Data Science and Artificial Intelligence

Part 1: Fundamentals of agent-based programming

- Cellular automata, self-organization and emergences

- Properties of agents or agent-based models

- Description of agent-based models using the ODD protocol

- Overview of known agent-based models

Part 2: Programming and evaluation of agent-based models

- Introduction to the conception and programming of agent-based models

- Introduction to the evaluation of agent-based models / simulations

- Advanced topics in agent-based modeling

Part 1: Basics

- Overview and definition of terms

- Processor architectures (CPU, GPU, TPU, ...) and relevant interfaces

Part 2: Hardware virtualization

- Platform virtualization

- Relevant cluster frameworks in the context of hardware virtualization

- Storage virtualization

Part 3: Operating system virtualization

- Container virtualization

- Relevant cluster frameworks in the context of operating system virtualization

Part 1: Classic data structures and algorithms

- Computability, Turing machine and Optimal Stopping

- Runtime considerations and Landau notation

- Basic tasks of algorithm development

- Simple and advanced data structures

- Simple algorithms (backtracking, bubblesort, ...)

- Divide and conquer principle (including dynamic programming)

Area 2: Advanced algorithms

- Special features when accessing sequentially stored data

- Priority queues and self-organizing data structures

- Basics of lossy compression of data

- Basics of Fast Fourier Transform

- Single pass algorithms

- Kalman filter

Part 1: Aspects of numerics

- Numerical presentation on the computer

- Type and reduction of numerical errors

- Conditioning problems

- Numerical differentiation and numerical quadrature

- Numerical solving of systems of equations (including Newton's method)

- Pivoting and matrix decomposition (LU, QR, ...)

Part 2: Optimization

- Basic aspects of optimization tasks

- One- and multi-dimensional extreme value tasks

- 1st order descent procedure (steepest descent, impulse methods, ...)

- 2nd order descent procedure (Newton and Newton-style procedures, ...)

- Conjugate gradients

- Linear optimization, simplex algorithm, MILP problems

- Optimization with constraints (long-range approach including KKT conditions)

- Multi-criteria optimization (including Pareto analysis)

- Special methods of stochastic optimization (e.g. simulated anealing)

Area 1: Basics and tools

- Repetition of natural neural networks

- Perceptron and linear separability

- Basic structures of artificial neural networks

- Multilayered Perceptron and error back propagation

- Hopfield Networks

- Markow Chain Monte Carlo Methods

- Tensors and tensor calculation

- Common frameworks for artificial neural networks

Area 2: Fields of application

- Time Series prediction

- Handwriting Recognition

- Associative Pattern Recognition

Area 3: Advanced Architectures

- Boltzmann Machines

- Self-organizing Cards

- Autoencoder

- Basics of Convolutional Neural Networks

- Basics of Recurrent Neural Networks

Part 1: ETL or ETL processes

- Basics of ETL resp. ETL processes

- Planning and creation of ETL workflows

Part 2: Multidimensional resp. OLAP databases

- Basics of multidimensional resp. OLAP databases

- Planning and creation of multidimensional resp. OLAP databases

- Access to multidimensional resp. OLAP databases

- Introduction to the query language MDX

- Data mining using multidimensional resp. OLAP databases

Part 3: Business Intelligence resp. Business Analytics

- Introduction to business intelligence and business analytics

- Overview of important solutions in the area of business intelligence and business analytics

- Overview of important solutions in the area of self-service BI

Part 1: Structure-discovering processes:

- Principal Component Analysis

- Exploratory factor analysis

- Nearest neighbor classification

- Cluster analysis

- Partial Least Squares regression

- Support vector machines

- Multidimensional scaling

Part 2: Structural inspections:

- Multivariate linear, nonlinear and logistic regression

- LASSO (least absolute shrinkage and selection operator)

- Multivariate time series analysis (including structural break analysis)

- Structural equation models

- Discriminant analysis

- Analysis of variance

- Confirmatory factor analysis

Part 3: Text mining

- Word frequencies and correlations

- Grouping / clustering of texts

Part 1: Basics of visualization

- Basics of human processing of visual information

- Pitfalls and distortions in visualizations

- Standardization in the field of visualization

- Report and chart types and their properties

- Classic diagram types (area, bar, column, line, network diagrams, boxplots, scatterplots etc.)

