Systems and Software Engineering), and Industrial Engineering, as well as students pursuing the Data Science and Engineering Master’s Track.