The course covers the pillars of data analysis and statistical modeling, focusing on applications in social sciences, particularly economics and finance, and exploring the main ingredients of econometrics at an intermediate level. Emphasis is put on conceptual issues as well as technical and practical issues. All methods are shown from both the methodological and the computational perspectives. Computing is performed using the R computing language. Practical strategies are constantly introduced and motivated based on rigorous methodological and mathematical treatment. Estimation and inference are based on the least square method. In addition, the finite sample inference is covered based on the Gaussian error assumption. Both classical and modern model validation techniques are explored with a particular emphasis on the predictive viewpoint, which is central to modern data science.Â
