95966 - Business Data Analysis

Learning outcomes

At the end of the course the student is familiar with the main statistical methods necessary for the study of economics. Furthermore, the student is able to use the main statistical techniques and, when possible, apply them to the contents of the sector.

Course contents

The course consists of two parts: the first is theoretical and introductory, while the second has a more practical focus.

By the end of the course, students will be able to conduct preliminary analyses of a dataset using basic statistical tools and dedicated software. They will also develop a greater awareness of statistical data, together with a critical approach to its interpretation.

 

The course is divided into two modules.

The first module, taught by Professor Meri Raggi, focuses on the theoretical component and covers the following topics:

Statistical variables; populations and samples; data collection and organization; datasets and frequency distributions; graphical representations. Measures of central tendency: mean, mode, median, and quantiles. Measures of heterogeneity, variability, and inequality. Distribution shape: skewness. Bivariate statistical analysis. Relationships between variables: statistical dependence, covariance, and linear correlation. Simple linear regression. Introduction to sampling and statistical inference. Bayes’ theorem. Random variables and probability distributions. Sampling methods. Point and interval estimation. Hypothesis testing.

 

The second module, taught by Professor Yari Vecchio, focuses primarily on the applied component of the course. Students will work on a number of case studies covering different topics and fields of application. Datasets will be provided to allow students to apply the theoretical concepts introduced during the course.

Readings/Bibliography

The course is mainly based on lecture notes and chapters/articles provided through the e-learning platform.

An useful background textbook could be: Cicchitelli et al. Statistics, Principles and Methods, Pearson, 2021.

Teaching methods

The course includes both theoretical lectures and practical sessions involving the analysis of datasets using dedicated statistical software.

Assessment methods

During the course, students will be provided with case studies from selected scientific articles or/and available dataset and will be required to write or present an essay dealing with the data analysis and interpretation.

For those students who do not deliver the essay, or fail, it will be possible to take a written exam with multiple-choice and open-ended questions on the foundations of data analysis and on the interpretation of a case study.

 

The result of the oral exam will be communicated at the end of the session. The minimum passing grade is 18/30.

The examination is considered passed only if all its components are successfully completed. The final grade will be awarded according to the following criteria:

• Insufficient knowledge and analytical skills: fail.
• Sufficient knowledge and analytical skills, expressed in formally correct language: 18–22.
• Adequate knowledge and sufficient analytical skills, although not particularly well developed, expressed in appropriate language: 23–26.
• Thorough knowledge of the topics covered in the course, good analytical and critical-thinking skills, and a sound command of subject-specific terminology: 27–29.
• Excellent and comprehensive knowledge of the topics covered in the course, strong critical-analysis skills, the ability to establish connections between concepts, and full command of subject-specific terminology: 30–30 cum laude.

A minimum final grade of 18/30 is required.

Negative results are not graded numerically but recorded as “withdrawn” or “failed” in the electronic transcript on AlmaEsami, and do not affect the student’s academic record.

Students may reject the final grade 2 times by informing the course examiner via email within 5 working days.

The designated course contact for this course is Prof Meri Raggi.

Exams are scheduled during the designated periods in the academic calendar. Additional examination sessions are available and may be arranged by contacting Prof Meri Raggi.

 

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer (mail to meri.raggi@unibo.it) who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

Additional materials (as scientific papers, slides, data..) will be provided during the lessons and the e-learning platform will enable access to this contents.

Office hours

See the website of Meri Raggi

See the website of Yari Vecchio