This course provides students with a foundational understanding of the Python programming language. Following an introductory exposition to the importance of Python for data science-related programming tasks, students will be acquainted with fundamental programming concepts like variables, data types, and statements. Building on this basis, the important notion of a function is explained and errors, exception handling, and logging are explicated. The course concludes with an overview of the most widely-used library packages for data science.

1. Introduction
   1. Why Python?
   2. Obtaining and installing Python
   3. The Python interpreter , IPython, and Jupyter
2. Variables and Data Types
   1. Variables and value assignment
   2. Numbers
   3. Strings
   4. Collections
   5. Files
3. Statements
   1. Assignment, expressions, and print
   2. Conditional statements
   3. Loops
   4. Iterators and comprehensions
4. Functions
   1. Function declaration
   2. Scope
   3. Arguments
5. Errors and Exceptions
   1. Errors
   2. Exception handling
   3. Logs
6. Modules and Packages
   1. Usage
   2. Namespaces
   3. Documentation
   4. Popular data science packages

This course builds upon basic knowledge of Python programming (Introduction to Programming with Python, DLBDSIPWP) and is concerned with the exposition of advanced Python programming concepts. To this end, important notions of object-oriented programming like classes and objects and pertaining design principles are outlined. Starting from an in-depth discussion of advanced features of Python functions, functional programming concepts and their implementation in Python are conveyed.

• This course provides students with a thorough introduction to important notions and concepts from the domain of object-oriented programming such as classes, objects, abstraction, encapsulation, inheritance, polymorphism, composition, and delegation. Additionally, the functional programming paradigm and pertaining ideas like functions as first class objects, decorators, pure functions, immutability and higher order functions are conveyed. Pursuant to the portfolio course type, the aforementioned concepts and ideas are explored by hands-on programming projects.

Stored data form the basis of many value chains of an information and knowledge society. The methodical structuring of data through data schemas therefore forms an important basis for storing information in such a way that it can be retrieved and processed quickly and easily. In addition to the structured storage of data, structured access to large amounts of data must also be possible.

This course teaches students how to store data in relational data models and how to access stored data with SQL. In addition to relational database systems, modern DB systems (NoSQL) for storing and accessing data will be presented.

1. Fundamentals of Relational Databases
   1. Basic Concepts of the Relational Data Model
   2. Find and Delete Records in the Database
   3. SQL and Relational Database Systems
2. Querying Data from a Single Table
   1. Query Data (SELECT)
   2. Query Data With Condition (WHERE)
   3. Sort Query Output (ORDER BY)
   4. Queries With Group Formation (GROUP BY)
   5. Subqueries With Nested SELECT Statements
3. Conception and Modeling of Relational Databases
   1. The Entity Relationship Model
   2. Relationships and Cardinalities in E/R Models
   3. Normal Forms of Databases
4. Creation of Relational Databases
   1. Logical Database Design Activities
   2. Mapping of the Conceptual Data Model into the Physical Data Model
   3. Generation of Tables in SQL Databases from E/R Diagrams
5. Complex Database Queries on Multiple Tables
   1. Composite Quantities (JOIN)
   2. Set Operations
   3. Data Views With CREATE VIEW
6. Manipulating Records in Databases
   1. Insert New Data Records (INSERT)
   2. Change Existing Records
   3. Transactions
7. NoSQL Database Systems
   1. Motivation and Basic Idea
   2. Selected Groups of NoSQL Systems

This course provides the opportunity to implement a realistic database use case scenario. A list of use case ideas is provided on the online learning platform. In addition, the students can contribute use case ideas of their own in accord with the tutor. The core aim is to apply the hitherto theoretical knowledge of database methods and approaches to solve a real-world application scenario. This entails reasoning about possible design and architectural choices in a rational way, as well as implementing them in a functioning database system.

• In this course, students apply their knowledge of data modeling and databases to implement a project use case of their choosing. All relevant artefacts, like use case evaluation, chosen implementation method, code, and outcomes, are documented in the form of a written project report.