Ciência de Dados Aplicada à Gestão

Basic notions of statistics are recommended

The following teaching methodologies are used in this course:

1. Expository method: explanatory method where theoretical foundations and concepts are presented by the teacher and discussed with the class. Concepts and information will be presented to students through, for example, slide presentations or oral discussions. It will be used in classes to structure and outline the information.

2. Demonstrative method: based on the example given by the teacher of a technical or practical operation that one wishes to be learned. It focuses on how a given operation is carried out, highlighting the most appropriate techniques, tools and equipment. It will be used, for example, in practical and laboratory classes.

3. Interrogative method: process based on verbal interactions, under the direction of the teacher, adopting the format of questions and answers. It allows for greater dynamics in the classroom and consolidates learning. It will be used, for example, to remember elements of previous classes and in revisions of the lectured content.

4. Active methods: pedagogical techniques will be used in which the student is the center of the learning process, being an active participant and involved in his own training. The teacher assumes the role of facilitator, stimulating critical thinking, collaboration, creativity and student autonomy. They will be applied in classes to achieve a dynamic and more lasting learning environment.

At the end of the curricular period of this curricular unit, the student must:

– Identify the main concepts of data science applied to management

– Implement applications in the field of data science

– Using methods/algorithms in new data science problems and evaluating the results

– Evaluate and interpret the work carried out in the field of data science for business 

– Use the concepts and tools analyzed and discussed in class in future projects and the labour market 

S1 – Introduction to data science concepts applied to management

S2 – CRISP-DM methodology

S3 – Data exploration

S4 – Data preprocessing

S5 – Feature engineering

S6 – Models for data science problems applied to management

S7 – Evaluation of models and interpretation of results

1 - Periodic Evaluation:

a) individual or group practical assignments (50%);

b) an individual written test (50%)

2 - Exam Evaluation:

a) individual or group practical assignments (50%);

b) an individual written test (50%)

There is a minimum value of 40% for each component of the periodic and exam evaluation. Each evaluation method has different statements.

The practical assignments are followed by an oral presentation to the discipline's teacher for individual validation and defense and discussion of the options implemented.

The practical assignments will be evaluated based on the participation and proficiency of each student in the laboratory activities carried out and on the report prepared by the group.

The practical assignments must be carried out exclusively during the teaching period. Failure to obtain a minimum score of 40% in practical group assignments will result in the student not being admitted to the Evaluation by Exam, with the consequent failure of the curricular unit.

NAO