Data Science and introduction to R

Why take this course

Do you want to master practical knowledge in the area of statistical data analysis and machine learning? This training will allow you to learn key techniques and methods used in banking, especially for credit risk analysis. Thanks to it, you will be able to independently create and implement data-based models using the R programming language.

What you'll learn

• Statistical and AI/ML methods used in banking, with a special focus on credit risk analysis.
• Create and implement basic data science and machine learning models in the R language.
• How to use R to solve problems in the area of credit risk.
• Practical knowledge, supported by examples and case studies from the banking sector.
• The most important statistical concepts and the principles of machine learning.

Certification & Exam

Upon completion of the training, you will receive a certificate of participation, confirming your skills in data analysis and R programming. There is no final exam—all you need is active participation in the classes.

Who is this course for

This course is for analysts and people cooperating with specialists in the field of data analysis and machine learning who want to gain a solid foundation in these areas, with an emphasis on practical applications in credit risk.

Topics covered

• Introduction to statistical data analysis
  • Introduction to Data Science, Machine Learning and Artificial Intelligence​
  • Development of DS/ML/AI in the last 25 years​
  • Key statistical concepts​
  • Types of statistical models​
  • Linear and nonlinear models​
  • Estimation methods​
  • Methods of validation and model selection​
  • Principles of statistical tests​
• Introduction to Machine Learning
  • Regression, classification, learning​
  • Discrete choice models​
  • Unbalanced sampling​
  • Decision trees, random forests, SVMs​
  • Empirical and structural risk (SLT)​
  • Regularization​
  • AI and machine learning​
  • Artificial neural networks​
  • Types of neural networks​
  • Neural network learning methods​
  • Principles of deep learning​
  • Modern neural network architectures​
  • Generative models​
  • LLM models
• Introduction to programming with R
  • R and R Studio​
  • Loading data​
  • Data structures and functions in R​
  • Data processing​
  • Regression models, decision trees, random forests, SVMs​
  • Artificial neural networks – construction and learning​
  • Deep learning – construction and learning​
  • Combining R with AI learning environments

The work in R will be based on specially prepared case studies from the area of credit risk.