Teaching Methodologies
The assessment methodology allows students to develop practical and research skills in specific areas of BusinessAnalytics, which the student perceives as relevant to their training, to the organization in which they operate or thearea where they intend to develop their future career.
To be successful, you will have to carry out 2 evaluation elements: TP1 and TP2.
TP1: practical use of a data set, one of the available tools for data visualization and the application of the acquiredknowledge for the elaboration of a set of visualization elements that allow decision making based on that data.
TP2: individual elaboration of a scientific article on the topics covered in BA, namely, on the most relevant andemerging trends associated with the area.
The final classification, CF, is obtained:
CF = 1/2 (TP1 + TP2)
TP1 and TP2: individual ratings without rounding, scale from 0 to 20
CF: rounded to the nearest integer
Learning Results
The challenges of data analysis today are very much focused on promoting a true “data culture” in organizations, inaddition to a deep knowledge of the business in which each company or organization is operating, which, acting inharmony, allows the application of effective data-driven decision-making policies. Tools for data visualization, namely,from a self-service perspective, on cloud platforms, are now the rule: democratic access to data is also a fundamentaltopic of discussion.
In this UC, special emphasis is placed on themes and applications for Business Analytics and the challenges related todata-based decision making, which it is urgent to overcome so that a true data culture can emerge, namely: dataquality, data transformation processes, updating and visualization in real time and collaborative use of data andgenerated output.
Program
1 –Business Analytics and Data Culture fundamentals
1.1 Data Analytics Life Cycle
1.2 Data sources
1.3 Data transformation
1.4 Data Quality
1.5 Digital Curation
1.6 Ethical issues and GDPR
2 – Data visualization
2.1 Guidelines for Data visualization
2.2 Tools for Data visualization
2.3 Data visualization process: planning, monitoring, and discussing
Internship(s)
NAO
Bibliography
Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting.John Wiley & Sons.
Few, S. (2019), The Data Loom: Weaving Understanding by Thinking Critically and Scientifically with Data, AnalyticsPress.
Few, S. (2019), Now You See It: Simple Visualization Techniques for Quantiative Analysis, Analytics Press.
Pochiraju, B., & Seshadri, S. (Eds.). (2019). Essentials of Business Analytics: An Introduction to the Methodology andIts Applications (Vol. 264). Springer.
Aparicio, M., & Costa, C. J. (2015). Data visualization. Communication design quarterly review, 3(1), 7-11.
M. Y. Santos e I. Ramos, Business Intelligence – da Informação ao Conhecimento – 3.a edição Atualizada, Editora FCA,2017. ISBN: 978-972-722-880.
Schniederjans, M. J., Schniederjans, D. G., & Starkey, C. M. (2014). Business analytics principles, concepts, andapplications with SAS: what, why, and how. Pearson Education.