D1. Knowledge and understanding: At the end of the course the student must demonstrate knowledge of the fundamental principles of analytical chemistry with particular regard to the quality parameters of the analytical result and the treatment of chemical equilibria in aqueous solution.
D2. Ability to apply knowledge and understanding: At the end of the course the student must be able to apply the knowledge acquired in point 1 to solve easy problems and exercises.
D3. Autonomy of judgment: At the end of the course the student must know how to recognize and apply the most basic methods of the treatment of chemical equilibria in aqueous solution and recognize the situations in which such methods can be advantageously used.
D4. Communication skills: At the end of the course the student must be able to clearly explain the concepts acquired in point D1.
D5. Learning skills: At the end of the course the student must be able to study independently the topics covered, besides must be able to transfer the concepts learned in subsequent courses.

Some basic statistics concepts. Knowing how to use a computer, a spreadsheet, and a text editor.

1. Data structures and encoding.
2. Real data visualization by different techniques of graphical representation.
3. Data preprocessing and exploratory data analysis.
4. Principal component analysis and related techniques.
5. Supervised quantitative models.
6. Supervised qualitative models.
7. Design of experiments.
8. Analysis of designed experiments.

J.N.Miller & J.C.Miller “Statistics and Chemometrics for Analytical Chemistry” ed. Pearson 2018
P. Brereton, Chemometrics: Data Driven Extraction for Science, 2018 John Wiley & Sons Inc
Chemometrics Using R (Harvey) available at https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Chemometrics_Using_R_(Harvey)

1. Data structures and encoding: vectors, matrices, arrays. Univariate vs multivariate data. Numerical descriptors of datasets: parametric and non-parametric descriptive statistics. Practical examples in R software environment.
2. Real data visualization by different techniques of graphical representation, data analysis by visual exploration. Anomaly identification methods. Control charts. Practical examples in R software environment.
3. Data preprocessing and exploratory data analysis: handling of missing data, row vs column transformation, data cleaning. Overview of clustering and unsupervised modeling. Practical examples in R software environment.
4. Principal component analysis and related techniques: theory and application of principal component analysis and related techniques. Definition, derivation, application of latent variables, graphical representation (scores, loadings, biplot). Practical examples in R software environment.
5. Supervised quantitative models: Linear regression methods. Model validation. Figures of merit. Practical Examples in R Software Environment.
6. Supervised qualitative models: Introduction to Class Modeling Methods, Classification/Discrimination, Differences, and Context of Use. Model validation. Figures of merit. Practical Examples in R Software Environment.
7. Design of experiments: Principles and techniques. Replication, randomization, blocking. Planning experiments. Screening designs. Practical Examples in R Software Environment.
8. Analysis of designed experiments: ANOVA and MLR. Estimation of model parameters. Response surface methodology. Optimal designs. Practical Examples in R Software Environment.

Classroom lectures and practical examples in dedicated free software (mainly R software environment). The teaching material, including examples, is available for the students on Moodle and MS-teams platforms.

The learning assessment is conducted through 2 in itinere reports plus 1 final oral exam:
REPORTS
During the course, two reports will be assigned (one on data analysis and one on experimental design). Each report must be submitted within the specified timeframe and, in any case, before the oral exam. Each report will be graded on a numerical range of 0 to 10 based on the following criteria: organisation, language, and capacity to synthesise (0-3); appropriate selection of analysis/planning methods (0-2); proper application (0-2); and ability to describe and interpret outcomes (0-3). The evaluation does not produce a final exam score, but it does determine eligibility to take the exam (average >= 6).
FINAL EXAM
The oral exam involves the candidate's presentation of a scientific article (score up to 10/30) assigned by the instructor. Typically, in addition to the presentation, two or three questions on topics covered during the course are proposed (score up to 20/30). The evaluation encompasses knowledge of specific subjects, language proficiency, communication effectiveness, and the ability to apply the studied methodologies.