Applied Statistics and Computer Science for Chemists

Regarding the course Applied Statistics and Computing for Chemists, the students shall be able to solve qualitative and quantitative problems following models previously developed, to recognize and analyse new problems and plan strategies to develop them.

Introduction to the theory and applications of statistics. Uncertainty analysis in experimental measurements. Hypothesis testing. Calibration and regression. Computing-assisted treatment of experimental data. Simulation and method validation. Application of statistic techniques, by using computers, to the analysis of real or simulated data. Handling of mathematical software designed for Chemistry.

For organization purposes, these contents are distributed in the following programme:

PROGRAMME OF LECTURES:
Unit 1: Introduction to programming with Python.
Unit 2: Use of Python to solve numerical problems in chemistry.
Unit 3: Descriptive statistics
Unit 4: Estimation and confidence intervals
Unit 5: Hypothesis testing.
Unit 6: Linear regression. Calibration.

PROGRAMME OF PRACTICAL LESSONS IN THE COMPUTING LAB:
Different software tools will be used to solve problems and perform case studies related to the topics explained during the theoretical lessons.

Basic Biblography:

- (*) Hinojosa Gutiérrez, A.P.: "Phyton: paso a paso"; RA-MA Editorial, 2016.
- Blanco, M.; Cerdà, V. (Eds.): "Temas avanzados de quimiometría", Universidad de las Islas Baleares, 2007.
- Crujeiras, R.M.; Faraldo, P.: "Manual de estadística básica para ciencias de la salud", Universidad de Santiago de Compostela, 2010.
- Miller, J.N.; Miller, J.C.; Miller, R.D.: "Statistics and chemometrics for analytical chemistry, 7th Ed.", Pearson, 2018. (Also available as an e-book at the University Library website-Catálogo Iacobus)
- Mongay Fernández, C.: "Quimiometría", Universidad de Valencia, 2005. (Also available as an e-book at the University Library website-Plataforma PreLo)
- Ramis Ramos, G.; García Álvarez-Coque, M.ªC.: "Quimiometría", Síntesis, 2001.

(*) This book is especially recommended for Units 1 and 2.


Complementary Bibliography:

Additional references will be recommended for specific topics during the course, if needed.

BASIC AND GENERAL COMPETENCES:
CB1 – Increase the scientific knowledge that the students acquired in the secondary school, e.g. with the help of advanced university books, and acquire some information about the last trends in the chemistry field.
CG3 – Apply theoretical and practical knowledge, the analytical and abstraction skills to the definition and solution of problems that can be found in the academic and in the professional environment.
CG5 – Improve autonomous learning, organization of time, information, new knowledge and techniques that are used in any scientific or technological field.

TRANSFERABLE COMPETENCES:
CT1 – Improve the ability to synthesize and analyze information.
CT4 – Improve problem solving skills.
CT10 – Acquire critical thinking skills.
CT12 – Acquire autonomous learning skills.

SPECIFIC COMPETENCES:
CE14 – Solve qualitative and quantitative problems following models previously developed.
CE15 – Recognize and analyse new problems and plan strategies to solve them.
CE25 – Establish relationships between chemistry and other fields.

The following teaching methodologies will be used during the lessons:
i) Lectures (denoted as "L" in the teaching schedule): theoretical lessons to explain the foundations of each topic, that will be illustrated with examples and related problems. The explanations will be supplemented with basic teaching resources and new technologies: use of the virtual platform, videos, slide presentations, Internet access...
ii) Interactive sessions or Seminars (denoted as "S" in the teaching schedule): theoretical/practical lessons to solve exercises and analyze case studies related to the topics explained during the lectures.
iii) Interactive sessions in the computing lab (denoted as "G" in the teaching schedule): practical lessons using the computer, to solve exercises and analyze case studies related to the lectures and seminars, with the help of different software tools.
iv) Group tutorials or Tutorials (denoted as "T" in the teaching schedule): theoretical/practical lessons to clear up points arising from the lectures and seminars.

The final mark will consist of two parts: continuous assessment and final examination.

The final mark obtained will not be lower than that obtained in the final examination, even after doing the calculations with the following percentages:

i) Continuous assessment: 30 %, divided into the following parts:
i.1) Attendance and participation in seminars and tutorials: 5 %
i.2) Attendance and participation in computing lab sessions: 25 %

ii) Final exam: 70 %

In case of cheating on exercises, tests or exams, the “Normativa de avaliación do rendemento académico dos estudantes e de revisión de cualificacións” will be applied.

The attendance to the seminars and group tutorials is compulsory. The attendance and execution of the problems proposed in the computing lab is compulsory to pass the course, both in the regular examination and in the second examination call. A minimum mark could be established by the lecturers to pass the computing part.

All the students enrolled in the subject must attend to the computing lab classes, except for the repeating students who have already attended in previous academic years (if the teachers have that agreement). These students can attend to the computing lab classes at their will.

The assessment adquisition of the competences:
i) In the Lectures, Seminars, Sessions in the computing lab and Tutorials, the following competences will be assessed: CB1, CG3, CG5, CT1, CT4, CT10, CT12, CE14, CE15 and CE25.
ii) In the examination, the following competences will be assessed: CB1, CG3, CG5, CT1, CT4, CT10, CT12, CE14, CE15 and CE25.

FACE-TO-FACE LEARNING: 51 h, consisting of:
Lectures: 21 h
Interactive lessons in smaller groups (Seminars): 8 h
Interactive lessons in the computing lab: 20 h
Group tutorials: 2 h
Autonomous learning: 99 h

It is recommended to:

1) Study regularly during the term.
2) Ask questions to solve doubts.
3) Try to solve the set of problems proposed by the lecturer before the seminars to check the acquisition of new knowledge.
4) Use the software recommended during self-study hours.