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Big Data Analysis and Visualisation

Code: 104750 ECTS Credits: 6
2025/2026
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

  • Basic knowledge of Microsoft Excel or OpenOffice Calc.
  • Python knowledge acquired in previous subjects.
  • A clear desire to learn data-oriented Python.

Objectives and Contextualisation

The main objective of the course is to provide students with the basic skills and competencies to be able to manage, analyze and visualize large volumes of structured information. For this, we will make an introduction to the Python programming language, oriented exclusively to working with data. We will emphasize on the methods of data mining, cleaning and transformation of information for its subsequent analysis. For this we will focus on libraries such as Pandas and Numpy. For the analysis and visualization section we will dive into Tableau Desktop.


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.
  • Determine and plan the technological infrastructure necessary for the creation, storage, analysis and distribution of interactive multimedia and social-networking products.
  • 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.
  • Promote and launch new products and services based on massive-scale mining and analysis of data from the Media.
  • 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 communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
  • Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
  • Take account of social, economic and environmental impacts when operating within one's own area of knowledge.

Learning Outcomes

  1. Analyse a situation and identify its points for improvement.
  2. Communicate using language that is not sexist or discriminatory.
  3. Critically analyse the principles, values and procedures that govern the exercise of the profession.
  4. Cross-check information to establish its veracity, using evaluation criteria.
  5. Describe the infrastructure needed to store big data.
  6. Differentiate between the various types of existing architectures for working with big data.
  7. Distinguish the salient features in all types of documents within the subject.
  8. 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.
  9. Explain the characteristics of the infrastructure needed to recover big data.
  10. Explain the explicit or implicit deontological code in your area of knowledge.
  11. Explain the infrastructure needed to process big data.
  12. Extract large volumes of data from social networks and the new digital media in particular.
  13. Identify situations in which a change or improvement is needed.
  14. Identify the social, economic and environmental implications of academic and professional activities within one's own area of knowledge.
  15. Plan and execute academic projects in the field of big data.
  16. Propose new methods or well-founded alternative solutions.
  17. 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.
  18. Propose projects and actions that incorporate the gender perspective.
  19. Propose viable projects and actions to boost social, economic and environmental benefits.
  20. Share experiences with the group as a path to learning, in order to work subsequently in multidisciplinary groups.
  21. Solve basic problems in big data.
  22. Submit course assignments on time, showing the individual and/or group planning involved.
  23. Weigh up the risks and opportunities of both one's own and other people's proposals for improvement.

Content

Block 1: Big Data Analysis:

  • Introduction to Big Data and data analysis with Python.

  • Basic infrastructure for data manipulation with Python.

  • Data analysis using the Pandas library.

  • Data sources (APIs, web scraping...)

Block 2: Big Data Visualization:

  • Principles of data visualization.

  • Presentation formats for Big Data.

  • Big Data visualization tools.

The calendar will be available on the first day of class. Students will find all information on the Virtual Campus: the description of the activities, teaching materials, and any necessary information for the proper follow-up of the subject.


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Laboratory sessions 33 1.32 20, 5, 6, 11, 9, 12, 15, 19, 21
Theory and guided hands-on learning 15 0.6 3, 1, 20, 2, 4, 5, 7, 6, 10, 11, 9, 12, 14, 13, 15, 23, 22, 16, 17, 18, 19, 21, 8
Type: Supervised      
Group Final Project 50 2 3, 1, 2, 4, 7, 6, 12, 13, 15, 22, 16, 17, 18, 19, 21, 8
Type: Autonomous      
Hours of study 27 1.08 15, 21
Laboratory practices preparation 25 1 1, 20, 6, 12, 13, 19, 21

Theoretical and practical sessions.

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
Attendance and Participation 10% 0 0 20, 15, 21
Classrom exercises 35% 0 0 4, 5, 7, 6, 11, 9, 13, 15
Final group Project 50% 0 0 3, 1, 20, 2, 4, 5, 7, 6, 10, 11, 9, 12, 14, 13, 15, 23, 22, 16, 17, 18, 19, 21, 8
Oral presentation in the classroom 5% 0 0 1, 2, 7, 14, 22

The competencies of this subject are assessed through the following activities:

  • Attendance and participation (10% of the final grade).

  • In-class exercises (35% of the final grade).

  • Group practical project (50% of the final grade).

  • Oral presentations of the projects (5% of the final grade).

The final grade will be the sum of the scores obtained in each of these components.
It is essential to complete and pass both the in-class exercises and the group practical project in order to pass the subject.
Students who do NOT pass the continuous assessment have the option to take a final exam, which will be held on the last day of class. This exam will include both theoretical and practical components. To sit the final exam, it is mandatory to have submitted the group project.

This subject offers the possibility of a single assessment. The conditions for being assessed under this modality will be explained on the first day of class.

If a student commits any irregularity that could significantly alter the outcome of an assessment, that assessment will be graded with a 0, regardless of any disciplinary proceedings that may be initiated. If multiple irregularities are detected in the assessment activities of the same subject, the final grade for that subject will be 0.

In this 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

Ghani, Norjihan Abdul, et al. (2019). Social media big data analytics: A survey. Computers in Human behavior, 101, 417-428.

Kelleher, John D.; Namee, Brian Mac & D'arcy, Aoife (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.

Knaflic, Cole Nussbaumer (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons. 

Lomborg, Stine & Bechmann, Anja (2014). Using APIs for data collection on social media. The Information Society 30(4), 256-265.

Parks, Malcolm R. (2014). Big data in communication research: Its contents and discontents. Journal of communication 64(2), 355-360.

Tufekci, Zeynep (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the international AAAI conference on web and social media, 8(1).

Van Atteveldt, Wouter; Trilling, Damian & Arcila, Carlos (2022). Computational analysis of communication. John Wiley & Sons.

 

Software

  • Tableau
  • Gephi
  • Pycharm
  • Microsoft Excel / OpenOffice Calc

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 second semester afternoon
(TE) Theory 6 Catalan second semester afternoon