 | Study programme 2021-2022 | Français | |
 | Bioinformatics and data sciences | ||
Programme component of Bachelor's in Biology (Charleroi (Hor. jour)) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-B2-SCBIOC-926-C | Compulsory UE | CONOTTE Raphael |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 0 | 70 | 0 | 0 | 0 | 5 | 5.00 | Année |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-BIOG-921 | Bio-informatics and data sciences A | 0 | 35 | 0 | 0 | 0 | Q1 | |
| S-BIOG-970 | Bio-informatics and data sciences B | 0 | 35 | 0 | 0 | 0 | Q2 |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Acquire, understand and use knowledge in the fields of biology and other fields
- Understand and use mathematical tools and basic statistics to describe and understand biological concepts
- Integrate knowledge from other fields of knowledge with biology (earth science, physics, chemistry, mathematics), in a critical way, to foster an interdisciplinary approach
- Solve issues relevant to biology
- Analyse and interpret, in an appropriate way, biological data collected in natura, through dissection or based on an experimental protocol in the laboratory
- Apply a scientific approach and critical thinking
- Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, rule of three, syllogism, analogy, etc.)
- Understand the statistical and/or probabilistic methods
- Work with efficiency / accuracy / precision
- Present a hypothesis and hypothetical-deductive reasoning
- Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
- Manage doubt and uncertainty
- Communicate effectively and appropriately in French and English
- Communicate in French, orally and in writing, the results of experiments and observations by constructing and using graphs and tables
- Develop autonomy, set training objectives and make choices to achieve them
- Organise time and work, individually and in groups
- Prioritise
- Manage stress regardless of events (exams, presentations, etc.)
Learning Outcomes of UE
- To master software and statistical tools required for data science, more particularly, data importation, management and transformation, data visualization and inference.
- To present results clearly and adequately in a scientific report.
- To be able to analyze correctly usual biological data in practice.
Content of UE
The chapters of this UE are:
- Introduction - Software & tools (Software R, RStudio, git & Markdown)
- Visualisation I - Scatterplot
- Visualisation II - Distributions
- Visualisation III - Barplot/boxplot
- Data processing I - Importation/conversion
- Data processing II - Contingency/sampling
- Data processing III - Multi-tables/databases
- Experimental design & good practices
- Probabilities & distributions
- Chi-2 test, proportions & correlations
- Confidence interval/Student test
- Analysis of variance
Prior Experience
- Basic use of a computer.
- Bases in calculus, including logarithm and exponential, cartesian coordinate system and elementary geometry in 2D and 3D.
Type of Assessment for UE in Q1
- Presentation and/or works
- Practical test
- Graded tests
- eTest
Q1 UE Assessment Comments
Final grade made of different parts:
- Continuous evaluation of the progression
- Participation in flipped classes
- Output during practical sessions
- Evaluation of a report of data analysis
- e-test.
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q2
- Presentation and/or works
- Practical test
- Graded tests
- eTest
Q2 UE Assessment Comments
Final grade made of different parts:
- Continuous evaluation of the progression
- Participation in flipped classes
- Output during practical sessions
- Evaluation of a report of data analysis
- e-test.
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q3
- Presentation and/or works
- Practical Test
- Graded tests
- eTest
Q3 UE Assessment Comments
Final grade made of different parts:
- Continuous evaluation of the progression
- Participation in flipped classes
- Output during practical sessions
- Evaluation of a report of data analysis
- e-test.
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
Not applicable
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-BIOG-921 | |
| S-BIOG-970 |
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-BIOG-921 |
|
| S-BIOG-970 |
|
Required Reading
| AA | |
|---|---|
| S-BIOG-921 | |
| S-BIOG-970 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-BIOG-921 | Not applicable |
| S-BIOG-970 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-BIOG-921 | |
| S-BIOG-970 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-BIOG-921 | Not applicable |
| S-BIOG-970 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-BIOG-921 | 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). Cornillon, P.A. Et al, 2008. Statistiques avec R. Presses Universitaires de Rennes. 257pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. Venables W.N. & B.D. Ripley, 2002. Modern applied statistics with S-PLUS (4th ed.). Springer, New York, 495 pp. Legendre, P. & L. Legendre, 1998. Numerical ecology (2nd ed.). Springer Verlag, New York. 587 pp. |
| S-BIOG-970 | 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). Cornillon, P.A. Et al, 2008. Statistiques avec R. Presses Universitaires de Rennes. 257pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. Venables W.N. & B.D. Ripley, 2002. Modern applied statistics with S-PLUS (4th ed.). Springer, New York, 495 pp. Legendre, P. & L. Legendre, 1998. Numerical ecology (2nd ed.). Springer Verlag, New York. 587 pp. |