Study programme 2018-2019 | Français | ||
Data Sciences V : reproducible research | |||
Programme component of Master's Degree in Biology of Organisms and Ecology Research Focus à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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US-M2-BIOEFA-015-M | Optional UE | GROSJEAN Philippe | S807 - Ecologie numérique des milieux aquatiques |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 10 | 10 | 0 | 0 | 0 | 2 | 2.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
S-BIOG-077 | Data Sciences V : reproducible research | 10 | 10 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
To specialize students in biology in biological data science through their initiation to various complementary concepts to previous courses. This course supplements data science concepts taught until now with various more advanced topics: floating-point calculation precision, best coding of data in relation with the current problem, how to realize perfectly reproducible analyses, reproducible pseudo-random generators, pre-sorting of the data with SQL commands. This course is partly modular in function of specific needs of students.
Content of UE
Data management; databases; SQL queries; S language (software R) with RStudio; floating-point calculation; pseudo-random numbers generation; reproducible analysis; unit tests; data formats; optimization of calculation speed; optimization of RAM used; vectorized algorithms, ...
Prior Experience
General knowledge in data science, including project management, data importation and transformation, visualization of data through graphs and bases of writing reproducible reports. Advanced biostatistics in main areas used in biological data analyses.
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
Evaluation according to questions and answers durting an oral examination.
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Evaluation according to questions and answers durting an oral examination.
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not appliable.
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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S-BIOG-077 |
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Mode of delivery
AA | Mode of delivery |
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S-BIOG-077 |
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Required Reading
AA | |
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S-BIOG-077 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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S-BIOG-077 | Not applicable |
Recommended Reading
AA | |
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S-BIOG-077 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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S-BIOG-077 | Not applicable. |
Other Recommended Reading
AA | Other Recommended Reading |
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S-BIOG-077 | Barnier, J., 2018. Introduction à R et au tidyverse (https://juba.github.io/tidyverse/index.html). Ismay, Ch. & Kim A.Y, 2018. Moderndive: An introduction to statistical and data science via R (http://moderndive.com). Wickham, H. & Grolemund, G, 2017. R for data science (http://r4ds.had.co.nz). Chambers, J.M., 2008. Software for data analysis. Programming with R. Springer, New York, 498pp. Dagnelie, P., 2007. Chambers, J.M., 1998. Programming with data. A guide to the S language. Springer, New York, 469pp. Fortner, B., 1995. The data handbook. A guide to understanding the organization and visualization of technical data. Springer, New York, 350pp. |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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S-BIOG-077 | Authorized |