Data Analytics

The basic recommended knowledge is the one common, in what regards Statistics, to the various syllabus of the undergraduate Mathematics courses in the different branches of compulsory education in Portugal (http://www.dge.mec.pt/).

The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared to actively engage students at various moments or throughout the entire class.

In the theoretical part of the lesson, the expository method will be frequently used to introduce concepts, fundamental results, and methods, interspersed with tasks that encourage active participation by all students (interactive lectures). These tasks include posing questions to and by students, orally and/or on a platform, as well as proposing debates/discussions in small groups on certain exposed aspects/topics.

The practical part will be designed to comprehensively develop the listed skills. This will be achieved through commented exemplification of procedures and/or problem-solving under the guidance/tutoring of the teacher. Autonomous work or work in small groups will be encouraged, progressing towards project-based learning, with the completion of a group assignment. There will be a strong interaction between theory and practice, with a central focus on visualizing and dealing with actual scenarios.

It is assumed that students attend classes regularly and are available for continued involvement beyond the classroom. This includes initiating or completing tasks agreed upon during class.

All supporting materials are available on the InforEstudante|Nonio platform. Other platforms that allow for interaction may also be used.

Statistical data analysis is relevant in numerous business contexts for the description and diagnosis of phenomena of interest, as well as for decision support. The goals and skills of the curricular unit of Data Analytics are focused on recognizing this potential.

Goals:

• Portray, statistically, the dataset under analysis.
• Generate insights relating to a phenomenon, by conducting descriptive or diagnostic analysis of the associated dataset.
• Adapt the descriptive techniques to be applied in the case of time series.

Skills:

• Recognize situations that may benefit from a descriptive, diagnostic or time series analysis.
• Identify, among a set of basic statistical techniques, those suitable for processing a certain dataset.
• Build a data dictionary composed of fundamental metadata for the analysis to be carried out.
• Carry out a summary assessment of the quality of the data based on the defined quality dimensions.
• Perform univariate and bivariate descriptive analyses.
• Transform and/or engineer new features that allow solving some more technical issues of the analysis and/or bringing new insights.
• Detect anomalies in a dataset.
• Execute descriptive analysis for time series data.
• Use software to support generate the results.

1. Background: goals of descriptive analysis and diagnostic analysis; particularities of temporal data.

2. Descriptive analysis
2.1 Univariate analysis
2.2 Bivariate analysis

3. Diagnostic analysis
3.1 Transformation and design of new features
3.2 Anomaly detection: missing values and outliers – first approach

4. Introduction to time series
4.1 Components
4.2 Trend lines
4.3 Decomposition

NAO

Fundamental:

• Curto, J.D. (2019). Potenciar os Negócios? A Estatística Dá uma Ajuda! (Muitas Aplicações em Excel e poucas fórmulas…), 3.ª Edição. Edição do Autor.
• Murteira, B., Ribeiro, C.S., Silva, J.A., Pimenta, C., Pimenta, F. (2023). Introdução à Estatística, 4.ª Edição. Escolar Editora.
• Webster, A. (2006). Estatística Aplicada à Administração e Economia. McGraw-Hill.
• Slides and worksheets available at InforEstudante|Nonio.

Complementary:

• Albright, S.C., Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. Cengage Learning.
• Alwan, L.C., Craig, B.A., McCabe, G.P. (2020). The Practice of Statistics for Business and Economics, 5th Edition. MacMilan.
• Bruce, P., Bruce, A. & Gedeck, P. (2020). Practical Statistics for Data Scientists, 2nd Edition, O’Reilly Media, Inc.
• Evans, J.R. (2020). Business Analytics, 3rd Edition. Pearson.