Statistics with R
Introduction to descriptive statistics with R
What will you learn?
At the end of the course, you, the student, will have acquired basic knowledge of descriptive statistics to understand the structure of data, describe it, detect general patterns and identify errors or unexpected data.
In addition, you will have used one of the most common and future-oriented tools in the area of Big Data, developed by and for data analysis, as the environment and programming language R.
- R: Introduction. First approach to the environment/programming language R. Interfaces, packages and data loading.
- The statistical design. First stage of the statistical method where the research to be carried out is defined: target population, sample sizes, data collection mechanisms, variables to be measured, etc.
- Univariate exploratory analysis of the data. Discrete (frequency tables, sector and bar charts) and continuous variables (grouped frequency tables, numerical measurements, histograms and box diagrams).
- Bivariate exploratory data analysis. Two qualitative variables: numerical analysis (contingency tables, Pearson’s Chi-square contrast) and graphical analysis (grouped frequency plots and mosaics). One qualitative and one quantitative variable: numerical analysis (means-equality contrasts, ANOVA) and graphical analysis (box plots and means-error bar charts). Two quantitative variables: numerical analysis (regression and correlation) and graphical analysis (matrix of scatter diagrams)
- Reporting. Reporting of conclusions for decision making.
The course takes place on the NeuroK online platform. A platform based on the principles of neurodidactics, which promotes participation and collaboration as the core of learning.
And what does this learning model consist of? Here we explain the methodology of the course:
- The teacher will publish the basic contents of each learning unit and propose areas of research.
- The student should look for related content and, after analyzing them, share them with the learning community.
- The student must also evaluate (through critical opinion) the content shared by other members of the community.
- The debate generated is moderated by the teacher’s criteria ensuring that the conclusions of this participation are appropriate.
- Each learning unit includes one or several learning activities to put into practice what is being learned. The activity must be understood as a challenge to be solved.
- The teachers will decide whether to evaluate the activity themselves or by peer evaluation. It’s equally important to deliver an activity as it is to evaluate your peers.
- The teacher’s role is to guide the students in the learning process, looking after and promoting everyone’s motivation.
How and when does it start?
To be confirmed
Hours per week:
The total duration of the course is 4 weeks. With an estimated dedication of the student of 4 hours per week.
One unit per week will be published with its corresponding learning activity. (With the activities we validate learning, these are not an evaluation).
Julio Sanmartino Rodríguez
Julio is a technician and researcher in the Department of Matter Structure, Thermal Physics and Electronics of the Faculty of Physical Sciences.
He is a member of the Membrane and Renewable Energy Research Group (GMER) at UCM and a professor of Big Data at Next International Business School (NextIBS).
Who is this course for?
This course is aimed at those interested in entering the field of statistics for descriptive data analysis out of simple curiosity, as a choice for a future career or as an alternative/complement to their current profession.
Do I need any prior knowledge?
Previous knowledge of the R programming language/environment is not required, although it is advisable to have some basic notions of statistics, although these will be reviewed during the course.