Data Analysis for Business Sciences

• CB1. Command of elementary descriptive statistics concepts (types of variables, measures of location and dispersion, frequency tables and graphical representations), at the level of the various Mathematics syllabuses across the different branches of compulsory education in Portugal (http://www.dge.mec.pt/).
• CB2. Basic instrumental skills in the use of spreadsheet software, including data entry and organisation, use of simple formulas and chart construction.

The classes are, in accordance with the curriculum plan, theoretical-practical, designed to actively engage the student throughout the entire session. The methodologies adopted are student-centred and combine the exposition of concepts with their immediate application to real data, prioritising interaction, questioning and guided problem-solving.

• ME1. Interactive lectures: structured introduction of concepts, fundamental results and methods, interspersed with tasks that elicit participation from all students – posing questions to and by students, orally and/or through a digital platform, and proposing debates/discussions in small groups on an exposed aspect or topic.
• ME2. Commented demonstration of procedures: step-by-step, critically commented exemplification of the application of data analysis techniques using statistical software, highlighting methodological decisions, the reading of results and their interpretation.
• ME3. Guided problem-solving: work on concrete, real situations, individually or in small groups, under the guidance and tutoring of the lecturer, encouraging autonomous work. It gives a central role to the visualisation and handling of data and fosters the transfer between theory and practice.
• ME4. Tutorial guidance and continued support: support for the consolidation of learning and the clarification of doubts, inside and outside the classroom, assuming regular attendance and the continuity of the student’s involvement beyond classes, with the initiation or completion of agreed tasks.

In an increasingly data-driven business environment, statistical data analysis has become indispensable for describing and exploring the business reality and diagnosing the problems that emerge within it, underpinning informed decision-making. This curricular unit introduces the student to this analysis, seeking to equip them with the techniques and critical judgement needed to turn data into useful knowledge. This purpose is realised in the learning outcomes and skills set out below.

Upon completion of this curricular unit, the student should be able to:

• OA1. Identify business decision contexts and situations that may benefit from a data-based study, selecting the appropriate analysis techniques.
• OA2. Describe a dataset statistically, through univariate and bivariate descriptive analysis.
• OA3. Apply techniques for transforming and creating new variables and for multivariate analysis to deepen data exploration.
• OA4. Analyse time series data, identifying their structural components and their evolution over time.
• OA5. Critically interpret the results of a data analysis, drawing relevant conclusions to support decision-making.
• OA6. Use statistical analysis software to support the implementation of data analysis techniques.

Skills to be developed:

• CD1. Data literacy: the ability to work with real data throughout the entire analysis cycle, from its organisation to the drawing of well-founded conclusions.
• CD2. Instrumental competence: the ability to autonomously use statistical analysis software in solving concrete problems.
• CD3. Critical thinking: the ability to assess the suitability and correct application of the analysis techniques and to judge the plausibility of the results in the context of the problem under analysis.
• CD4. Technical communication: the ability to describe and justify, clearly and rigorously, the procedures and conclusions of a data analysis.

• P1. Introduction
  • 1.1. Types of data
  • 1.2. Sampling
• P2. Fundamental data analysis
  • 2.1. Univariate descriptive analysis
  • 2.2. Bivariate descriptive analysis
• P3. Complementary data analysis
  • 3.1. Transformation and creation of new variables
  • 3.2. Multivariate descriptive analysis
• P4. Temporal data analysis
  • 4.1. Components of time series
  • 4.2. Trend lines
  • 4.3. Decomposition

In accordance with the Academic Regulations of the 1st Cycle of Studies of the Polytechnic University of Coimbra (https://www.iscac.pt/aluno-a/regulamentos-e-legislacao-academica/), the following two assessment regimes are proposed: periodic assessment and assessment by exam.

A) PERIODIC ASSESSMENT

MA1. Individual Tests: Each test graded from 0 to 20 points. Minimum classification of 6.0 points per test. Tests carry equal weighting. Closed-book. Artificial Intelligence (AI) guideline: Level 0 - Prohibited (no use of AI).

Notes on periodic assessment:

• Structure: Periodic assessment consists of a set of individual tests distributed over the teaching period. The tests are held in class or in periods set aside specifically for that purpose, and the last one may coincide with the date of the normal-period exam, depending on date and room availability. The exact number of tests and their dates will be announced to students in class and on the InforEstudante platform until the end of September.
• Registration: Students wishing to sit the tests must register, without exception, between 15 and 30 September, via a link to be announced on the InforEstudante platform.
• Attendance: No minimum class attendance is required for access to, or a pass under, this assessment regime, although attendance is strongly recommended.
• Final Classification Formula (FC): FC = (T1 + T2 + ... + Tn) / n, where n is the number of tests set for the academic year and T1, …, Tn are the classifications obtained in each of them.
• Passing condition: A pass under this regime is only possible if the classification obtained in each test is equal to or higher than 6.0 points. Failure to meet this requirement in any test results in failing this assessment regime, with the classification NRC (Requirements Not Met) being assigned. A student who does not pass under the periodic assessment regime moves to the assessment-by-exam regime.
• Absences: If a student misses a test, regardless of the reason, no make-up test will be scheduled and a grade of 0 (zero) points will be assigned.

B) ASSESSMENT BY EXAM

MA2. Individual Exam: Exam graded from 0 to 20 points. Date set according to the exam schedule. Closedbook. Artificial Intelligence (AI) guideline: Level 0 - Prohibited (no use of AI).

• Final Classification Formula (FC): FC = MA2, corresponding to the classification obtained in the exam.

Final rules applicable to both regimes:

• Under either assessment regime, a pass is awarded with a final classification, rounded to the nearest whole number, of at least 10 points.
• For assessment-by-exam purposes, classifications obtained in the periodic-assessment tests are not carried over.
• Improvement of the final classification requires sitting an exam in the period for which the student registers for that purpose.
• In the event of a well-founded suspicion of fraud detected during the marking of an assessment, the student will be summoned to an oral examination to verify the knowledge and skills demonstrated. This verification is carried out by a panel composed of the course lecturer and two further lecturers from the same area. Should the student fail to demonstrate the knowledge and skills shown in the written assessment, the assessment will be annulled.

NAO