DATA ANALYSIS

In today's data-driven world, the ability to extract valuable insights from large and complex datasets is highly relevant after across various fields and disciplines. The DATA ANALYSIS course aims to provide students with with a solid background on the main concepts and techniques to effectively collect, clean, analyze, and interpret data to make informed decisions.

The course is intended to provide students with an overview of some of the main aspects of data analysis. Through a combination of theoretical notions and practical sessions, students will learn about different types of data and the appropriate statistical techniques to analyse them. They will also be introduced to the statistical software R, and will learn some of the most important commands to analyse data on such a platform.

Students are expected to have some basic understanding of the mathematical concepts of variable and function. Moreover, students are expected to have good computer utilisation skills.

The course will cover the following topics:
1. Programming tools for data processing (R):
a. Introduction to the software for data analysis (R)
b. Importing, cleaning, modifying and saving data
c. Programming Basics: Cycles and Conditions
d. Examples and applications

2- Applied statistics to data analysis
a. Definition of the search problem and identification of the variables to be considered for the analysis
b. Different kinds of variables: quantitative and qualitative
c. Summary statistics (mean, median, variance, standard deviation, correlation, main distributions) in the quantitative case and specificity of the qualitative case
d. Data visualization: bar plot, scatter plot, line plot, box plot, histogram
e. Regression analysis: notes

- Donna Kirk, "Contemporary Mathematics", Chapter 8 https://openstax.org/details/books/contemporary-mathematics ,
- Måns Thulin, 2025, Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling https://www.modernstatisticswithr.com/

Assessment
- 10% partecipation (>75%)
- 30% partial exam: written part (multiple choice questions 40%); oral part (exercises 60%)
- 30% home assignments
- 30% final exam: written part (multiple choice questions 40%); oral part (exercises 60%)

written and oral

The following is the grading on the basis of a maximum of 30L
18–21 points: Given for weak comprehension and underdeveloped problem-solving skills.
22–25 points: Indicates good comprehension and solid problem-solving techniques.
26–27 points: Reflects very good understanding and problem-solving ability.
28–30L points: Awarded for excellent performance.

The course will be based on both a theoretical of concepts underyling data analysis, and on a very practical approach of "learning by doing", also thanks to the strong emphasis on interactive work and lab sessions.

Office hours will be available upon appointment