Course objectives
Developing skills for collecting, recognizing and performing basic analysis and classification of data. Getting acquainted with basic methods for statistical analysis, time-series analysis, and sensitivity analysis. Understanding methods of artificial intelligence such as artificial neural networks and classification methods. Acquiring experience in managing and analyzing large, complex data structures, complex systems and stochastic systems. Adopting the knowledge needed to apply analytical methods to technical systems and data. Understanding the basic ideas of substitute (surrogate) models and mastering the skills required for their implementation and use.
Expected course learning outcomes
Independently implement a computer program for data collection and filtering. Perform basic statistical data analysis and filter and correct errors in data. Use artificial intelligence (artificial neural networks and classification methods) in data analysis. Create moderately complex computer programs for time-series analysis, complexity and sensitivity analysis and visualization of analytical results. Model and analyze stochastic systems.
Course content
Data collection. Basic statistical analysis. Data errors and data filtering. Interpolation of data by using artificial neural networks. Classification of data. Analysis of time-series and dynamic systems. Methods for analyzing system sensitivity and chaosiness. Complex systems and big data. Probabilistic and stochastic systems. Basic surrogate models and their implementation. Analytical visualization. Examples in engineering.