STRUMENTI STATISTICI E INFORMATICI PER L'ANALISI DEI DATI

At the end of the course, students will be able to apply advanced statistical methods for the analysis and
interpretation of complex data in the field of chemistry and pharmaceutical technology. They will be capable
of designing appropriate scientific experiments using experimental designs like factorial designs and
utilizing advanced statistical software such as R and Python for data analysis and visualization. Additionally,
they will know how to implement multivariate analyses for data dimensionality reduction and classification,
perform linear and nonlinear regressions, and optimize synthetic processes. They will understand and apply
advanced pharmacokinetic and pharmacodynamic models, validating analytical methods according to ICH
guidelines. They will be able to manage databases using SQL and apply knowledge of artificial intelligence
and in silico medicine for predictive analysis and personalization of therapies. Finally, they will conduct
complete research projects—from hypothesis formulation to results presentation—while respecting current
regulations and ethical principles in data management and analysis.

Through lessons and practical sessions at the end of each learning unit (when planned).
If the lessons are given in a mixed or remote way, the necessary changes with respect to what was
previously stated may be introduced, in order to meet the program envisaged and reported in the syllabus.
To guarantee equal opportunities and in compliance with the laws in force, interested students can ask for a
personal interview in order to plan any compensatory and / or dispensatory measures, based on the didactic
objectives and specific needs.
It is also possible to contact the referent teacher CInAP (Center for Active and Participated Integration -
Services for Disabilities and / or SLD) of our Department, Prof. Santina Chiechio.
Pursuant to the RDA, Article 12 – University Educational Credits (CFU), within the standard workload of 25
total hours of student commitment corresponding to one credit, the following shall apply:
(a) seven (7) hours shall be devoted to lectures or equivalent instructional activities, with the remaining
hours reserved for individual study;
(b) a minimum of twelve (12) and a maximum of fifteen (15) hours shall be devoted to classroom exercises
or equivalent supervised activities (laboratories), with the remaining hours reserved for personal study and
elaboration.

Solid knowledge in organic, analytical, and physical chemistry.
Basic notions of statistics and mathematics.
Basic computer skills

Ruled by http://www.dsf.unict.it/corsi/lm-13_ctf/regolamento-didattico.

Module 1: Introduction to Statistics in the Pharmaceutical Context

• Importance of statistical analysis in drug research and development.
• Fundamental concepts of descriptive statistics

• Full and fractional factorial designs.
  
• Probability distributions (Binomial, Poisson, Normal, Gaussian, and others)
  
• Risk measures
  
• Inferential statistics and hypothesis testing
  
• Analysis of Variance (ANOVA)
  
• One-way and two-way ANOVA
  
• Linear and Non-linear Regression
  
• Multiple regression models
  

• Principal Component Analysis (PCA)
• Data dimensionality reduction.
• Classification and Clustering Methods
• Linear Discriminant Analysis (LDA)
• Clustering algorithms (K-means, hierarchical).

• Advanced Statistical Software
• Using R for statistical analysis.
• Advanced Spreadsheets Tools
• Data analysis with Excel: Solver, Data Analysis ToolPak.
• Database Management

• Advanced Pharmacokinetics and Pharmacodynamics
• Validation of Analytical Methods according to ICH guidelines.

• Hands-on laboratory sessions
• Analysis of real datasets from pharmaceutical studies

Teacher's notes will be available through studium website or made available during the lessons.

Written exam on the knowledge acquired and possible evaluation of the project.
The examination dates are published on the website of the Department of Drug and Health
Sciences https://www.dsf.unict.it/sites/default/files/2025-07-24-calendario%20esami%20CTF.pdf

1) Which of the following statements best describes Principal Component Analysis (PCA)?
A) It is a regression method to predict a dependent variable based on independent variables.
B) It is a supervised classification technique to assign samples to predefined categories.
C) It is a dimensionality reduction technique that transforms correlated variables into a set of uncorrelated
components.
D) It is a clustering algorithm used to group similar data without predefined labels.

2) In pharmacokinetics, which compartmental model best describes a drug that distributes rapidly and
uniformly throughout the body after absorption?
A) One-compartment model.
B) Two-compartment model.
C) Three-compartment model.
D) Non-compartmental model.