Introduction to Data Science (MSc)

Induction is about learning general principles of the world, also called "parameters", by observing special cases, also called "data". 

In the first part of the course, we will learn about estimation procedures, in particular maximum likelihood and the method of moments, and their theoretical properties. We derive the concept of hypothesis testing, as a decision theoretic tools in an uncertain world. In the second part of the course, we apply the theoretical concepts of the first part to develop and practice data analysis techniques that are central to the daily practice of data science and statistical consulting, namely linear regression, mixed effects models, non-linear regression and generalized linear models. The course will be offered online as well to also allow double-degree Master students to enrol.

• derive properties, such as bias, consistency, sufficiency, efficiency, of estimation procedures; 
• derive and apply maximum likelihood and method-of-moments;
• show the proof for the Cramer-Rao lower bound and the Asymptotic efficiency of MLE; 
• apply hypothesis testing and derive its properties, including the Neyman-Pearson theorem and the asymptotic distribution of the likelihood ratio statistic; 
• derive and apply data analysis techniques, such as linear and non-linear regression, mixed effects models, and generalized linear models. 

• Quality education
• Industry, innovation and infrastructure

In presence

Combination of lectures and tutorials

Midterm (40%) and final written exam (60%). The exams are to be taken in person, also for the students that take the course remotely.