Data Science and introduction to R

Course objectives

To get familiarized with core methods within the area of statistical data analysis and Machine Learning, along with typical applications in the banking industry.

Benefits

Participants of the training will learn:

• what kind of statistical and AI methods are used in banking,
• what classes of processes are supported using that kind of models,
• how to implement basic data science models in R,

Discussed methods are accompanied by practical examples.

Training scope

• Introduction to statistical data analysis
  • Classes of statistical models,
  • Linear and nonlinear models,
  • Methods of estimation (OLS, NLS, ML, GMM) ,
  • Principles of statistical hypothesis testing,
  • Model selection techniques,
  • The concept of a Monte Carlo simulation,
  • Principles of Bayesian statistics,
  • Elements of time series analysis.
• Introduction to Machine Learning
  • Supervised learning, regression and classification,
  • Discrete choice models + unbalanced sample,
  • Decision trees, random forests, prunning,
  • Empirical and structural risk (SLT), overfitting,
  • Artificial Neural Networks,
  • Support Vector Machines,
  • High dimensional statistics,
  • Unsupervised learning,
  • Concept of Deep Learning and General Purpose AI,
  • Elements of Natural Language Processing,
• Introduction to programming with R
  • R and R Studio,
  • Reading data into R,
  • Working with data frames and R functions,
  • R functions: some syntactical concepts,
  • Drive Data Manipulation , Preparation and Analytics,
  • Classification techniques,
  • Regression techniques,
  • Cluster analysis,
  • Time series analysis,
  • Apply R on 2 solved problems (internal/external data),
  • Apply R on 2 unsolved problems of choice (internal/external data).

Audience

People who work with data scientists or who want to acquire understanding of fundamentals of data science, with a practical focus on applications of such methods typical for the banking industry.