- Modern diagram types (heat maps, tree maps, stream graphs, chord and sunburst diagrams etc.)

- Special types of diagrams (speedometer, waterfall diagrams, maps etc.)

- Text-based visualizations (word clouds, infographics, etc.)

Part 2: Advanced topics

- Animated visualizations

- Interactive visualizations

- Automated dynamic reporting

Part 1: Preparation of data

- Reading in and working with data from different sources (CSV, XML, HTML, JSON, ...)

- Character sets or character set transformation

- Data type conversion and renormalization

- Duplicate detection and deduplication

- Complex transformations of data (especially pivoting and unpivoting)

- Complex filtering and sorting of data

Part 2: Erroneous and incomplete data-

Data quality analysis

- Smoothing discrete data

- Anomaly detection

- Singular and multiple imputation

Part 3: Continuous data

- Special features of audio, image and video data (or signal data)

- Transformations and discretization of continuous data

- Convolution and application of filters

- Smooth continuous data

- Compression of continuous data

Part 1: Fundamentals of cloud computing

- Overview and definition of terms

- IT architectures and IT service management

- Service deployment models (XaaS, Edge Computing, Fog Computing, ...)

- Security management and identity management

- Overview of important cloud computing providers

Part 2: Introduction to cloud computing

- Idenity management binding and synchronization

- Setup and configuration of simple cloud services

- Monitoring and cost management

Part 3: Data storage and data processing in the cloud

- Setup, configuration and deployment of selected storage services

- Setup, configuration and deployment of clusters for the distributed storage and processing of big data

- High-performance and scalable queries

Part 1: Advanced KI-powered applications

- Semantic text analysis and text synthesis, natural language processing - Biometric analysis

- Generation of synthetic data sets

- Other advanced KI-powered applications

Part 2: Methods of Artificial Intelligence in Practice

- Field of application as well as advantages and disadvantages of different KI methods

- Hybrid approaches (fuzzy neural approaches etc.)

- Selection of suitable AI methods for specific problems

- Typical mistakes and problems as well as their avoidance or reduction

- New approaches in artificial intelligence and computational intelligence

Part 1: Advanced topics regarding neural networks

- Convolutional Neural Networks

- Recurrent Neural Networks

- Generative Adversarial Networks

Part 2: Advanced applications of neural networks

- Handwriting and speech recognition

- Edge detection in pictures and videos

- Object recognition in pictures and videos

Part 3: Deep learning in practice

- Deep learning frameworks for CPU, GPU and TPU computing

- Planning, conception, setup as well as training and optimization of neural networks

Part 1: Preferences and Mechanism Design Theory

- Binary relations and preference orders

- Theory of disclosed preferences and conjoint analyzes

- Preference aggregation method and Arrow's impossibility theorem

- Gibbard-Satterthwaite theorem

Part 2: Decision Theory

- Decision-theoretical basic concepts

- Risk awareness and risk tendency

- Solution concepts for risk decisions

- Solution concepts for decisions in the event of uncertainty

Part 3: Non-cooperative game theory

- Basic concepts of non-cooperative game theory

- Static games with complete information

- Dynamic games with complete informatio

- Static games with incomplete information

- Dynamic games with incomplete information

- Auctions and auction theory

Part 4: Cooperative game theory

- Basic concepts of cooperative game theory ..

