(in Polish) Reproducible Research
General data
Course ID: | 2400-QFU1RR |
Erasmus code / ISCED: | (unknown) / (unknown) |
Course title: | (unknown) |
Name in Polish: | Reproducible Research |
Organizational unit: | Faculty of Economic Sciences |
Course groups: |
Obligatory courses for Quantitative Finance, 1st year |
ECTS credit allocation (and other scores): |
3.00
|
Language: | English |
Type of course: | obligatory courses |
Short description: |
The main goal of the course is to present the basic concepts of reproducibility and repeatability in research, their significance in scientific and commercial research and development processes, and to impart to students basic practical knowledge about several of the most popular modern tools for reproducibility in the industry. Upon completion of the course, students will understand the general concept of research reproducibility and comprehend which tool can be used in a given context. They will also acquire skills in using computer tools that enable achieving reproducibility and repeatability of research, and they will be able to apply the skills acquired during the course in participating in modern scientific and commercial data science projects. |
Full description: |
1. Introduction: The three Rs: Repetition, Reproduction, Replication; Importance of reproducibility in science and the R&D process; Reasons for and consequences of lack of reproducibility; Some ways of handling non-reproducible research; Course grading overview. 2. Version control systems: Introduction to VCSs and git; Using git for version control and progress documentation; Teamwork via git; Working with GitHub; Project workflow; GitHub as the course repository and as ‘home’ for final projects. 3. Reporting tools: Introduction to Quarto and Markdown; Reproducible and automated reports; Reports with data inputs; Other formats 4. Writing reproducible code: Documenting code and versioning; Tools for managing software versions; Principles of writing clean and clear code; 5. Introduction to code testing 6. Introduction to online repositories 7. Introduction to metaanalyses |
Bibliography: |
Lecture slides or notebooks Numerous online resources |
Learning outcomes: |
Upon the completion of the course, student: 1. understands the general concept of research reproducibility; knows the reproducibility tools classification; understands which tool can be used in a given context; 2. has basic skills in computer tools allowing to achieve research reproducibility and replicability; has basic skills in modern best programming practices; has basic skills in the cloud development environment; is able to employ skills gained during the course while participating in modern scientific and commercial data science projects; is aware of the importance of reproducibility in data science, as well as in science and development in general; is aware that reproducibility tools are evolving rapidly and that constant training in this area is required to keep skills up to date; is aware of the trends in modern data science and IT development; |
Assessment methods and assessment criteria: |
1. Active participation in the classes 2. Final project and its presentation (in teams) |
Classes in period "Summer semester 2023/24" (in progress)
Time span: | 2024-02-19 - 2024-06-16 |
Navigate to timetable
MO KON
KON
TU W TH FR |
Type of class: |
Seminar, 30 hours
|
|
Coordinators: | Wojciech Hardy, Jakub Michańków | |
Group instructors: | Wojciech Hardy, Jakub Michańków | |
Students list: | (inaccessible to you) | |
Examination: |
Course -
Grading
Seminar - Grading |
Copyright by University of Warsaw.