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# Informatica II

Course director

Course director

## Description

This course builds on the introduction to R in Informatica I and Statistica I. It furthermore makes use of some of the concepts presented in Statistica I and Matematica I+II such as distributions, matrix algebra or functional analysis.

The course covers the following topics:

1. The R language
2. Calculating: Operators
3. Efficient programming: Functions
4. Repeating and deciding: Flow control
5. Solving problems: Algorithms
6. Finding the best solution: Optimization
7. A world of models: Applications of linear algebra
8. Working with data: Frames
9. More than thousand words: Introduction to data visualization
10. All possibilities: Introduction to simulation
11. From A to Z: Writing an entire research report in R

• A short introduction to the blockchain, cryptocurrencies and NFTs
• An introduction to LaTex

Required Material

• Students are expected to bring a laptop with a recent version of R and Rsudio installed to class. The two programs are free and can be obtained here: www.r-project.org and www.rstudio.com
• All slides and sample programs will be distributed on the iCorsi platform.
• The official Introduction to R is available free of charge from cran.r-project.org/doc/manuals/R-intro.pdf
• Students will furthermore get free access to Datacamp for tutorials and to the Autograder platform for submitting exercises
• This course is supported by www.datacamp.com

## Objectives

Being able to write a simple program is a skill that every economics student should posses. Programming is not very difficult – a program is just a list of precise instructions that the computer will follow – and it can be quite some fun. Most important: a little programming helps you to get your work done much faster.

The goals of this course are

• Learn a simple programming language: the R language
• Manage data with R and learn how to use it as statistics/econometrics program
• Understand why problems are solved with the computer differently than with pen and paper
• Learn a few standard algorithms for numerical problem solving
• Add a few further useful skills to the toolstack of students

After this course, students should know enough R to solve simple econometric, financial, or economic problems and have a good basis for writing code for an empirical topic in their Bachelor thesis.

In presence

## Learning methods

Programming is a both a matter of understanding and of practise. The main idea of the course is to accompany students form theory to practise in simple steps. New topics are introduced with short tutorials (video or interactive) that should be followed at home before class starts. One lecture per week is then reserved to discussing new concepts and how to apply them. Students have then one week to solve small programming exercises, which are submitted to the new Autograder system that gives immediate feedback and gives every student a second (and even third) chance to submit the correct solution. The results of these exercises and further applications are finally discussed in a dedicated exercise session.

The course is entirely taught in English.

## Examination information

• 15% Completion of Datacamp tutorials, grading based on % completed by deadline
• 25% Assignments on Autograder, grading based on correctness of result, difficulty of problem. Bonus for finding the answer with fewer tries.
• 60% Final exam. Format: “empty program” to be filled in + few short questions