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Overview
The course introduces students to the statistical programming language R and the use of R studio. We will cover the concepts of data manipulation and data preparation as well as uni- and bivariate statistics in R.
We will cover the so-called grammar of graphics in R with the ggplot2 package to create stunning and publication-ready data visualizations. We will also discuss how to conduct basic descriptive statistics (such as mean, standard deviation, correlation) in R to describe your data.
Our main focus will be the discussion of a selection of machine learning algorithms and their implementation in R. We will for example try to model the factors that influenced the survival of the Titanic passengers, predict customer churn for a telecommunications company and try to classify traffic signs based on images.
The course is designed to give a robust theoretical understanding of the methods and allow students to use the algorithms with real-world data sets.
Programme Structure
Aims of the curriculum:Introduction to the statistical programming language R and the use of R studioData manipulation and data preparation in RUni- und and bivariate statistics in RGrammar of graphics in R (with ggplot2) to produce publication-ready graphicsTheoretical understanding of selected machine learning algorithms (e.g. logistics regression, decision trees and random forests, k-nearest-neighbours, hierarchical cluster analysis)Practical application of selected machine learning algorithms in R
Lecturers
Daniel Hoppe
Key information
Duration
- Full-time
- 10 days
Start dates & application deadlines
- StartingApply anytime.
Language
English
Credits
4 ECTS
Assessment criteria: written assignment (10 – 15 pages of text plus R code), application of uni- and bivariate statistics, graphical visualization and (at least) one machine learning algorithm to be applied to a data set.
Delivered
On Campus
Disciplines
Data AnalyticsAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Student insurance
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- Additional medical costs (i.e. dental)
- Repatriation, if something happens to you or your family
- Liability
- Home contents and baggage
- Accidents
- Legal aid
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Other requirements
General requirements
Generally, anyone interested in learning the statistical programming language R for data analysis and application of machine learning algorithms are welcome to apply. Specifically:* aspiring Bachelor students (after successfully passing the statistics course)* master students / PhD students.No previous knowledge in R is required. However, basic statistical knowledge (descriptive and analytical statistics) is recommended.Students should bring their own laptop (Windows or Mac) and have R and R studio installed. Details on how to install R and R Studio will be provided.
Tuition Fee
To always see correct tuition fees
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International
450 EUR/fullTuition FeeBased on the tuition of 450 EUR for the full programme during 10 days.
Early-Bird Course Fee (until 31 March ) - 400€
Regular Course Fee (after 31 March ) - 450€
Living costs for Tallinn
598 - 948 EUR /month
Living costs
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
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