The Data Science and Artificial Intelligence program trains professionals who actively participate in multidisciplinary teams to design and implement data-driven and artificial intelligence-based solutions that address the needs of diverse groups in our society.
Mission
To prepare professionals in the data science and artificial intelligence areas through a comprehensive and competitive training enhance their skills on analysis, design, implementation, research, and innovation of data-based technological solutions that contribute to improving life quality and that promote the sustainable and equitable development of our country and region, by considering social, economic, and environmental aspects within the professional ethical framework.
Core Components of the Program
The Data Science and Artificial Intelligence program includes a comprehensive set of knowledge, skills, and methods that graduates must acquire upon completing the overall training program. The Association for Computing Machinery (ACM) has defined a set of knowledge areas that are linked to the program’s courses to develop the skills that a graduate of our program should possess.
- Data analysis and presentation.
- Artificial intelligence.
- Big data systems.
- Data acquisition, management, and governance.
- Data mining.
- Machine learning.
Study Modality
- First four semesters of the program are in-person, and the last four semesters are online.
- Assessments will be both online and in-person.
* Undergraduate tuition/fees:
The Constitution of the Republic of Ecuador in its Article 356, among other principles, establishes that third-level public higher education will be tuition/fees free. Zero cost education is linked to the academic responsibility of the students.
Number of admitted students per academic year
Number of graduates per year
Number of enrolled students per academic year
Graphs show the figures in real time, at the time of the query
The program is not yet internationally accredited, because it was recently created and has not yet had its first cohort of graduates, a condition required by accrediting bodies.
The program’s academic has been developed in accordance with internationally recognized quality standards, with the goal of beginning the accreditation process once the necessary criteria are met.
High school graduates who apply for the Data Science and Artificial Intelligence program must be curious, imaginative, creative, and innovative students who possess the following:
- Basic computer science skills.
- Numerical aptitude and basic knowledge of mathematics.
- Ability to abstract, analyze, synthesize, and apply logical reasoning.
- Willingness to work as a team.
- Reading comprehension in English.
- Strong perception and attention skills.
EDUCATIONAL OBJECTIVES
The Data Science and Artificial Intelligence Engineering alumni from the Escuela Superior Politécnica del Litoral will have in the 3-5 years period the following professional practices:
- Solve professional challenges at a global level by applying the fundamentals of their profession and innovation and by considering social, economic, and environmental aspects, while adhering to ethical and moral principles.
- Successfully propose and lead the development and implementation of solutions related to their discipline and contributing valuable solutions to various segments of society.
- Acquire and enhance technical and scientific skills and knowledge throughout their professional lives, drawing on contemporary aspects of research and entrepreneurship.
- Contribute to improved productivity through data-driven solutions.
- Apply communication and writing tools in Spanish and English.
STUDENT OUTCOMES
SO 1. Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions
SO 2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
SO 3a. Communicate effectively in Spanish in a variety of professional contexts.
SO 3b. Communicate effectively in English in a variety of professional contexts.
SO 4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
SO 5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
SO 6. Apply theory, techniques and software throughout the data science lifecycle and employ the resulting knowledge to meet the needs of those involved.
First Year
Description:
It is a core course for Engineering, Natural Sciences, Exact Sciences, and Social Sciences and Humanities, students. Topics, such as, topological notions, limits and continuity of real variable functions, derivatives and their applications, antiderivatives and integration techniques, and the definite integral with its applications, are examined. This course is aimed to the development of student’s skills and know-how in the derivation and integration processes, as a fundamental basis for the following upper level courses in its academic training process
Description:
In this course, students apply the Design Thinking methodology to identify, analyze real-life problems or needs, to design innovative solutions. Students work in multidisciplinary teams to present solution proposals that add value to customers/users from private companies, public organizations and non-profit organizations.
Description:
The course presents students with strategies to solve common problems in various professional fields through the design and implementation of solutions based on the use of a programming language. It covers the basic principles so that the student can read and write programs; emphasizing the design and analysis of algorithms. In addition, it introduces students to the use of development and debugging tools.
Description:
This is a basic training course for engineering and science students, in which the study of matrices, systems of linear equations, vector spaces, linear transformations, spaces with inner product, eigenvalues and eigenvectors. The main purpose of the course is to contribute to the integral formation of the future professional allowing later, to take care of conceptual, social and technological necessities in the solution of problems and development of the abstract thought.
