The basic training in Python with the focus on data science is the ideal introduction for analysts to the programming language. In addition to programming paradigms, object types and syntax structures and pandas, the central library for data management. Through the training the participants will be able to implement the data management as well as the first analyses and visualizations in Python.

Course content:

• First steps into Python
• Concept and philosophy of Python
• Data structures and their properties
• Importing data
• Data management with pandas
• Data analysis with Python
• Loops and control elements

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Recommended course length: Two days

A selection of the training contents:

• Introduction to the basic concepts of machine learning
• Dealing with the machine learning framework scikit-learn
• Introduction to machine learning algorithms such as decision trees, support vector machines or random forests
• Creation of training and test data
• Parameter tuning of the models with the help of cross-validations
• Presentation of relevant processing steps such as one-hot encoding, standardization or imputation
• Presentation of different metrics of model evaluation
  • For classifications: (Balanced) Accuracy, Sensitivity, Specificity, Area under the curve
  • For regressions: RMSE, MAE
• Linking of preparation and modeling steps in pipeline objects

The course is aimed at people who have already had some programming experience with Python and have a basic understanding of statistics.

Recommended course length: Two days

GenAI has long been an integral part of the working world. In our training course, you will acquire the tools you need to thoroughly understand and apply GenAI from a data science perspective. As the field is evolving rapidly, we ensure that you become familiar with state-of-the-art approaches through regular updates and trainers with practical experience.

The aim of the training is to impart practical skills in the field of generative AI. After the training, participants will be able to thoroughly understand language models and RAG, implement their own RAG systems with Python and company data, and put agent workflows with Langchain and Langgraph into practice.

The course content at a glance:

• Introduction to language models
  • Fundamentals and key concepts
  • Use cases
  • Current developments
• Retrieval Augmented Generation (RAG)
  • Purpose and functionality
  • Data preparation and connection for RAG systems
  • RAG components: vector database and embedding models
  • Introduction to Langchain
  • Practical implementation and exercises
• Agent systems
  • Purpose and functionality
  • Components of agent systems: planning, tools, reflection
  • Introduction to Langgraph
  • Practical implementation and exercises

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Target group: Data scientists and software developers with Python skills

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Course length: Two days

In addition, we offer further advanced topics for an optional third day of the course:

• LLM Security
  • Problem areas and vulnerabilities in LLM deployment
  • Guardrails/risk mitigation measures, including red teaming exercises
• Evaluation, tracing, monitoring
• Infrastructure for LLMs and other system components
• In-depth exercises on agentic RAG systems with your own data