This course offers a comprehensive introduction to modern tools and models for analyzing both quantitative and qualitative data. It is designed to strengthen students’ existing knowledge of statistics while expanding…
Scheda dell'insegnamento
Obiettivi formativi
This course offers a comprehensive introduction to modern tools and models for analyzing both quantitative and qualitative data. It is designed to strengthen students’ existing knowledge of statistics while expanding their skills in advanced analytical techniques, with a special emphasis on multivariate statistics, generalized linear models, and time series analysis.
A specific focus will be placed on statistical models suited for non-quantitative data, such as responses from questionnaires and categorical variables, which are commonly encountered in psychological and social science research.
The course also introduces fundamental Machine Learning approaches, discussing their assumptions, applications, and limitations through hands-on examples. Students will gain practical insight into how these methods can complement traditional statistical models, particularly in the context of predictive modeling and classification tasks.
Finally, the course provides an introduction to the computational and statistical tools used for handling and analyzing high-dimensional datasets (Big Data), helping students develop a critical understanding of the opportunities and challenges of working in data-intensive environments.
Knowledge and understanding
By the end of the course, students will be able to:
Demonstrate knowledge and understanding of the main statistical methods and experimental designs applicable to qualitative, categorical, and quantitative data.
Interpret the results of data analyses, accurately describe statistical outputs, and provide clear, context-appropriate interpretations of the findings.
Applying Knowledge and Understanding
By the end of the course, students will be able to:
Use computational tools to extract, explore, and manipulate information from both low- and high-dimensional datasets.
Identify the most appropriate statistical framework and analytical approach for hypothesis testing in diverse research contexts.
Assemble a structured and logical analysis workflow, applying basic functions and tools as needed.
Enhance and communicate analytical results using the most suitable and effective graphical representations.
Critical Thinking
By the end of the course, students will be able to:
Recognize research questions, critically evaluate available data and information, design meaningful experiments, and compare alternative hypotheses.
Formulate clear and focused research questions, and identify the most appropriate statistical tools to test the corresponding hypotheses.
Communication Skills
By the end of the course, students will be able to:
Confidently use appropriate technical language to interpret and describe statistical results.
Clearly explain and justify the chosen experimental design and the statistical methods applied.
Communicate analytical findings effectively through written reports, visual presentations, and discussions, adapting the language and format to suit both specialist and non-specialist audiences.
Learning Skills
By the end of the course, students will be able to:
Critically assess their own level of knowledge and identify areas for improvement.
Independently identify and consult reliable sources to deepen their understanding of statistical techniques applicable to both qualitative and quantitative data, particularly in the context of food consumption research.
Enhance their coding skills autonomously by making effective use of e-learning platforms, open-source resources, and self-directed training tools.
Programma
Topics
CFU
Recap of exploratory statistics:
Describing data and variability.
Probability distributions and descriptors (Binomial, Poisson, Chi Squared, Student T, Normal).
Introduction to Hypothesis testing
Confidence intervals
1.50
Basic concepts on probability and counting:
Probability rules and applications.
Permutations and combinations
Conditional probability
0.50
Introduction to statistical inference:
Comparing populations (small and large samples).
Correlation and regressions
Statistical models, ANOVA, GLM and GLMM.
1.50
Non-parametric data and beyond:
Non-parametric test (Sign test, Kruskall-Wallis, Wilcoxon, rank correlation.
Time series analysis
Introduction to multivariate techniques (MDS, PCOOA)
0.50
Practical
Introduction to R programming.
Data types and data editing.
Algorithm thinking and main constructs.
Plots, graphical representations, and reports in R.
Introduction to the most common data analysis packages
1.00
The course will combine traditional lectures with hands-on practical activities. Lectures will cover the theoretical foundations of the subject while integrating real-world examples relevant to students’ future professional careers. Practical sessions are designed to reinforce theoretical concepts and help students translate them into applicable skills. Lectures will be delivered through in-person (frontal) teaching.
Modalità d'esame
At the end of the course, students will be assessed through a written exam with a maximum score of 30/30, consisting of a combination of multiple-choice questions and practical exercises. Each multiple-choice question will award 1 point for a fully correct answer, 0 points for no answer, and −1 point for an incorrect answer, in order to discourage random guessing. Practical exercises will be graded from 2 to 6 points, depending on their level of difficulty; the point value of each exercise will be clearly indicated in the test. The final score will be calculated as the sum of the points obtained from both the multiple-choice section and the exercises.
Testi di riferimento
Reading material, references, presentations, and code will be made available at the end of each section of the course.
Codice insegnamento UC16491