Data modeling for materials engineering, processing, properties and durability
The module explores data-driven methodologies with a strong focus on understanding how to choose, apply, and interpret appropriate statistical and Machine Learning approaches able (a) to unveil patterns in data and (b) learn from them.
Topics covered include introduction to statistical data analysis and machine learning approaches, understanding the importance of identifying relationships hidden in data, application of modelling approaches (i.e. supervised vs. unsupervised learning), model building steps, evaluation of models, etc. The module will also equip participants to use R, a well-known statistical programming language, for executing data-driven solutions.
- Will gain a depth of statistics and data modelling skills necessary to apply high-level analytical thinking to data analysis problems.
- Will become familiar with the main concepts in statistical data analysis.
- Descriptive statistics, exploration and graphical representation of data
- Statistical inference with parametric and non-parametric methods
- Will acquire data analytics skills using ML algorithms (supervised learning tasks)
- Will develop the ability to build and assess data-driven models.
- Will develop computer programming skills in the statistical programming language R & RStudio and, thus, the ability to quickly acquire technological know-how demanded on data analytics related job roles.
- Will apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively.