Data Analysis and Visualization in R
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2339 is a semester module in English language at Bachelor’s level and Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
|Total workload||Contact hours||Credits (ECTS)|
|180 h||90 h||6 CP|
Content, Learning Outcome and Preconditions
R programming basics 2 (including report generation with R markdown)
Cleaning and organizing data: Tidy data 1
Cleaning and organizing data: Tidy data 2
Grammar of graphics 1
Grammar of graphics 2
Unsupervised learning (hierarchical clustering, k-means, PCA)
Case study I
Drawing robust interpretations 1: empirical testing by sampling
Drawing robust interpretations 2: classical statistical tests
Supervised learning 1: regression, cross-validation
Supervised learning 2: classification, ROC curve, precision, recall
Case study II
- 1. produce scripts that automatically generate data analysis report
- 2. import data from various sources into R
- 3. apply the concepts of tidy data to clean and organize a dataset
- 4. decide which plot is appropriate for a given question about the data
- 5. generate such plots
- 6. know the methods of hierarchical clustering, k-means, PCA
- 7. apply the above methods and interpret their outcome on real-life datasets
- 8. know the concept of statistical testing
- 9. devise and implement resampling procedures to assess statistical significance
- 10. know the conditions of applications and how to perform in R the following statistical tests: Fisher test, Wilcoxon test, T-test.
- 11. know the concept of regression and classification
- 12 apply regression and classification algorithms in R
- 13. know the concept of error in generalization, cross-validation
- 14. implement in R a cross-validation scheme.
- 15. know the concepts of sensitivity, specificity, ROC curves
- 16. assess the latter in R
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Data Analysis and Visualization in R (IN2339)||Gagneur, J.||
Tue, 14:00–16:00, virtuell
|UE||4||Exsercise Data Analysis and Visualization in R (IN2339)||Gagneur, J.||
Wed, 14:00–17:00, Interims II 004
Thu, 08:00–11:00, MW 0608m
Fri, 08:00–10:00, MW 2001
Wed, 13:00–15:00, MI 00.08.038
Wed, 11:00–13:00, MI 00.08.038
Thu, 12:00–14:00, MI 00.08.038
Thu, 08:00–10:00, MI 01.10.011
Tue, 16:00–18:00, MI 00.13.009A
Thu, 14:00–16:00, MI 00.13.009A
Wed, 10:00–12:00, GALILEO Taurus
Learning and Teaching Methods
with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
R for Data Science, by Garrett Grolemund and Hadley Wickham
Description of exams and course work
The listed achievements, see Intended Learning Outcomes, are evaluated by one written exam of 90 min. There will be moreover two case studies, where the students must provide the source code that generates the report of an analysis of a given dataset. The analysis of this data covers all topics stated under Intended Learning Outcomes. The first case study covers topics 1-7. The second covers the topics 8-16. The final mark is the exam mark with bonus points for the two case studies.
The exam may be repeated at the end of the semester.