Ateneo - UPO

The R software and its use to represent data and for statistical data analysis.

Reference material is posted on the DIR website. See also
R for Data Science (https://r4ds.had.co.nz/)

*Knowledge and understanding
The course aims to enrich the theoretical knowledge of statistics and probability with appropriate calculation data analysis. Aims of the course is also to display the results. The course in based on the free and open source software R.
*Applying knowledge and understanding
Students should be able to use the acquired skills even in multidisciplinary context and they should be able to analyse data and generate professional graphical representations.
*Making judgements.
At the end of the course students are expected to apply R even in different situations and that they have acquired the tools needed to extend their knowledge by themselves.
* Communication skills
At the end of the course students are expected to be able to express their results both numerically and graphically.
* Learning skills.
During the course students should learn how to study by choosing their personal path and should become able to choose the appropriate resources.

The student should have the basic knowledge of mathematics and statistics

The course is taught in the Computer Lab

Reference material and additional informations are posted on the DIR website.

Ongoing Quizzes and online Final Examination.
The tests will take place on DIR and consist of a series of exercises aimed at addressing issues addressed during the course. The exercises are of various types and may range from simple theoretical questions to more complex one requiring the download of datasets and their analysis. The ongoing tests constitute a benchmark for the preparation for the final exam.

R software. Elementary operations. Functions and graphs. Import, export in R. Data Structures, packages. Matrices, lists, arrays. The data frame. Working with sata: selecting elements, logical operators. Loops and conditionals. Missing data. Additional graphics packages: ggplot2. Regression line and regression curves. Anova. Pharmacokinetic models. Clustering. Simulations.

The student is expected
-to be able to explain the knowledge acquired also in multidisciplinary field and therefore know how to analyze data and create professional graphic representations.
- to be able to apply the methods learned even in different situations and if required have the tools to extend his knowledge in an autonomous way.
- to acquire the ability to express the results of a data analysis both at a numerical and graphic level.
- to acquire the ability to study and learn by choosing his path with originality and must be able to choose resources, possibly even online, useful for his study.