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Advanced Information Management 3 (AIM-3) Scalable Data Science: Systems & Methods (SDSSM)



#40311 / #5

SS 2017 - SS 2018

Fakultät IV

EN 7

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement

Markl, Volker

Soto, Juan

POS-Nummer PORD-Nummer Modultitel
2346010 36529 Advanced Information Management 3 (AIM-3) Scalable Data Science: Systems & Methods (SDSSM)

Learning Outcomes


The module will focus on mainstream distributed processing platforms and paradigms and learn how to employ these to solve challenging big data problems using popular data mining methods. Students will learn how to implement and employ varying data mining algorithms, such as Naïve Bayes, K-Means Clustering, and PageRank on varying open-source systems (e.g., Apache Hadoop, Apache Flink).

Module Components


All Courses are mandatory.

Course Name Type Number Cycle Language SWS VZ
Advanced Information Management 3 (AIM-3) - Scalable Data Science: Systems & Methods (SDSSM) IV 0434 L 472 WS/SS No information 4

Workload and Credit Points

Advanced Information Management 3 (AIM-3) - Scalable Data Science: Systems & Methods (SDSSM) (IV):

Workload description Multiplier Hours Total
Exercises/Practice 15.0 4.0h 60.0h
Plenary sessions 15.0 4.0h 60.0h
Preparation & Consolidation (incl. literature studies) 15.0 4.0h 60.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

This Integrated Course (Integrierte Veranstaltung, IV) consists of: (i) lectures on key concepts, (ii) practical theoretical & programming exercises, and (iii) student lead presentations (including literature search). 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, the database course (“Information Systems and Data Analysis”) or the equivalent, as well as good Java programming skills are required. Basic knowledge in linear algebra, numerical analysis, probability, and statistics are strongly recommended. Furthermore, it is preferable if students have already completed (or are currently enrolled in) a machine-learning course. Since the course will be offered in English, fluency in English is also required.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 points in total



Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) Homework 30 written 30 hours / 20 pages
(Deliverable assessment) In-class presentations 20 oral 40 min. / about 35 slides
(Examination) Written test 50 written 75 min.

Grading scale

Test description (Module completion)

The portfolio exam (worth 100 points) is comprised of three parts, namely: (i) written homework (30 points), (ii) in-class presentations (20 portfolio points), and (iii) a written exam (50 portfolio points). The final grade according to § 47 (2) AllgStuPO will be calculated with the faculty grading table 2. (Die Gesamtnote gemäß § 47 (2) AllgStuPO wird nach dem Notenschlüssel 2 der Fakultät IV ermittelt.)

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

Students are required to register via the DIMA course registration tool before the start of the first lecture ( Within the first six weeks after commencement of the lecture, students will have to register for the course at QISPOS (university examination protocol tool) and ISIS (course organization tool) in addition to the registration at the DIMA course registration tool.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  available
Additional information:


Recommended literature
Anand Rajaraman, Jeffrey David Ullman : Mining of Massive Datasets (Free Online:
Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten and Eibe Frank, Morgan Kaufmann, 2011.
Hadoop: The Definitive Guide (4th Edition), Tom White, O’Reilly Media, 2015.
Supplementary reading material may be assigned to complement course lectures.

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

This course targets Master’s students focused on Database Systems and Information Management in Computer Science (Major: System Engineering), Computer Engineering (Major: Information Systems & Software Engineering), and Industrial Engineering. Compulsory Elective module for ERASMUS MUNDUS IT4BI, plus Compulsory for EIT-ICT Data Science (DS) and Compulsory Elective for EIT-ICT Cloud Computing and Services (CCS) Subject to space availability, Master’s students in other academic programs may also enroll and satisfy elective module requirements. Wahlpflichtmodul im Masterstudiengang Informatik/Studienschwerpunkt System Engineering, Tech-nische Informatik/Studienschwerpunkte Informationssysteme & Software Engineering und im Master-studiengang Wirtschaftsingenieurswesen (Studiengang IuK). Wahlpflichtmodul im ERASMUS MUNDUS IT4BI, sowie für EIT-ICT Cloud Computing and Services (CCS), Pflicht für EIT-ICT Data Science (DS). Je nach Verfügbarleit der Plätze können auch Studierende anderer Fachrichtungen als Wahlpflicht das Modul belegen.


Since 2014, this module is offered each summer and winter term. For each topic during this course additional research papers and reports will be used.