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Big Data Management and Analytics (WS 2018/19)

News

  • [19.03.19] The regristration for the follow-up exam (Nachklausur) scheduled for Wed. 17.04.2019 14:00-16:00 in A 140 (Main building) in UniWorX is now open. Participants with a permission for compensation for disadvantages (Nachteilsausgleich) who want to have a writing time extension (Schreibzeitverlängerung) have to report until at latest 10.04.2019. Requests after 10.04.2019 can not be considered.
  • [15.03.19] The insight into the exam (for all who participated and did not cancel their exam) is scheduled for Tue. 02.04.2019 from 14:00-15:00 in 157 Oettingenstraße 67.
  • [11.03.19] The follow-up exam is scheduled for Wed. 17.04.2019 14:00-16:00 in A 140 (Main building). The registration in UniWorX for the follow-up exam will be opened soon.
  • [18.02.19] Announcement for the Exam: The exam takes place at Wednesday 20.02.19 at 14:00 in the LMU mainbuilding, Geschwister-Scholl-Platz 1 in the following lecture halls with the following last names assigned: A 240 : A-L; M 218: M-Z. Please get at latest at 13:55 in your corresponding lecture hall. The lead time of the exam is 90 minutes. A simple non-programmable calculator is permitted.
  • [14.02.19] Update: On slide 14 in Lecture 7: Stream Analytics, an error has been fixed.
  • Update: Slide Series 8 have been corrected. On P.34 a slide is introduced providing a step-by-step guide on how the radius is computed
  • Registration for the exam is now possible in UniWorX.
  • Compensation for disadvantages (Nachteilsausgleich): All students who are eligible to get an extension of the writing time (Schreibzeitverlängerung) please report it to us until at latest 13.02.2019. Requests after the 13.02.2019 can not be considered.
  • The first Tutorial begins at 24.10.18.
  • On Thursday (25.10, 14-16 and 16-18) the Tutorials takes place (unscheduled) in the "Edmund-Rumpler-Straße" Room B 257
  • You can bring a Notebook for the Tutorials, however this is not mandatory!
  • 02.10.18: The exam is scheduled for wednesday 20.02.19 14:00-16:00 in the lecture halls M 218 and A 240 (LMU main building)
  • 26.09.18: The registration for this module is now open in UniWorX

Organisation

  • Umfang: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Matthias Schubert
  • Assistants: Daniyal Kazempour, Evgeniy Faerman
  • Required: Lecture "Database Systems I" or equivalent
  • Beneficial: Lecture "Knowledge Discovery in Databases I" or equivalent
  • Audience: The lecture is directed towards Bachelor students (5th term) and Master students in Mediainformatics, Bioinformatics, and Informatics

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Tue, 13.00 - 16.00 h Room S 004 (Schellingstr. 3) 16.10.2018
Tutorial 1 Wed, 16.00 - 18.00 h Room D Z007 (HGB) 24.10.2018
Tutorial 2 Wed, 18.00 - 20.00 h Room D Z007 (HGB) 24.10.2018
Tutorial 3 Thu, 16.00 - 18.00 h Room D Z007 (HGB) 25.10.2018
Tutorial 4 Thu, 14.00 - 16.00 h Room D Z007 (HGB) 25.10.2018

Content

In almost all areas of business, industry, science, and everybody's life, the amount of available data that contains value and knowledge is immense and fast growing. However, turning data into information, information into knowledge, and knowledge into value is challenging.To extract the knowledge, the data needs to be stored, managed, and analyzed. Thereby, we not only have to cope with increasing amount of data, but also with increasing velocity, i.e., data streamed in high rates, with heterogeneous data sources and also more and more have to take data quality and reliability of data and information into account. These properties referring to the four V's (Volume, Velocity, Variety, and Veracity) are the key properties of "Big Data". Big Data grows faster than our ability to process the data, so we need new architectures, algorithms and approaches for managing, processing, and analyzing Big Data that goes beyond traditional concepts for knowledge discovery and data mining.

This course introduces Big Data, challenges associated with Big Data, and basic concepts for Big Data Management and Big Data Analytics which are important components in the new and popular field Data Science.

 

Course Schedule

LectureTutorial
DateTopicDateTopic
16.10.18 Lecture 1: Data Science: The Big Picture 17.10.18
18.10.18
-
23.10.18 Lecture 2: NoSQL Databases 24.10.18
25.10.18

Tutorial 1:

Python Core Language

Solution (Python Notebook)

30.10.18 Lecture 3: Batch Systems no tutorials All Saint's Day
06.11.18 Lecture 4: : Apache Spark 7.11.18
8.11.18

Tutorial 2:

Python OOP, Pandas, Numpy, Matplotlib

Moviedata
Blobdata
Mousedata
Solution (Python Notebook)

 

13.11.18 Lecture 5: Stream Processing 14.11.18
15.11.18

Tutorial 3:

Hadoop and MapReduce

Slides

20.11.18 Lecture 6: Apache Flink 21.11.18
22.11.18

Tutorial 4:

Spark

customercookies.csv

kMeans_template.py

Solutions

27.10.18 Lecture 7: Stream Analytics 28.11.18
29.11.18

Tutorial 5:

Stream Processing

Slides

04.12.18 Lecture 7: Stream Analytics cont. 05.12.18
06.12.18

Tutorial 6:

Flink & Streaming

mm_flink_template.java

Code Solutions

Slides

11.12.18 Lecture 8: High Dimenasional Data 12.12.18
13.12.18

Tutorial 7:

Stream Processing II

Slides

18.12.18 Lecture 8: High Dimenasional Data cont. 19.12.18
20.12.18

Tutorial 8:

Stream Clustering and Text Processing

Slides

Happy Hollidays
08.01.19 Lecture 9: Community Detection 09.01,19
10.01.19

Tutorial 9:

PCA and SVD

PowerIteration Code

Slides

15.01.19 Lecture 10: Node Importance and Neighborhoods 16.01.10
17.01.19

Tutorial 10:

SVD and CUR

SteamData

CUR Code

Slides

22.01.19 Lecture 10: Python for Data Analytics - Best Practices 23.01.19
24.01.19

Tutorial 11:

Page Rank

Slides

29.01.19 Lecture 11: The Flip Side of the Coin 30.01.19
31.01.19

 

Tutorial 12:

Community Detection

Slides

05.02.19 Q&A 06.02.19
07.02.19