ECO0375 - INTRODUCTION TO DATA ANALYTICS

courses
ID:
ECO0375
Duration (hours):
45
CFU:
6
SSD:
STATISTICA ECONOMICA
Year:
2025

Overview

Date/time interval

Secondo Semestre (16/02/2026 - 29/05/2026)

Syllabus

Course Objectives

The increasing availability of data has highlighted the growing need for appropriate methodologies and tools for quantitative decision-making processes in the fields of economics, business, and finance. Today’s graduates must be able to utilize data to a much greater extent than their predecessors. The aim of the course is to provide students with the main concepts and the most relevant techniques for analyzing and synthesizing large datasets. The theoretical discussion will be complemented by practical implementation through the analysis of real data, illustrating methods and concepts with the help of the statistical software R. The course includes practical lessons, where the application of each technique is discussed with reference to real datasets. This course is part of the study program with the objective of providing students with knowledge of the basic methodologies for the statistical analysis of economic data. By the end of the course, students will be able to: Understand and analyze the main techniques of multivariate statistics and data analytics useful for the analysis of economic, business, and financial data. Study the relationships between relevant variables. Evaluate and compare different statistical models. Apply these statistical tools to economic and business-related problems using R software. Independently conduct an analysis of real economic data and interpret the main results. Justify the reasoning behind the adoption of a specific analytical technique and formulate, in a critical and rigorous manner, reasoning on key economic and business aspects, drawing synthetic information to support decision-making and the management of business risk situations.

Course Prerequisites

Profitable learning of the course contents requires knowledge of basic concepts and tools of probability, descriptive and inferential statistics, included in the Statistics for Economics teaching programme. There are no preparatory constraints with respect to other courses of the course of study.

Teaching Methods

The lessons take place in the classroom according to the schedule communicated. During the lessons, topics will be addressed both from a theoretical perspective and by illustrating the methods of implementation using the software. Real data analysis and discussion of the results will be carried out during the lessons. Some materials discussed in class will be made available on the university’s e-learning platform. The students' learning activity will also involve group work and presentation of results. Active participation in the lessons is strongly recommended.

Assessment Methods

In order to measure the achievement of the learning outcomes mentioned above, the students' assessment is based on two main components: GENERAL WRITTEN EXAM lasting about 90 minutes, which contributes 60% to the final grade. The written exam consists of exercises and both open-ended and closed-ended questions designed to assess the knowledge of the statistical techniques covered in the course, the ability to correctly apply statistical tools, to synthesize information contained in datasets, to study the relationship between variables, and to choose appropriate statistical models. PROJECT (40% of the final grade), which aims to test the students' ability to analyze a real dataset using the R statistical software and critically discuss the output. The final grade is the weighted average of the points obtained in the written exam and the project. To pass the exam, the student must achieve a final grade of at least 18/30. Grades higher than 30 entitle the student to honors. As an alternative to the general written exam, students may take two PARTIAL WRITTEN EXAMS, one midway through the course and one at the end. In this case, the weight is 30% for the first partial exam and 30% for the second partial exam. To pass a partial exam, the student must score at least 15/30. The final grade is the weighted average of the points obtained in the two written exams and the project. To pass the exam, the student must achieve a final grade of at least 18/30. Grades higher than 30 entitle the student to honors. There is no difference between attending and non-attending students.

Contents

The course will cover the main multivariate statistics techniques. All topics will be examined and treated through concrete examples and applications to real data.

Course Language

Italian

More information

Office hours by appointment, by writing to valerio.lange@uninsubria.it

Degrees

Degrees (2)

People

People

LANGE' VALERIO
Teaching staff
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