Assuming familiarity with basic data management and storage techniques (such as ER models and SQL), which – if needed – will be repeated in a bridging course, this course shall teach you essentials of data management and analytics, from the concepts to their application on practical examples (for example applied to Web data but also to business scenarios). 

Part A of the course will focus on advanced databases, storage and data management techniques, analytical queries using SQL and Relational Database Management Systems, but also discuss Document and GraphDatabases. Next, we will discuss how to make certain tasks scale with big data (i.e. high volume, high velocity or highly heterogeneous data). To this end, we will review traditional indexing techniques and methods to deal with concurrent data access and discuss trends in Data Management and Storage. 

In Part B, we will cover (Descriptive, Predictive and Prescriptive Analytics) Data Analytics techniques and discuss how these can be scaled.

In this course you shall

• learn how to structure and model data for analytics
• 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
• learn about data analytics tasks to be performed on data in a database or collected/integrated from different strucured and unstructured data sources
• 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 12 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, max 3% per class, the worst result can be discarded, i.e. max. 30% for in-class work.
  • for those presenting their solutions in class to the others, there is maximum of 3 bonus points throughout the semester achievable on top.
• "Mastery" path: 10% of the points can be achieved by extra notebooks per class that will require additional reading.
• Each class will additionally have up to 3 clicker quiz questions per class (max. 2% each class, the worst result can be discarded, i.e. max. 20% for quizzes)
• plus two individualised exams (20% each, i.e. 40%) for Part A and Part B:
  • Part A will be an in-class exam in the last week of the course,
  • Part B will be a take-home exam in the form of a small individual project.

Grading Scheme:

>= 90% ... Excellent (1)

>= 80% ... Good (2)

>= 70% ... Satisfacory (3)

>= 60% ... Sufficient (4)

 <  60% ... Fail

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We expect that you fully understand and will build upon the contents of the Bridging Course in DigEcon: IT & IS Skills, particularly:

• Block 2: Basic Programming Skills
• Block 3: Databases

In more detail, we expect a basic understanding of Data Modeling and Schemas, basic SQL, relational algebra. Also, we will use Jupyter and Python as tools in the course, which you should have become familiar with during the Bridging courses.

In particular, we may check the prior knowledge of Block 3 in a "prior knowledge check" quiz in the first unit of the course.