Pārlekt uz galveno saturu

Šā gada oktobrī, novembrī un decembrī notiks RSU Doktorantūras skolas vebināru cikls Mastering your Data – from Exploration to Visualization.

Darba valoda: angļu

Par lektoru

sergio_uribe.jpgProf. Sergio Uribe is a Lead Researcher at the RSU Bioinformatics Research Unit, a visiting Associate Professor at the RSU School of Dentistry, and an Associate Professor at the Universidad Austral de Chile in Valdivia. Prof. Uribe has dedicated his life to scientific research investigating how to improve people's oral health, either through interventions, by identifying risk factors that can be modified or by improving the diagnostic accuracy and usefulness of maxillofacial imaging tests.

Prof. Uribe works with researchers from different countries and the results of their research have been disseminated in more than 50 publications in high impact journals and book chapters. He is a dentist and specialist in Maxillofacial Radiology at the University of Valparaiso, Chile and has a PhD from the Universidad Austral de Chile, Valdivia.

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Par kursu

The traditional approach to research programmes is to assume that students will find a way to analyse and visualise their data. This assumption brings problems for the students, their supervisors and a significant waste of time. Many students are scared by the data rather than curious and usually skip exploratory data analysis and go straight to advanced statistical models that they cannot explain later because they do not understand their data in depth. This course aims to provide basic knowledge, skills and tools to perform such an exploratory data analysis, with a major focus on publication-ready data visualisation to detect patterns and trends in the data, to extract meaningful information from the data and to prepare for further inferential analysis.

On successful completion of the course, the students will have the knowledge and practical skills to successfully apply the R statistical software and its essential functions and packages to wrangle and transform their research data to perform informative exploratory analyses and perform publication-ready visualisation of their data, enabling effective interpretation and communication of the research results and findings to the scientific community.

Course page and more information

Vebināri šajā ciklā

27/1015.00–16.30

2. vebinārs: Data Visualization II: How

geom_points; geom_histogram; geom_col; geom_bar; facets; Locating and dealing with NA

11/1115.00–16.30

3. vebinārs: Data Wrangling I: Why

Filter; Select; Mutate; Summarise I; Arrange; lubridate

25/1115.00–16.30

4. vebinārs: Data Wrangling II: How

Pivoting; group_by; Summarise II; gtsummary

15/1215.00–16.30

5. vebinārs: Data Management

GIT, OSF; Data workflow: data validation, data form entry, when and when not to use spreadsheets; Files naming; Basic of coding management: tidylog; Basic of data cleaning: janitor; Good code practices and data sharing Codebook (dataMaid)

22/1215.00–16.30

6. vebinārs: Data Final project presentations

Student presentations, 15 minutes per project, max 6 projects

Aizvadītie vebināri šajā ciklā

14/10

1. vebinārs. Data Visualization I: Why

Introduction to R; The grammar of graphics: data, geoms, aes; Basic visualisations; How to deal with errors

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