Base Knowledge
Applied Mathematics.
Teaching Methodologies
The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared considering active learning activities, to actively engage all 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. 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.
Learning Results
Statistical data analysis holds relevance across various business contexts, enabling description, exploration, diagnosis, and comprehension of real phenomena, crucial in guiding decision-making processes. The focus of the Data Analysis curriculum is precisely on recognizing and harnessing this potential.
The following learning outcomes are thus defined:
1. identify contexts and situations conducive to data-driven studies;
2. collect and statistically describe the dataset that supports the analysis;
3. extract insights regarding a phenomenon via exploratory analysis of the associated dataset;
4. identify and apply descriptive and exploratory statistical techniques to support tangible decision-making;
5. utilize software to implement statistical data analysis techniques effectively.
Skills:
Ability for analysis and synthesis, oral and written communication, problem solving and application of knowledge in practice.
Program
1. Introduction
1.1. Data types
1.2. Sampling
2. Fundamental data analysis
2.1. Univariate descriptive analysis
2.2. Bivariate descriptive analysis
3. Complementary data analysis
3.1. Transformation and creation of new variables
3.2. Multivariate descriptive analysis
4. Data analysis of temporal data
4.1. Components of time series
4.2. Trendlines
4.3. Decomposition
Curricular Unit Teachers
Maria Manuela Coelho LarguinhoGrading Methods
In accordance with the Academic Regulation of the 1st study cycle of IPC, the following assessment system is proposed
Assessment by Exam
- Individual written examination
- Date: Consult the map of exams
- Content: Programme as a whole
- Duration: 2 hours
- Marking: 20/20
- Entrance requirements: matriculation and enrolment requirements fulfilled
- Approval: Final classification is equal to the exam's classification, occurring approval with the final classification of at least 10 values
- Final grade: The student's grade is equal to the classification of the exam, except if the classification is higher than 19, in which case the student will have to take an oral test to defend the grade. If the student misses this oral test the final grade will be of 19 values.
Periodic Evaluation
- In the periodic evaluation, the student will have to do two written tests, on dates to be defined by the responsible teacher.
- Each of the tests is quoted to 10 values. In each test is required the minimum classification of 3 values. Not obtaining this grade in the first test invalidates the student's presence in the second test.
- For periodic assessment throughout the semester, only 8 absences from classes are permitted.
- The final classification of the periodic evaluation is the arithmetic sum of the grades of the two written tests, that is, Final Classification = CT1+CT2, where CT1 and CT2 are the classifications of the first and second tests, respectively, except if the classification is higher than 19, in which case the student will have to take an oral test to defend the grade. If the student misses this oral test the final grade will be of 19 values.
- If the student does not have the minimum grade in the second test, but the sum of the two tests is greater than or equal to 10 values, the final score is admitted to exam.
- For approval in the discipline it is necessary to obtain a final rating equal to or greater than 10 values.
Internship(s)
NAO
Bibliography
Fundamental:
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., & Cochran, J.J. (2019). Statistics for Business & Economics, 4th Edition. Cengage Learning.
Evans, J.R. (2020). Business Analytics, 3rd Edition. Pearson.
Jones, J.S., & Goldring, J. (2022). Exploratory and Descriptive Statistics. Sage.
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.