Data Exploratory Analysis

This course assumes basic knowledge of Statistics equivalent to the content covered in undergraduate Statistics courses, namely:

• PK1. Knowledge of descriptive statistics.
• PK2. Ability to interpret statistical tables, graphs, and indicators.
• PK3. Basic knowledge of statistical inference, including estimation and hypothesis testing.
• PK4. Basic computer skills and proficiency in the use of common software applications.

• TM1. Theoretical-Practical Classes: In accordance with the curriculum, classes are theoretical-practical in nature and integrate the introduction of fundamental concepts with their practical application, promoting the achievement of the course learning outcomes.
• TM2. Interactive Lectures: The introduction of concepts, methods, and fundamental results will be delivered through interactive lectures, complemented by the worked solution of illustrative examples and questions designed to promote active student participation.
• TM3. Guided Problem Solving: The practical component will focus on problem-solving and the application of statistical techniques to datasets using statistical software, under the instructor’s guidance, while encouraging independent learning and collaborative work in small groups.
• TM4. Integration of Theory and Practice: The theoretical and practical components will be delivered in an integrated manner, enabling the progressive application of acquired knowledge to the solution of real-world problems and the development of the competencies defined for the course.
• TM5. Learning Resources: All supporting materials will be made available through the InforEstudante|Nonio platform. Additional digital platforms may also be used whenever appropriate to support teaching activities and promote interaction with students.

Statistics is a well-established scientific discipline with applications across a wide range of fields, particularly in marketing, management, and international business. Within this context, this course aims to develop students’ competencies in the application of statistical methods for data analysis and interpretation, using statistical software to support research and data-driven decision making.

Learning Outcomes:

• LO1. Plan the different stages of the statistical method, including problem identification, data collection and preparation, and the selection of appropriate statistical techniques.
• LO2. Apply statistical techniques using specialized software, extracting and interpreting the results obtained.
• LO3. Critically interpret and evaluate statistical results, determining their contribution to addressing the research problem and supporting data-driven decision making.

Competencies to Be Developed:

• SC1. Problem Identification and Analysis Planning: Identify research problems and select the most appropriate statistical techniques for the context under analysis.
• SC2. Statistical Analysis Using Software: Apply statistical techniques using specialized software and accurately interpret the results obtained.
• SC3. Critical Thinking and Results Communication: Critically evaluate statistical results and communicate conclusions clearly, rigorously, and based on evidence.

• P1. Introduction to Statistics
  1.1. Univariate and bivariate descriptive analysis
  1.3. Estimation and hypothesis testing
• P2. Multivariate Statistics
  2.1. Factorial analysis
  2.2. Clusters analysis
• P3. Econometric Models
  3.1. Regression models
  3.2. Extension of the regression models

In accordance with the Academic Regulations for the Second Cycle of Studies of the Polytechnic University of Coimbra (IPC), assessment in this Course Unit comprises two mandatory components: a practical assignment and a written examination.

MA1. Practical assignment (40%)

• Weight: 8 points.
• Mandatory component.
• Open-book assessment. Students may consult the materials made available through InforEstudante|Nonio, as well as notes prepared by themselves, with no restrictions on content.
• During the regular assessment period, the practical assignment is completed in groups of 2 to 3 students, in person, during the penultimate class of the trimester. The assignment brief and dataset are provided at the beginning of the class, and students have the duration of the session to complete the proposed task using the software employed throughout the course for data analysis.
• During the make-up and special examination periods, the practical assignment is completed individually, in person, on the day of the examination session, based on a new assignment brief and a new dataset provided at the beginning of the session.

MA2. Written examination (60%)

• Weight: 12 points.
• Mandatory component.
• Held on the date specified in the official examination timetable.
• During the examination, students may consult up to two A4 sheets (double-sided), handwritten and/or printed, containing a summary of the course contents prepared by the student, with no restrictions on content.
• The examination consists of questions that include outputs generated by the software tools used throughout the course and also requires the use of the software employed during classes to solve some of the questions.

Final grade formula

FG = MA1 + MA2

where MA1 corresponds to the practical assignment grade (0-8 points) and MA2 corresponds to the written examination grade (0-12 points).

Final assessment notes:

• Course unit approval: Students are deemed to have successfully completed the course unit if they obtain a final grade, rounded to the nearest whole number, of 10 points or higher.
• Platform access during assessments: Students must know their password(s) and be able to access the InforEstudante|Nonio platform.
• Validity of the practical assignment: The grade obtained in the MA1 practical assignment during the regular assessment period remains valid for the make-up and special examination periods.
• Practical assignment during the make-up and special examination periods: Students who did not complete the MA1 practical assignment during the regular assessment period must complete an individual practical assignment on the day of the examination session for which they are registered. In this case, students complete the written examination (MA2), followed immediately by the individual practical assignment (MA1).
• Course unit grade improvement: For the purpose of improving the final course unit grade, students may choose one of the following options:
• a) Sit a new written examination while retaining the practical assignment grade.
• b) Sit a new written examination and complete a new individual practical assignment.
• Guidelines on the use of artificial intelligence: The use of Generative Artificial Intelligence tools is not permitted during any assessment component of the course unit.
• Academic misconduct: If there are reasonable grounds to suspect academic misconduct identified during the marking process, the student will be required to attend an oral examination to verify the knowledge and competencies demonstrated. This assessment will be conducted by a panel comprising the course instructor and two additional faculty members from the same academic area. If the student is unable to demonstrate the knowledge and competencies evidenced in the written assessment, that assessment will be declared null and void.
• Working students: Assessment of students holding Working Student Status is governed by the Academic Regulations for the Second Cycle of Studies of the IPC.

NAO