Digital Analytics
Code: 104753 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Interactive Communication | OT | 4 |
Contact
- Name:
- Gemma Gómez Bernal
- Email:
- gemma.gomez.bernal@uab.cat
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
To be able to take this subject it is necessary to have basic knowledge of the English language to face the reading of the bibliography.
Objectives and Contextualisation
Learn data collection techniques for preparing reports based on information obtained from the Internet in order to optimize processes.
Measurement and data collection systems and web analytics tools will also be delved into.
Social media analytics will play a prominent role, as well as measurement strategies
Competences
- Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
- Act within one's own area of knowledge, evaluating sex/gender-based inequalities.
- Identify the characteristics of information systems from both a conceptual and a practical perspective.
- Introduce changes in the methods and processes of the field of knowledge to provide innovative responses to the needs and demands of society.
- Manage time efficiently and plan for short-, medium- and long-term tasks.
- Plan, implement, analyse and evaluate social-media marketing campaigns and implement automation systems in management.
- Search for, select and rank any type of source and document that is useful for creating messages, academic papers, presentations, etc.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
Learning Outcomes
- Analyse a situation and identify its points for improvement.
- Analyse reports on internet and mobile data.
- Analyse the sex-/gender-based inequalities and gender bias in one's own area of knowledge.
- Communicate using language that is not sexist or discriminatory.
- Consider how gender stereotypes and roles impinge on the exercise of the profession.
- Create measurement strategies.
- Critically analyse the principles, values and procedures that govern the exercise of the profession.
- Cross-check information to establish its veracity, using evaluation criteria.
- Distinguish the salient features in all types of documents within the subject.
- Evaluate the impact of problems, prejudices and discrimination that could be included in actions and projects in the short or medium term in relation to certain people or groups.
- Identify data-collection systems.
- Identify the social, economic and environmental implications of academic and professional activities within one's own area of knowledge.
- Interpret big data in websites and applications.
- Interpret the results of content creation based on scientific thought.
- Plan and conduct academic studies in the field of digital analytics.
- Propose new methods or well-founded alternative solutions.
- Propose new ways to measure the success or failure of the implementation of innovative proposals or ideas.
- Propose projects and actions that are in accordance with the principles of ethical responsibility and respect for fundamental rights and obligations, diversity and democratic values.
- Propose projects and actions that incorporate the gender perspective.
- Propose viable projects and actions to boost social, economic and environmental benefits.
- Recognise the different tools of web analytics.
- Submit course assignments on time, showing the individual and/or group planning involved.
- Understand and apply the metrics of web analytics.
- Weigh up the risks and opportunities of both one's own and other people's proposals for improvement.
Content
- Introduction to Digital Analytics
- Web Analytics: Google Analytics, Looker Studio, measurement paradigms.
- Digital Analytics: Analytics applied to social networks and digital platforms.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Laboratory practices | 12 | 0.48 | 2, 23, 21, 11, 13, 14, 15, 22 |
| Master classes with ICT support | 15 | 0.6 | 7, 1, 8, 9, 12, 24, 16, 17, 18, 20, 10 |
| Seminars | 21 | 0.84 | 7, 3, 1, 4, 8, 9, 12, 24, 22, 16, 17, 18, 19, 20, 5, 10 |
| Type: Supervised | |||
| Theoric exam | 3 | 0.12 | 7, 23, 21, 11, 13, 18, 10 |
| Type: Autonomous | |||
| Study: Reading and synthesis of scientific documents | 54 | 2.16 | 7, 2, 1, 8, 6, 9, 11, 12, 13, 14, 10 |
| Tutorials (individual or group face-to-face activity aimed at solving learning problems) | 12 | 0.48 | 7, 2, 3, 4, 6, 13, 14, 15, 18, 19, 5, 10 |
The acquisition of knowledge will be done through various methodological procedures that include different types of activities, grouped into: theoretical sessions, practice sessions and seminars.
In the theoretical sessions, the contents of the program will be presented, thus providing the necessary elements to carry out the practical exercises in the laboratories.
As for the practices, they will be used to apply in real cases the knowledge acquired in the theoretical sessions. The seminars encourage critical reflection and debate on the analysis of real cases and models.
