Behavioral Data Analysis in R

When we have all data online it will be great for humanity. It is a prerequisite to solving many problems that humankind faces.

The course ‘Behavioral data analysis in R’ is aimed at helping students to understand the whole data life cycle and the many aspects of the data computing environment.


This track builds upon the knowledge from the ‘Introduction to R’ course and focuses on how to use R during the whole data life-cycle. We will show you how to prepare data, how to do exploratory visualization and analysis, how to conducts statistical tests and also how to visualize the results. And above all, how these steps interact and why they usually cannot stand without each other. You can be sure that after finishing the track you will see data-intensive research as less complicated and quicker than ever before.

ECTS
4
Level
Advanced Course
Site
Face-to-Face & Online

Opportunies to attend this course

Lecturers

Lukáš Hejtmánek

CEBEX
Czech Republic

Course details

Topics covered


- Control Structures

- Data Loading

- Data Preprocessing

- Data Merging

- Data Cleaning

- Exploratory Analysis

- Visualizations in Ggplot

- Introduction to Statistics

- Analysis of Dependence

- Comparing Two Means

- Comparing Mutliple Means

- Reporting Results

- Combining it All Together


Schedule


Each lecture lasts 60 minutes. Morning lectures start at 8:30 and finish at 12:20. Afternoon lectures start at 13:30 and finish 17:30. If not set otherwise, the break after each lecture lasts 30 minutes. All times are in Central European Time (CET).

For students who participate face-to-face, there will be some icebreakers on August 8. Therefore for those who only have an advanced course (no crash course), the summer school starts on August 8.


ECTS requirements


To obtain the ECTS, a student has to get in total at least 60% of all available points which are being awarded for the following activities:

- attend all lectures (20 hrs. of WL / 1 pt. per hour, 20 in total)

- submit 4 homeworks (10 hrs. of WL / 2.5 pts. per homework, 10 in total)

- final exam (30 hrs. of WL / 30 pts.)

- final project (40 hrs. of WL / 40 pts.)


Literature


Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications.

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