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

Students will build upon their foundational knowledge of Python programming, by exploring 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.

• Students are being provided 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.

The quest for artificial intelligence (AI) has captured humanity’s interest for many decades and has been an active research area since the 1960s. This course will give a detailed overview of the historical developments, successes, and set-backs in AI, as well as modern approaches in the development of artificial intelligence.

This course gives an introduction to reinforcement learning, a process similar to how humans and animals experience the world: exploring the environment and inferring the best course of action.

This course also covers the principles of natural language processing and computer vision, both of which are key ingredients for an artificial intelligence to be able to interact with its environment.

1. History of AI
   1. Historical Developments
   2. AI Winter
   3. Expert Systems
   4. Notable Advances
2. Modern AI Systems
   1. Narrow versus General AI
   2. Application Areas
3. Reinforcement Learning
   1. What is Reinforcement Learning?
   2. Markov Chains and Value Function
   3. Time-Difference and Q Learning
4. Natural Language Processing (NLP)
   1. Introduction to NLP and Application Areas
   2. Basic NLP Techniques
   3. Vectorizing Data
5. Computer Vision
   1. Introduction to Computer Vision
   2. Image Representation and Geometry
   3. Feature Detection
   4. Semantic Segmentation

This project course will give students hands-on experience in the challenging task of designing and developing an AI system for a specific application and domain.

Students will need to consider requirements and practical constraints as well as the desired output of the AI system.

Following this course the students will get holistic overview of developing a specific AI-based application.

• This project course focuses on understanding and implementing a simple AI system. Based on the course Artificial Intelligence (DLBDSEAI01), students will design and implement a simple AI system. In the first step, students will choose a specific application and domain and then use the methods from the course to analyze the requirements and outcomes before implementing their own AI application. All relevant artifacts and considerations are documented by the students in a course portfolio.