[WiSe 21/22] Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies

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.

Description of Teaching and Learning Methods

This 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 examination

Desirable 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.

Registration and COVID-19

Our goal is to conduct ROC on-campus, in person. With respect to COVID-19, we will follow the 2G rule with persons either fully vaccinated or recovered (geimpft oder genesen) attending. Therefore please confirm that you will be either fully vaccinated or recovered at the first lecture of the course (on Oct 26th 14:00-18:00 EN719) and that you will be able to provide proof of that during the first in-person meeting.

Furthermore, in order to get to know you and plan the course accordingly, please confirm resp. answer the following questions in advance and sent them to melanie.neumann@tu-berlin.de: 

  1. I confirm that I am fully vaccinated against or recovered from COVID-19 and will show proof during the first lecture.
  2. What is your motivation for attending the ROC – Course?
  3. What do you expect from the Course?
  4. What courses have you taken in the area of data management systems so far?
  5. What do you consider the biggest current challenges in data management