NDAB16012U Modelling and Analysis of Data (MAD)

After the course, the student should have the following knowledge, skills, and competences.

Knowledge of

• Descriptive statistical methods

• Likelihood functions and maximum likelihood estimation

• Least-squares methods, linear regression

• Simple models for classification

• Presentation and validation of machine learning results

• Multivariate statistics

• Presentation of analysis results, including visualization by simple plotting

• Introduction to programming tools for data analysis

• The student will also become familiar with the analytical derivation of algorithms for data analysis

Skills to

• Apply the least-squares method for linear modeling and estimation.

• Analysis of sampled data by appropriate mathematical modeling methods.

• Describe certain useful multivariate methods and their use

• Visualize low- and high-dimensional data with simple plots and images.

• Implement simple data analysis and modeling methods.

• Perform the analysis of experimental data using the methods learned during the course and evaluate the results.

Competencies in

• Building and using simple statistical models, assessing their relevance for solving concrete scientific problems, and quantifying uncertainty about the conclusions drawn.

• Performing basic data analysis tasks which include modeling, visualization, and interpretation of the results.

• Assessing the limitations of the used methods.

• Applying calculus tools, such as partial derivatives, gradients, and integrals.