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. 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

In accordance with the Academic Regulations of the 1st Cycle of Studies of the IPC, the following two assessment regimes are proposed.

1) Periodic assessment

It consists of two tests to be carried out during the class period.

• Test 1
  Quotation: 6.5 points
  Minimum classification required: 2.0 points
• Test 2
  Quotation: 6.5 points
  Minimum classification required: 2.0 points
• Test 3
  Quotation: 7.0 points
  Minimum classification required: 2.0 points

Notes:

• To access this assessment regime, class attendance is not mandatory, although attendance is strongly recommended.
• The tests are administered on a computer using the Microsoft 365 platform, with institutional access required. Therefore, students must ensure that their password is up-to-date. Please note that, if it hasn't been changed, it is different from the password used to access InforEstudante|Nonio.
• The tests will also require students to use the software that supports statistical analysis introduced in class, specifically Microsoft Excel and JASP (https://jasp-stats.org/).
• The tests are closed-book.
• The contents to be evaluated in each test are those summarized up to the lesson prior to the test.
• If a student misses a test, regardless of the reason, a new test will not be scheduled and the grade will be assigned 0 (zero) points.
• In this regime, the student's final classification is obtained by adding the classifications of the three tests, as long as they are equal to or higher than the minimum classifications required.
• Failure to comply with the minimum classification requirement will result in the student failing this assessment regime.

2) Assessment by exam

• This evaluation regime consists of an exam, quoted for 20 points, with the date of completion according to the exam schedule.
• The exam is administered on a computer using the Microsoft 365 platform, with institutional access required. Therefore, students must ensure that their password is up-to-date. Please note that, if it hasn't been changed, it is different from the password used to access InforEstudante|Nonio.
• The exam will also require students to use the software that supports statistical analysis introduced in class, specifically Microsoft Excel and JASP (https://jasp-stats.org/).
• The exam is closed-book.
• The student's final grade is equal to the exam grade.

Final rule:
A passing grade is awarded under any assessment regime if the final mark, rounded to the nearest whole number, is equal to or greater than 10 points.

NAO