Your Deep Dive into Data Science

Use machine learning and data mining algorithms in R to develop artificial intelligence applications based on your data.

A selection of the training topics:

• Introduction to the basic terms of machine learning
• Introduction to machine learning algorithms such as decision trees, random forest, gradient boosting machine
• Introduction to a methodical approach in the development of machine learning models
• Typical steps in data preparation such as feature selection or data transformation
• Creation of training and test data sets
• Introduction of validation techniques such as cross-validation or bootstrapping
• Introduction and interpretation of different metrics for measuring success such as:
  • For classifications: Accuracy, sensitivity, specificity
  • For regression: RMSE, MAE, MAPE, …
• Interpretation of ROC curves
• Tuning of parameters
• Introduction of common machine learning packages

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Target group: This course is aimed at participants with initial programming experience in R.

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

As a Full Service Certified Partner of Posit and leading Shiny expert, we enable you to independently develop successful Shiny applications for productive use.

Topic Overview

• Introduction to Shiny
• Philosophy of Shiny: reactive programming
• Data structures and their properties
• User Interface Design
• Workflow for developing a Shiny application
• Extension packages around Shiny
• Do’s and Don’ts for the productive use of Shiny applications

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Target group: This course is aimed at participants with initial programming experience in R.

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