Assuming familiarity with basic data management and storage techniques (such as ER models and SQL), this course shall teach you essentials of data management and data governance, from the concepts to their application on practical examples (for example applied to Web data and in business scenarios). 

We will start off strategic aspects of Data Management including proper Data Governance, but also discuss the usage of Cloud Services and Virtualisation to store, host and manage data, including regulatory and legal implications. On the technical side, we will focus on advanced databases, storage and data management techniques, analytical queries using SQL and Relational Database Management Systems, but also discuss more recent architectures to scalable store and retrieve data, that are meant to scale better for particular forms of data, such as Document Stores and GraphDatabases; i.e., we will also discuss how to make certain tasks scale with big data (i.e. high volume, high velocity or highly heterogeneous data). Here, we will review traditional indexing techniques and methods to deal with concurrent data access and discuss trends in Data Management and Storage. We will also tackle the complementary topics of database access, data quality, and cloud data management.

In this course you shall

• learn the fundamental principles of data management and data governance
• learn how to structure and model data for analytics, including in the cloud
• understand how to store this data in modern database systems
• understand how to extract knowledge from a database by formulate complex questions as queries using SQL and other query languages
• understand how to improve query performance for common queries using indexes
• understand how to assess data quality
• apply your conceptual learnings on practical cases using (publicly available) real data using tools such as R and Python

According to the examination regulation full attendance is intended for a PI. Attendance of 80% of all classes is compulsory

The covered topics will be discussed in 6 classes, each of which will consist of concepts delivered in the form of pre-watching videos or reading materials to be prepared by the students, which are then in the lecture applied in Jupyter notebooks.

• Each of the classes will be accompanied by one or more interactive Jupyter Notebook(s) that should be prepared in groups before class and that we will walk through together in class,
  • final notebooks to be submitted after each class as documentation of in-class participation, the worst result can be discarded, i.e. max. 20% for in-class work.
  • for active participation (in terms of e.g. presenting solutions in class to the others, constructive discussion on the forum), max. 5%.
• "Mastery" path: 10% of the points can be achieved by extra individually solved notebooks per class that will require additional reading.
• Each class will additionally have some quiz questions per class (max. 4% each class, the worst result can be discarded, i.e. max. 20% for quizzes)
• Oral group assignment review: based on your ability to explain your submitted notebooks (including mastery path notebooks): max. 15%
• Plus an exam (max. 30%; in-class exam in the last week of the course)
   

Grading Scheme:

>= 90% ... Excellent (1)

>= 80% ... Good (2)

>= 70% ... Satisfacory (3)

>= 60% ... Sufficient (4)

 <  60% ... Fail

We expect that you participate(d) in the "Foundations of Digital Economy" course and build upon its contents, especially regarding programming and databases. Also, we will use Jupyter and Python as tools in the course.

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We expect that you participate(d) in the "Foundations of Digital Economy" course and build upon its contents, especially regarding programming and databases. Also, we will use Jupyter and Python as tools in the course.