1. introduction to data science and machine learning

• Basic terms and concepts of data science and classic AI methods
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

2. the CRISP-DM cycle as a standard in data analysis

• Business Understanding: Goals, requirements, questions
• Data Understanding: Data structure and data quality
• Data preparation: cleaning, filtering and formatting data
• Modeling: Developing and validating data models 
• Evaluation: Reviewing models and aligning them with business objectives
• Deployment: Putting models for data analysis into operation
• New: along the phases of data understanding, preparation, modeling and evaluation, current use cases of language models (e.g. ChatGPT) are shown, which can provide targeted support for typical tasks in a data science project

3. all phases of the data project explained in detail

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering with classic machine learning tools (KNIME, Azure Machine Learning or RStudio) and in conjunction with language models (e.g. ChatGPT)
• Evaluate the model and put it into production