Description:
This basic and general education subject presents grammatical structures to produce a simple paragraph based on a writing program. Additionally, it allows the identification of a specific argument in oral and written communication. It also considers learners’ personal opinions about different topics related to social, academic, and professional aspects. It includes the necessary vocabulary to make comparisons between present and past, books or movies description, creation of simple students’ profile, opinions about inventions, formal apologies and tell past events.
Description:
Vector calculus is a course aimed at the basic training of professionals in the areas of Engineering, Exact Sciences and Natural Sciences who developed problem-solving and problem-solving skills in the n-dimensional context. For this purpose, the course consists of 5 general themes: three-dimensional analytic geometry and functions of several variables, differential calculus of scalar and vector fields, optimization of scalar functions of several variables, line integrals and multiple integration, surface integrals and theorems of the vector theory; being the main applications of this course: the optimization of functions of several variables applied to practical problems, the calculation of lengths, area, volumes, work and flow, using objects of the plane and space.
Description:
This professional training course introduces students to concepts related to the object-oriented programming paradigm and graphical user interfaces programing. Student designs and implements software solutions to real world problems using an object-oriented programming language. Concepts are reinforced with hands-on exercises in weekly lab sessions and challenging programming tasks. It also covers data persistence, error handling and an introduction to multithreading programming.
Description:
During this course, you will examine the entity-relationship (ER) diagram, which allows the conceptualization of the requirements. Furthermore, the ER diagrams allow the formal modeling to implement a relational database (RDBMS). In addition, you will use structured query language for data manipulation. You will understand an optimization plan of a query execution through relational algebra. Finally, you will address basic security concepts and new alternatives in developing applications with relational databases.
Description:
This subject of basic formation and general education presents the grammatical structures for the production of an academic paragraph, through the development of the writing program in a transversal way. In addition, it allows the identification of specific arguments in oral and written communication, considering the production of one's own criteria on different topics of a social, academic or professional nature. The necessary vocabulary is also applied to refer to the different forms of communication, share work experiences and the use of digitl technology, tell short stories about interpersoanl relationship and personalities, and comment on the future of the environment.
Second Year
Description:
This professional training course introduces the fundamentals of analysis, design, and implementation of elementary data structures used in computer science. The course studies algorithms for manipulating and organizing data into structures, with particular emphasis on computational efficiency and appropriate coding styles. Likewise, it poses real problems of medium complexity so that students design and implement solutions using appropriate data structures.
Description:
This course is part of the professional development area of the curriculum and develops strategies related to the design and implementation of solutions where technology is used. These strategies follow a design process that includes user research, problem definition, prototyping, and evaluation of proposed solutions. This course complements de professional strengthening of our students in the area of software design. In addition, the course enables the exploration of diverse ways to solve problems using creativity and innovation.
Description:
In this subject, we study the development of the academic prosumer profile of the students, which should be consolidated throughout each individual's life, based on the processing of complex, holistic, and critical thinking. We aim to foster understanding and the production of academic knowledge through rigorous analysis of realities and readings from various academic/scientific sources.
Description:
The Statistics I course allows students to improve their ability to analyze, synthesize and solve problems, by studying the statistical foundations for obtaining information from a set of data, starting from their information gathering, procedure, analysis and interpretation of the data obtained. Also, the concept of probability will be studied as a measure of uncertainty, mathematical models of discrete and continuous random variables will be analyzed univariate and multivariate, to finally consolidate the bases of inferential statistics
Description:
This subject of basic instruction and general education presents grammatical topics for the elaboration of an outline and a structured composition, through the development of the writing program in a transversal way. In addition, it allows the identification of arguments in oral and written communication on contemporary and academic topics. Additionally, appropriate vocabulary is applied to discuss issues related to different cultures, places where we live, everyday news, entertainment media, and past and future opportunities.
Description:
This course provides an introduction to algorithm design and algorithm analysis. Covers formal techniques to determine algorithms’ efficiency in addition to algorithm design strategies that allow to solve new computational problems. Moreover, this course covers computational complexity topics and ways to reduce new computational problems to known ones to be solve efficiently through algorithms.
