ID:
511318
Duration (hours):
24
CFU:
3
SSD:
CHIMICA ANALITICA
Year:
2025
Overview
Date/time interval
Primo Semestre (29/09/2025 - 23/01/2026)
Syllabus
Course Objectives
The course aims to provide theoretical and practical skills in data analysis and experimental design needed in the pharmaceutical sciences.
Course Prerequisites
None
Teaching Methods
Lectures and guided solutions to numerical exercises discussed in class using Microsoft Excel and open-source software specifically developed for the course. Educational material is created with PowerPoint and Excel files and is fully available on Kiro and open-source software.
Active learning (learn by doing): During class hours, students will be guided in using Microsoft Excel and software
Mastery learning: Students must master the topics covered in each unit. Students who do not reach a sufficient level of competence are provided additional support.
Attendance at lectures is not only recommended but also highly beneficial. It allows students to engage directly with the course material and the instructor, enhancing their understanding and learning experience.
For students with specific needs who cannot attend teaching activities in person and have applied to take advantage of “Modalità Didattiche Inclusive” (Inclusive Teaching Modalities), adequate teaching material is available for autonomous study. Upon request, the autonomous study is supported by tutoring, teaching activities, and dedicated online meetings, with time flexibility depending on the students’ needs.
Active learning (learn by doing): During class hours, students will be guided in using Microsoft Excel and software
Mastery learning: Students must master the topics covered in each unit. Students who do not reach a sufficient level of competence are provided additional support.
Attendance at lectures is not only recommended but also highly beneficial. It allows students to engage directly with the course material and the instructor, enhancing their understanding and learning experience.
For students with specific needs who cannot attend teaching activities in person and have applied to take advantage of “Modalità Didattiche Inclusive” (Inclusive Teaching Modalities), adequate teaching material is available for autonomous study. Upon request, the autonomous study is supported by tutoring, teaching activities, and dedicated online meetings, with time flexibility depending on the students’ needs.
Assessment Methods
The exam can be passed with four ongoing assessments consisting of discussing a project work presented by the student at the end of each course unit. The projects involve analyzing and interpreting data acquired under the instructor's guidance. The exam grade is calculated on a scale of thirty as the arithmetic mean of the scores obtained in the ongoing assessments. Alternatively, the exam can be passed in the traditional, written mode during regular sessions. These sessions will last two hours and include solving a data analysis exercise. Manuals, notes, and personal computers will be allowed during the exam, which will be graded on a scale of thirty.
Texts
G. Marrubini, C. Melzi, Trattamento dei dati e progettazione degli esperimenti per le scienze chimiche e farmaceutiche. McGraw-Hill, Milano, maggio 2024.
K.H. Jarman, The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics. Wiley, New York 2013,
G.A. Lewis, D. Mathieu, R. Phan-Tan-Luu, Pharmaceutical Experimental Design.
Marcel Dekker-CRC Press, New York 1998.
D.C. Montgomery, Design and Analysis of Experiments. 10th edition, Wiley, New York 2019.
G.E.P. Box, J.S. Hunter, W.G. Hunter, Statistics for Experimenters. Design, Innovation, and Discovery. Second edition, Wiley, New York 2005.
R.G. Brereton, Data Analysis for the Laboratory and Chemical Plant. Wiley, New York 2003.
K.H. Jarman, The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics. Wiley, New York 2013,
G.A. Lewis, D. Mathieu, R. Phan-Tan-Luu, Pharmaceutical Experimental Design.
Marcel Dekker-CRC Press, New York 1998.
D.C. Montgomery, Design and Analysis of Experiments. 10th edition, Wiley, New York 2019.
G.E.P. Box, J.S. Hunter, W.G. Hunter, Statistics for Experimenters. Design, Innovation, and Discovery. Second edition, Wiley, New York 2005.
R.G. Brereton, Data Analysis for the Laboratory and Chemical Plant. Wiley, New York 2003.
Contents
The course is organized into four units dedicated to a specific topic. Access to unit 4 requires understanding and practice of the topics covered in units 1-3. Unit 1 (4 hours): descriptive data analysis, univariate and multivariate data. Unit 2 (4 hours): statistical inference and hypothesis testing. Unit 3 (4 hours): regression analysis. Unit 4 (12 hours): design of experiments. UNIT 1. Analysis of univariate data: descriptive statistics. Quantitative and qualitative variables, tables, and graphs. Position and dispersion descriptors. Calculation of mode, mean, median, quantiles, range of variation, variance, standard deviation, relative standard deviation, pooled standard deviation. Multivariate data analysis. Possible representations of multidimensional data. Principal Component Analysis (PCA). Scores and Loadings. Graphic representation of scores and loadings. Data pre-processing. UNIT 2. Inferential statistics. Population and sample. Point estimate of a parameter. Interval estimates of a parameter. Hypothesis testing. Types of errors and power of hypothesis tests. Comparison of an average value with reference data. Use of the standardized normal distribution, z score: the variance of the population is known a priori. T-Test: Population variance is unknown. Checking the preconditions to apply the Z and T tests correctly. Test to compare two means. Data series that are not independent of each other but can be made independent. Independent data sets. Analysis of variance (ANOVA). One-factor ANOVA. Test for comparing two variances. UNIT 3. Linear regression. Estimation of parameters of linear models. Significance of the parameters. Regression ANOVA. Multiple linear regression. Estimation and significance of parameters of multiple regression models. Correlation and determination coefficients. Regression analysis of variance. Introduction to Design of Experiments (DoE). UNIT 4. Methodological approaches to experimental research: what is an experimental plan? Matrix representation of an experimental plan. Full factorial models. Interactions in full factorial models. Fractional factorial designs. Confusions in fractional factorial designs. Plackett-Burman designs. Response surface methodology. D-optimal designs. Mixture designs.
Course Language
Italian
More information
None
Degrees
Degrees (2)
MEDICINAL CHEMISTRY AND PHARMACEUTICAL TECHNOLOGY
Single-cycle Master’s Degree
5 years
PHARMACY
Single-cycle Master’s Degree
5 years
No Results Found
People
People
Personale tecnico amministrativo
No Results Found