Part 1: Swarm intelligence

- Basics of swarm intelligence

- Examples of swarm-intelligent systems

- Basics of particle swarm optimization

- Conception and programming of swarm-intelligent models using agent-based programming

- Evaluation of swarm-intelligent models / simulations

Part 2: Genetic and evolutionary algorithms

- Basic principles of genetic and evolutionary algorithms

- Applications of genetic and evolutionary algorithms

- Use of evolutionary algorithms to evaluate agent-based models

- Basic principles of evolutionary game theory

- Basic principles of artificial immune systems

Part 1: Basic business terms

- Controlling and accounting

- Investment and finance

- Organization, HR management and leadership

- Performance management

- Marketing , customer relationship management and logistics

- Legal framework

- Risk and risk management

Part 2: Strategic Analysis

- External analysis of macroeconomics, industry, sectors etc.

- Internal analysis of resources, stakeholders, governance, corporate culture etc.

- SWOT analysis

Part 3: Strategies and strategy development

- Business strategy vs. Corporate strategy

- Mergers & acquisitions and strategic alliances

- Strategy development in practice

Part 4: Innovation

- Innovation, entrepreneurship and intrapreneurship

- Software solutions for performing Monte Carlo Simulations

- Monte Carlo simulation as well as creation of business models and financial plans (especially P&L, cash flow planning)

- Rapid prototyping

Part 1: Philosophy of Science

- History of the philosophy of science

- Important theories resp. lines of thought in scientific theory

- Overview of scientific research methods

Part 2: Research processes

- Deriving research questions and hypotheses

- Conducting intensive research

- Design of the research project or decision regarding methodology

- Analysis, publication and presentation of gain of knowledge

- Working techniques and time management

Part 3: Publication and publication standards

- Clear and consistent writing style as well as gender-appropriate wording

- Citation and handling of literature management programs

- Property rights and ethical principles

- Structuring, formatting and visualization of publications

- Publicationvariants

- Quality assurance resp. reviews and peer reviews

- Rankings and impact factors

Part 1: Fundamentals of R&D project management

- Basic terms and phases

- norms and standards

- methods and tools

- Basics of agile project management

- Communication, presentation and moderation

- crisis management

Part 2: Funding projects

- An important basis for funding projects

- Important funding agencies and funding channels

Part 3: Software-based project management

- Software for planning, controlling and controlling projects

- Software-based project management in practice

Part 4: Evaluation of software solutions

- Important evaluation criteria for software in the field of data science

- Established state-of-the-art platforms and software solutions

Part 1: Implementation of data science projects

- Dealing with given requirements

- Development of different solution strategies

- Planning, implementation, control and controlling the project resp. project progress

- Teamwork including any conflict resolution

Part 2: Project documentation and dissemination of project results

- Creation of project documentation based on norms, standards and specifications

- Presentation and discussion of project and results

Part 1: Ethics

- Ethical funamentals and problems

- Ethical consideration of big data and artificial intelligence

- Corporate social responsibility

Part 2: Data protection

- Basic terms and overview

- Data protection law

- General data protection regulation

- Enforcement in data protection

Part 3: Compliance or IT compliance

- Governance and compliance

- IT governance and IT compliance

- IT risks and IT risk management

Part 1: Data Science in Practice

- Analysis of problems and selection of suitable methods and algorithms

- Discussion of the advantages and disadvantages of different methods and algorithms

Part 2: Best Practices and the future of Data Sciene

- Best practices in data science projects

- Avoiding typical pitfalls in data science projects

- Discussion of the status quo and the future of data science

Part 1: Master's thesis

- Deriving research questions and hypotheses

- Conducting intensive research

- Design of the research project or decision regarding methodology

- Implementation of the planned research project

- Writing the master's thesis according to certain norms, standards and specifications

- Regular coordination with the supervisor of the master's thesis

Part 2: Master’s examination

- Presentation and defense of the master thesis

- Taking partial exams on important content relevant to the curriculum

Part 1: Exposé for the master's thesis

- Preparation of the synopsis for the master's thesis according to certain norms, standards and guidelines

Part 2: Dissemination of the first results of the master thesis

- Presentation and defense of the first results of the master thesis

- Discussion about the first results of other master thesis projects

- give and take feedback and reflect