Big Data Management and Analytics (WS 2018/19)
News
- [29.05.2019] The insight for this exam (for all who participated and did not cancel their exam) is on Wed 12.06.2019 10-11 in the room 157 Oettingenstraße 67.
- [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
| Lecture | Tutorial | ||
|---|---|---|---|
| Date | Topic | Date | Topic |
| 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: |
| 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
|
| 13.11.18 | Lecture 5: Stream Processing | 14.11.18 15.11.18 |
Tutorial 3: |
| 20.11.18 | Lecture 6: Apache Flink | 21.11.18 22.11.18 |
Tutorial 4: |
| 27.10.18 | Lecture 7: Stream Analytics | 28.11.18 29.11.18 |
Tutorial 5: |
| 04.12.18 | Lecture 7: Stream Analytics cont. | 05.12.18 06.12.18 |
Tutorial 6: |
| 11.12.18 | Lecture 8: High Dimenasional Data | 12.12.18 13.12.18 |
Tutorial 7: |
| 18.12.18 | Lecture 8: High Dimenasional Data cont. | 19.12.18 20.12.18 |
Tutorial 8: |
| Happy Hollidays | |||
| 08.01.19 | Lecture 9: Community Detection | 09.01,19 10.01.19 |
Tutorial 9: |
| 15.01.19 | Lecture 10: Node Importance and Neighborhoods | 16.01.10 17.01.19 |
Tutorial 10: |
| 22.01.19 | Lecture 10: Python for Data Analytics - Best Practices | 23.01.19 24.01.19 |
Tutorial 11: |
| 29.01.19 | Lecture 11: The Flip Side of the Coin | 30.01.19 31.01.19 |
Tutorial 12: |
| 05.02.19 | Q&A | 06.02.19 07.02.19 |
|