Data science with R, RMarkdown, RStudio, Git and GitHub 0800-OG-DATAR

(po angielsku) Contact hours with teacher (obligatory: 30 hrs, voluntary: 10 hrs): - participation in laboratories – 30 hrs - consultations – 10 hrs Self-study hours (35 hrs): - preparation for the laboratory session (watching the provided video and reading the supplied material) – 15 hrs - writing and reviewing assignments (projects, reports, etc.) – 20 hrs Altogether: 75 hrs (3 ECTS)

(po angielsku) Student W1: has comprehensive knowledge of the data analysis lifecycle, including preprocessing, exploration, and reporting. W2: understands advanced data transformation and visualization techniques and their impact on analysis outcomes. W3: has a solid understanding of building, validating, and interpreting statistical models. W4: understands how to write robust, efficient, and reproducible scripts using RMarkdown and integrated tools. W5: recognizes the importance of collaboration and communication in data science, leveraging version control tools such as Git and GitHub.

(po angielsku) Student U1: is proficient in importing, cleaning, and transforming diverse datasets, including spatial and textual data. U2: can design, implement, and evaluate statistical models to solve real-world problems. U3: is capable of creating fully reproducible workflows using RMarkdown, ensuring transparency in analyses. U4: is skilled in using Git and GitHub for collaborative data science projects, including managing conflicts and maintaining code quality. U5: can critically assess and incorporate ethical considerations into data analysis and communication processes. U6: is capable of conducting peer reviews of assignments through GitHub Pull Requests, fostering critical thinking and collaboration.

(po angielsku) Student K1: understands the significance of ethical considerations in data analysis. K2: recognizes the importance of reproducibility and transparency in scientific research. K3: is prepared to collaborate effectively using version control systems. K4: is aware of the impact of data visualization on data interpretation. K5: values continuous learning and staying updated with advancements in data science tools and methodologies.

(po angielsku) Expository teaching methods: - online materials (e.g., video lectures, reading materials) Active teaching methods: - practical exercises - group projects - interactive tutorials - peer reviewing

(po angielsku) - Basic understanding of statistics. - Familiarity with at least one programming language is advantageous but not mandatory.

Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb:

• Strona przedmiotu 0800-OG-DATAR w USOSweb