Description:
This professional training course introduces to web and mobile applications design and implementation complying with current standards and good programming practices. The design and modeling of applications that perform asynchronous web requirements between the client and the server are addressed.
Description:
This professional core course addresses the importance of software engineering, the software development life cycle, and the application of a software development process model, with an emphasis on planning, managing, requirements analysis, and the design of a medium complexity software system. In addition, technical and non-technical skills are developed. The first group of skills includes the application of methods and tools to plan a software-development project, the analysis of the needs of a real customer and the design of a software system, both architecturally and in detail. The second group of skills includes teamwork, the ethical practice of the profession, and oral and written communication.
Description:
Statistics II is a transversal subject of basic training for engineering and bachelor's degree students, it addresses the knowledge necessary to make inferences of a population through a sample by point estimations, intervals or hypothesis tests, so that the student will be able to solve complex situations through comparisons or establishing relationships between variables in a sample, thus facilitating the decision-making process from a statistical point of view.
Description:
This subject of basic formation and general education, presents the grammar structures to produce a persuasive essay, through the transversal development of the writing programme. In addition, it allows students to identify specific arguments in the oral and written communication, as well as, to express their own opinions about different topics of social, academic, or professional fields. It also includes the necessary vocabulary to stablish a conversation, narrate situations of their environment, activities to reach their goals, analyze cause and effect and personal and professional opportunities.
Third Year
Description:
This basic training and general education subject presents the necessary structures for the production of a persuasive essay, through the development of the writing program in a transversal way. It also allows the identification of specific arguments both in oral and written communication, in order to issue students' criteria on social, academic, or professional issues. They also apply the necessary vocabulary to engage in discussions about choices to make, changes in daily life and home, financial problems as well as moral dilemmas and achievements in the course of their personal, student, and professional lives.
Fourth Year
Additional
HUMANITIES ELECTIVE COURSES
1 credits - 1.9 ECTS
ARTS, SPORTS AND LANGUAGES ELECTIVE COURSES
1 credits - 1.9 ECTS
SELECTED ELECTIVE COURSE
3 credits - 5.8 ECTS
SELECTED ELECTIVE COURSE
3 credits - 5.8 ECTS
Professional Skills and Occupational Profile:
The Data Science and Artificial Intelligence Engineer will be able to design, implement, and manage data-driven technological solutions. The professional will have the following skills:
- Entrepreneur.
- Manager of a company that provides data-driven solutions.
- Director of data-driven projects.
- Advisor and consultant for the development of data-driven systems and projects.
- Data-driven systems auditor.
- Graduate student.
In order to obtain the degree in Data Science and Artificial Intelligence Engineering, students must fulfill the following requirements:
- Complete a minimum of 58 credits in Professional Unit courses.
- Complete a minimum of 24 credits in General Education courses.
- Complete a minimum of 15 credits in Mathematics and Basic Science courses.
- Complete a minimum of 6 credits in Selected Elective courses.
- Complete a minimum of 2 credits in Elective courses.
- Accumulate a minimum of 336 hours of Internship Experience, distributed as follows:
- 240 hours of Pre-professional Business Internships (5 credits).
- 96 hours of Community Service Internships (2 credits).
- Successfully complete the Curricular Integration Unit, which includes:
- Capstone course (3 credits).
- Capstone project (5 credits), equivalent to 240 hours.
The Capstone Project is a culminating requirement for graduation. These projects provide students with the experience of applying acquired knowledge and skills to the needs of society, with a focus on sustainability.
The IDEAR Fair showcases all Capstone projects, offering students a valuable opportunity to showcase their work and hone soft skills such as communication and teamwork. It is also a space for students to network with potential clients and future employers.
Explore all of the Capstone projects completed by the Data Science and Artificial Intelligence Engineering program.
DID YOU KNOW?
The World Economic Forum recognizes Artificial Intelligence and Machine Learning specialists as one of the fastest-growing future jobs. This program trains professionals capable of manipulating and transforming large volumes of data to design and implement AI-based machine learning models to identify patterns in text, market transaction data, images, sensor data, and many other data sources, thereby generating data-driven predictions that support decision-making in various industries' digital transformation processes.
