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Educational guide | |||||||||||||||||||||||||||||||||||||||
IDENTIFYING DATA | 2024_25 | |||||||||||||||||||||||||||||||||||||||
Subject | DATA ANALYSIS | Code | 01745001 | |||||||||||||||||||||||||||||||||||||
Study programme |
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Descriptors | Credit. | Type | Year | Period | ||||||||||||||||||||||||||||||||||||
3 | Compulsory | First | Second |
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Language |
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Prerequisites | ||||||||||||||||||||||||||||||||||||||||
Department | MATEMATICAS |
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Coordinador |
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aquic@unileon.es jgomp@unileon.es |
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Lecturers |
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Web | http:// | |||||||||||||||||||||||||||||||||||||||
General description | Data analysis and interpretation of results in Biotechnology and Biomedicine | |||||||||||||||||||||||||||||||||||||||
Tribunales de Revisión |
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Competencies |
Type A | Code | Competences Specific |
A18955 | ||
Type B | Code | Competences Transversal |
B5753 | ||
B5754 | ||
B5755 | ||
B5756 | ||
B5757 | ||
B5758 | ||
B5759 | ||
B5760 | ||
B5761 | ||
B5762 | ||
B5763 | ||
B5764 | ||
B5765 | ||
B5766 | ||
B5767 | ||
B5768 | ||
Type C | Code | Competences Nuclear |
C1 | ||
C2 | ||
C3 | ||
C4 | ||
C5 |
Learning aims |
Competences | |||
To understand and to be able to use the essential mathematical methods to analyse data obtained from biological or medical studies. | A18955 |
B5753 B5754 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5764 B5765 B5766 B5767 B5768 |
C1 C2 C3 C4 C5 |
To update knowledge of computer programmes useful for the mathematical analysis of data. | A18955 |
B5753 B5754 B5755 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5763 B5764 B5765 B5766 B5767 B5768 |
C1 C2 C3 C4 C5 |
To update knowledge related to the editing and presentation of scientific documents with mathematical content. | A18955 |
B5753 B5754 B5755 B5756 B5757 B5758 B5759 B5760 B5761 B5762 B5763 B5764 B5765 B5766 B5767 B5768 |
C1 C2 C3 C4 C5 |
Contents |
Topic | Sub-topic |
Topic I: Introduction | 1. Introduction to data analysis 2. Introduction to R and RStudio |
Topic II: Probability | 1. Probability review, random variables and known distributions 2. Convergence theorems and their relationship with statistical inference |
Topic III: Considerations on study design | 1. Considerations on study design |
Topic IV: Descriptive statistics | 1. Descriptive statistics and exploratory data analysis |
Topic V: Statistical inference | 1. Basics of inferential statistics and study of different perspectives 2. Most commonly used tests in data analysis in biotechnology and biomedicine 3. Regression |
Planning |
Methodologies :: Tests | |||||||||
Class hours | Hours outside the classroom | Total hours | |||||||
Problem solving, classroom exercises | 20 | 20 | 40 | ||||||
Personal tuition | 4 | 4 | 8 | ||||||
0 | 15 | 15 | |||||||
Lecture | 6 | 6 | 12 | ||||||
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Description | |
Problem solving, classroom exercises | On-site practical sessions: Experimental trials, problems or computer simulations. Students will carry out or follow an experimental protocol following good laboratory practices, as well as the guide of good practices for handling experimental animals and/or genetically manipulated microorganisms when necessary. In these sessions, the students and the teacher will discuss the results obtained, analysing their importance and interest. |
Personal tuition | Specialised face-to-face online tutorials through educational support platforms. |
Self-teaching activities: - Analysis of relevant scientific articles on current topics related to the subject. - Preparation of written reports, oral presentations or seminars on experimental or bibiographical work. - Carrying out autonomous tasks online or in a computer classroom. | |
Lecture | Classroom theory sessions: Presentation or discussion of the contents of the subject. |
Personalized attention |
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Assessment |
Description | Qualification | ||
Problem solving, classroom exercises | Attendance at laboratory practicals (attendance and active participation in all training activities). | 30% - 50% | |
Critical review of scientific articles. | 20% - 40% | ||
Others | Project writing, presentation and discussion. | 20% - 40% | |
Other comments and second call | |||
<p>In case a student does not pass the course in the ordinary call, the second call could be evaluated by means of a final exam covering all the contents of the course. Before resorting to the final exam, however, there will be the possibility of recovering some of the parts in the continuous evaluation.</p> |
Sources of information |
Access to Recommended Bibliography in the Catalog ULE |
Basic |
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Complementary |
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http://equator-network.org/ |
Recommendations |
Other comments | |
It is recommended to have taken a course in statistics during undergraduate studies. |