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- R2: Data Analysis with R
R2: Data Analysis with R
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Short Overview
The R statistical language is a valuable tool for data management and mainly for statistical analysis that includes cutting-edge modern methodologies. The fact that R is an open source language where anyone can contribute by developing libraries that implement statistical methodologies has led to an almost frenetic pace of development. At the same time R has high quality graphics making it an important tool in data visualization.
Thus, there is an immediate need to educate and train a large number of graduates in this language who until now have relied on the use of closed-type commercial statistical software. At the same time, the development of methodologies for the analysis of large volumes of data as well as developments in both statistical science and other related sciences lead to the need for training in R and its capabilities.
This program is a logical continuation of the program R#1 - Introduction to R. In this program we deal with data analysis issues and consider practical and real data problems. The purpose of this program is on the one hand to train graduates and executives in data analysis with R but at the same time to offer and teach important concepts of statistics in a correct and systematic way using R as a tool.
In this context, this course offers ways to use R for statistical data analysis in real-world problems, always keeping in mind the applications and modern data management problems that appear in practice.
As part of the seminars, there are 5 three-hour lectures with theory and 5 three-hour workshops where small exercises are given as homework with the aim of both getting familiar with R data analysis files and solving practical problems with real data.
Learning Goals
At the end of the unit the learner will be able to:
- To use the R language.
- Analyze data and highlight relationships between variables.
- Apply linear models based on which to make predictions and interpret real phenomena.
Weekly Schedule
The program lasts 5 weeks. Each week there will be a theoretical lecture and a laboratory lesson. All classes last three hours and will be held on Tuesday (18:00 - 21:00) and Friday (18:00 - 21:00)
Teaching Material
Slides, notes, weekly homework, and online bibliography.
Evaluation Method And Final Grade Computation
The assessment is done by submitting exercises and assignments on a weekly basis and an examination with multiple choice questions and a limited number of open answers. In case of failure, the trainee can be re-examined (the re-examination will take place within a period of less than 3 weeks). The conditions for awarding of the training certificate are the successful examination through the final examination and the delivery of at least three laboratory exercises.
Learning Method Description
Trainees participate through Live Streaming from the place of their choice. There are 5 three-hour theory lectures and 5 three-hour workshops. In each workshop there is a small homework exercise. Indicative days and times (which are always in the afternoon) are Friday (18.00-21.00) and Tuesday 18.00-21.00 (Laboratory).
Program Website
Application deadline: 05/11/2024
Program start: 12/11/2024
Program completion: 20/12/2024
Scientific area
Way to follow
Other important information
An additional 25% discount will be given to students who have taken the "Data Analysis Using R" or "Advanced Data Analysis Using R" eLearning programs.
ECTS units: 2
Hours of live training: 30
Includes training hours with classroom, laboratory, or remote (via live streaming) instruction along with any breaks.
Includes training hours with classroom, laboratory, or remote (via live streaming) instruction along with any breaks.
Additional hours of employment: 30
Additional hours of work are included (indicative) and may include individual study, writing assignments, participation in field visits, participation in exams, etc.
Additional hours of work are included (indicative) and may include individual study, writing assignments, participation in field visits, participation in exams, etc.
Total hours: 60
The total hours include hours of synchronous and asynchronous training, as well as additional hours of employment.
The total hours include hours of synchronous and asynchronous training, as well as additional hours of employment.
Weeks of training: 5
Weeks in which activities of any kind are planned are included.
Weeks in which activities of any kind are planned are included.
Vocational Education and Training Certificate
Trainees who successfully complete the program are granted a Vocational Education and Training Certificate of the Center for Education and Lifelong Learning of the Athens University of Economics and Business, which is accompanied by a Supplement to the Certificate, detailing the subject of the program, the thematic units attended by the trainee , as well as the training methodology followed.
Target Audience
- Current students in statistics or related departments who want to learn the R language that is not necessarily taught beyond the Statistics department.
- Graduates of Statistics departments from previous years where R was not widespread and not taught.
- Graduates of Statistics departments from recent years who were either not taught R or would like to delve deeper.
- Graduates of Mathematics departments who, while they have been taught theoretical courses, have little familiarity with the use of statistical programs such as R.
- People who already work and need to analyze data and use R.
- Researchers from various other fields who need both statistical methods and the use of R for their research.
Prerequisite Knowledge
Those admitted to the course must:
- have a prior knowledge of statistics (have been successfully taught, for example, a course).
- have knowledge of using computers.
- have sufficient knowledge of English (Lower level and above).
Scientific Responsible
Program Tutors
Mode And Frequency Of Communication
Via email or over the phone
Cost
LIVE STREAMING: 400€
Trainees are informed of the installment amounts and related deadlines via email upon their acceptance into the program.
CONTACT
46, Kefallinias Str., 11251, Athens
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dummy kedivim-opa@aueb.gr
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dummy+30 210 8203 912
For the in Class programs:
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dummydz@aueb.gr
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dummy+30 210 8203 916, 912, 914
For the eLearning Programs:
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dummyelearning@diaviou.aueb.gr
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dummy+30 210 8203 753
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