99087 - Data Analysis for Media (1) (LM)

Learning outcomes

The course aims to provide students with a clear understanding of the role of data in the contemporary media landscape. Students will learn to use data-driven methodologies to analyze media products, their production and circulation, and the social discourses related to them. An in-depth theoretical part will be dedicated to the way in which the enormous availability of data, in the digital context, has transformed both the media supply chain and the methodologies of evaluation, prediction and analysis. The student will then be provided with specific skills related to the extraction, transformation and loading of data and modeling tools in the media environment. No previous knowledge of programming languages or specific software is required. At the end of the course the student will have acquired skills related to: - data extraction both from dedicated databases and from social media and web pages; - data analysis, evaluation of data quality, cleaning and construction of datasets; - qualitative and quantitative modeling; - implementation of simple scripts for data manipulation; - introduction to the use of databases; - basic elements of data visualization

Course contents

The course is structured in three parts, each of which addresses progressively more complex aspects of data analysis in the audiovisual sector. The methodological approach is based on the use of LLM (ChatGPT) as the main analysis tool, allowing students to conduct data analysis without resorting to traditional programming.

Part 1: Fundamentals of Data Analysis (3 lessons)

This section introduces the fundamental concepts of data analysis applied to media. Students will examine the importance of data-driven analysis in the contemporary media landscape, data collection methods, and dataset cleaning techniques. Particular attention will be given to exploratory data analysis (EDA) and visualization, which are necessary tools for identifying patterns and communicating results effectively.

Part 2: Statistical Methods and Machine Learning (5 lessons)

Students will acquire skills in the application of statistical methods, from hypothesis formulation to significance testing, exploring different analysis techniques and measures of effectiveness.

The course continues with the study of regression models, from classic forms to the most recent extensions, leading to an introduction to machine learning and generative artificial intelligence applied to media.

Part 3: Application projects (2 lessons)

The final segment consists of practical applications on real case studies from the audiovisual sector. Students will analyze concrete datasets, such as the one on gender inequalities in the production of Italian TV series, and develop a final project analyzing and interpreting film data. These activities consolidate the skills acquired through the direct application of the methodologies studied.

The course provides students with the skills necessary to conduct data analysis in the audiovisual sector using conversational artificial intelligence tools.

Readings/Bibliography

The course is geared toward direct interaction with students. Therefore, study and practice materials will be provided in class and released through the Virtuale platform. For each lecture, the materials needed for exam preparation and any optional further study will be indicated. Students are encouraged to attend; without attendance and without significant prior knowledge, preparation for the exam could present significant difficulties.

Students who wish to delve systematically into the course topics can refer to Joel Grus' text, Data Science from Scratch: First Principles With Python, O'Reilly, 2019. The study of this optional text is not necessary to take the exam. In order to complete the exercises, students should have access to a ChatGPT Plus account, at least one for every two students.

Students with SLD or temporary or permanent disabilities. It is suggested that they get in touch as soon as possible with the relevant University office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en) and with the lecturer in order to seek together the most effective strategies for following the lessons and/or preparing for the examination.

Teaching methods

The course involves constant interaction with students, both in the discussion of theoretical topics and in the actual implementation of application projects and exercises. Students are advised to use their own computer to follow lectures and be able to continue the exercises independently. Exercises are an integral part of the course.

Assessment methods

The assessment comprises an ongoing assessment and a final exam.

The ongoing assessment consists of multiple-choice questions to be completed in class during lesson 8. The test consists of 31 questions, which will be marked as follows: 1 point for each correct answer, -0.5 points for each incorrect answer, 0 points for each unanswered question.

For the final exam, students will have to carry out, either individually or in groups, a Data Analysis project agreed upon with the instructor. The project will be presented at the end of the course (depending on the size of the class) or during the exam. The exam consists of an oral interview dedicated to both the analysis of the project and the assessment of the learning of the course topics.

The grade will be given based on the average of the results of the ongoing assessment and the final exam.

Students with SLD or temporary or permanent disabilities. It is necessary to contact the relevant University office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en) with ample time in advance: the office will propose some adjustments, which must in any case be submitted 15 days in advance to the lecturer, who will assess the appropriateness of these in relation to the teaching objectives.

Teaching tools

Lectures will include Prezi presentations accessible to students, while cloud-based collaborative notebooks (Jupyter Notebook) will be used for coding, tutorials and project development. Additional materials will be provided through the Virtual platform.

Office hours

See the website of Guglielmo Pescatore