Course instructors are responsible for the course content descriptions in English.
General principles, contexts of application, data analysis methods, tools, and technologies; examination of practical cases.
The fundamentals of design, analysis and implementation of data structures and algorithms, applying the principles of abstraction and object-oriented decomposition is one of the most important pillars that you should know a software engineer. On this pillar will build other skills acquired in engineering and allow it to be a professional in this field.
Competencies and learning outcomes
- Capacity for continuous improvement, experimentation, and innovation.
- Capacity for evaluating, optimizing, and comparing criteria for decision making.
- Capability to recognize, understand, and apply the necessary legislation in the development of the technical engineer in computing profession, and manage specifications, regulations, and mandatory standards.
- Ability to solve problems with initiative, decision making, autonomy, and creativity. Capacity to know how to communicate and transmit knowledge, abilities, and skills from the technical engineer in computing profession.
- Capacity to analyze and assess the social and environmental impact of technical solutions, understanding the ethical and professional responsibility in the activity of the technical engineer in computing.
- Knowledge and application of the basic elements of economics and human resources management, project organization and planning, as well as legislation, regulations, and standardization in the field of computer projects.
- Knowledge about and application of the features, functionality, and structure of databases that allow for their appropriate use, and the design, analysis, and implementation of applications based on them.
- Knowledge about and application of the tools necessary for storing, processing, and accessing information systems, including those web-based.
- Knowledge about and application of the fundamental principles and basic techniques of intelligent systems and their practical application.
Objectives (Learning outcomes)
- 01To learn about data explotation principles
- 02To distinguis between DM aplication contexts
- 03To dessign Visual and Non-Visual Data analysis
- 04Data servers tools management
- 05Data analysis tools management
- 06Real applications analysis
Association between objectives and units
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Methodology and grading
- Lecture: Pass on knowledge and activate cognitive processes in students, encouraging their participation.
- Problem-based learning: Develop active learning strategies through problem solving that promote thinking, experimentation, and decision making in the student.
- Solving exercises and problems: Exercise, test, and apply previous knowledge through routine repetition.
- Part 1: Theory (between 40% and 60%) corresponds with the final exam score
Part 2: Practice (complementary to the front, between 60% and 40%): Individual and group exercises, self-assessment tests practical, class presentations, practical work with computer. All these activities can be evaluated by using custom revisions and multiple choice questions or development.
The final grade will be calculated from the corresponding percentages stated above, being essential to pass both parts separately, in order to pass the course.
If the student does not exceed either of these parts, but the weighted average gave superior or equal to 5, your final grade will be suspended (4). Failure to submit any of the parties, the final score is not met.