The detailed calendar and content of the different sessions will be presented on the day of presentation of the subject and will also be posted on the virtual campus where students can find the detailed description of the exercises and practices, as well as the various teaching materials and any information necessary for the proper follow-up of the subject.
Note: The course content will be sensitive to issues related to gender perspective and the use of inclusive language.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Submission of individual assignments | 20% | 15 | 0.6 | 7, 3, 1, 4, 8, 9, 12, 24, 22, 16, 17, 18, 19, 20, 5, 10 |
| Subsmission and presentation of final project | 40% | 15 | 0.6 | 7, 2, 3, 1, 4, 8, 6, 9, 12, 13, 14, 15, 24, 22, 16, 17, 18, 19, 20, 5, 10 |
| Theoric exam | 40% | 3 | 0.12 | 7, 23, 21, 11, 13, 18, 10 |
The competencies of this course are assessed through different activities:
- Theoretical exam (40% of the final grade)
- Submission and presentation of the final group project (40% of the final grade)
- Submission of individual assignments (20% of the final grade)
The final grade will be the sum of the scores obtained in each of these parts.
It is essential to complete all three assessment components in order to pass the course.
The weighting of the three graded parts will be applied even if one of them is failed. However, weighting will not be applied if two parts are failed.
The evaluation system for this course corresponds to continuous assessment.
OPTIONAL REEVALUATION SYSTEM:
Students will have the right to recover the course only if all assessment activities have been completed. Only individual assignments and the theoretical exam may be retaken. Therefore, any activities not submitted cannot be recovered. The final group project is not eligible for recovery or reassessment.
The maximum grade for recovered individual assignments will be 5 out of 10.
The grade obtained in the resit of the written exam will be the final grade for that component, regardless of whether it is higher or lower than the grade obtained in the first attempt.
Attendance: Attendance at seminar and laboratory practice sessions is mandatory. Unjustified absence from these sessions will result in a "not submitted" grade for the corresponding seminar or practice, and it will therefore not be recoverable.
If a student commits any irregularity that could lead to a significant alteration of an assessment activity, that activity will be graded with a 0, regardless of any disciplinary proceedings that may be initiated. If multiple irregularities occur in the assessment activities of the same course, the final grade for the course will be 0.
This course does not allow for a single assessment system.
Inthis course, the use of Artificial Intelligence (AI) technologies is not permitted at any stage. Any assignment containing content generated by AI will be considered a breach of academic integrity and may result in a partial or total penalty to the assignment grade, or more serious sanctions in severe cases.
Bibliography
Bonini, Tiziano & Treré, Emiliano (2024). Algorithms of Resistance: The everyday fight against platform power. The MIT Press.
Bucher, Taina (2018). If... then: Algorithmic power and politics. Oxford University Press.
Gupta, Shaphali, et al. (2020). Digital analytics: Modeling for insights and new methods. Journal of Interactive Marketing 51(1), 26-43.
Kaushik, Avinash (2011). Analítica Web 2.0: El arte de analizar resultados y la ciencia de centrarse en el cliente. Gestión 2000.
Lara-Navarra, Pablo; López-Borull, Alexandre; Sánchez-Navarro, Jordi & Yànez, Pau (2018). Medición de la influencia de usuarios en redes sociales: Propuesta Socialengagement. El profesional de la información, 2018, 27(4).
Muñoz Vera, Gemma & Elosegui, Tristán (2011). El arte de medir: Manual de analítica Web. Bresca.
Perriam, Jessamy, Andreas Birkbak, and Andy Freeman (2020). Digital methods in a post-API environment. International Journal of Social Research Methodology 23(3), 277-290.
Rogers, Richard (2013). Digital methods. MIT press.
Van Atteveldt, Wouter, Damian Trilling & Carlos Arcila Calderon (2022). Computational analysis of communication. John Wiley & Sons.
Wolf, Christine T. (2019). Invisible Women: Data Bias in a World Designed for Men. Ballantines Book.
Software
Digital analytics and visualization tools.
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 61 | Catalan | first semester | afternoon |
| (TE) Theory | 6 | Catalan | first semester | afternoon |