Ciência de Dados Aplicada à Gestão

– Principles of Statistics.

– Basic knowledge of information technology. 

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.

– Organize and interpret data, identify patterns and trends, apply data science methods/algorithms to problem-solving, and evaluate the results.

– Assess and interpret work conducted within the scope of data science applied to management. 

– Use the concepts and tools analyzed and discussed in class to draw useful conclusions that support well-founded decision-making.

1 – Introduction to data science concepts applied to management

2 – OSEMN methodology (Obtain/Scrub/Explore/Model/iNterpret)

3 – Data collection

4 – Data cleaning

5 – Data Exploration, including Preprocessing and Feature Engineering

6 – Models for data science problems applied to management

7 – Model evaluation and interpretation of results

• - a) practical assignements - 50.0%
• - Exam - 50.0%

• - Individual and/or Group Work - 25.0%
• - Exam - 75.0%

NAO