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Master’s in Data Science & Business Analytics

Live online classes
16-month course
2 weekly sessions
October, 2024

Data Scientist, Data Analyst, Data Engineer

Official Qualification in Panama, Colombia and Ecuador from the Metropolitan University of Education, Science & Technology (UMECIT).
More Info
Study Plan

Study Plan

0. Prework

This Prework stage introduces concepts that the program will take to greater depth, so students are well grounded from the first day and the entire group has an even level, which allows for further and better progress, as well as improving cooperation between all participants.

  1. Computer basics: Concepts including hardware and software, CPU, memory, storage devices, operating systems and networks.
  2. Introduction to programming languages: Explanation of what a programming language is, what it is used for, and the types of languages that exist (compiled and interpreted). An overview of the most commonly used languages today, and why they are used.
  3. Fundamental programming concepts: Discussion of variables, data types, operations, control flow structures (if/else, loops), and functions. How to break down a complex problem into smaller, more manageable subproblems. Issues are explained in a basic way to avoid overlapping with the Programming Fundamentals module (with Python).
  4. Development tools and good practices. Introduction to the use of an IDE, such as PyCharm or VSCode, as well as notebooks. Discussion about version control with Git. Underlining good programming practices, such as the importance of commenting code and following style conventions (Pythonic Code).
  5. Introduction to data structures: Overview of concepts such as arrays, lists, sets, dictionaries/maps and trees. The focus is not confined to a specific programming language, but also at pseudocode level. The aim is for students to understand what they are, what they are used for, and when it might be appropriate to use one data structure over another.
  6. Fundamental database concepts: Explanation of what a database is, what it is used for, and the various types that exist (e.g. relational and non-relational databases). Introduction to key concepts such as table, record, field, primary key, and relationships between tables.

Review of the key programming concepts needed to process and use data by means of code. Introduction to R programming language and an extensive overview of the capabilities that Python offers.

  1. Fundamental concepts of Python and libraries for data science: Numpy, Pandas.
  2. Python - intermediate and advanced.
  3. Data processing and visualization with Python.



This module examines what databases are and discusses the main types. Students explore the world of relational database modeling and learn to program in SQL. In addition, the module looks at ETL processes and how they are designed and implemented.

  • Database design.
  • SQL Standard I
  • SQL Standard II
  • The data warehouse and ‘extract, transform and load’ (ETL) tools and processes.



Review of the life cycle of data and how it affects the data analysis process. Students also explore the world of data visualization and learn to design dashboards in PowerBI.

  1. Data quality and life cycle.
  2. Data preparation and pre-processing.
  3. Visualization tools and techniques I
  4. Visualization tools and techniques II



This module examines big data analysis as a tool to address key research questions and issues. Students will understand what is meant by big data, how it has developed over time, the causes that have led to the emergence of big data technologies and how it compares with traditional business intelligence.

  1. Introduction to the world of big data.
  2. Business intelligence vs big data.
  3. Big data technologies.
  4. The value of data and applications across sectors



Discover and use the tools that comprise the ecosystem in order to process vast quantities of data. These include Spark, Hadoop, and NoSQL databases.

  1. HADOOP and its ecosystem.
  2. SPARK
  3. NoSQL databases
  4. Cloud platforms



In this module students learn the fundamental concepts of programming in R, a programming language for statistical computing. The main statistical concepts essential for data analysis are also introduced here.

  1. Introduction to statistics.
  2. Probability and sampling.
  3. Inference and lineal regression.
  4. Experiment design.



Students learn the fundamental concepts and algorithms of machine learning, one of the cornerstones of data science and artificial intelligence.

  1. Machine learning tools and the techniques and applications of supervised learning.
  2. Techniques and applications of unsupervised learning.
  3. Deep learning models and techniques.
  4. Cloud solutions for machine learning.



The purpose of the Directed Research I module is to enable students to see that research is a systematic and ordered process aimed at managing knowledge. Consequently, during every academic training process, it is essential to carry out research work that is geared towards the needs of a given context and that demonstrates the student's research capabilities.

  1. The research question.
  2. The rationale for the research.
  3. Establishing research objectives.
  4. The contextual and temporal scope of the research.



Students examine the concept of artificial intelligence, its meaning and the types of problems it can solve. Identification of decision-making techniques (expert systems and supervised learning), as well as their applications. Analysis of reinforcement learning, its life cycle, the most important components, and the types of problems it solves.

Artificial intelligence and applications in decision making. Reinforcement learning and its applications. Techniques and applications of natural language processing (NLP). Recommendation systems and applications.



Analysis of the concept of the digital transformation from the point of view of the technologies that drive it, with a special focus on the following trends: Big Data, Artificial Intelligence, Blockchain, Internet of Things, Industry 4.0 and Smart Cities.

The Digital Transformation. Blockchain. Internet of Things. Industry 4.0 and Smart Cities



How analytics can be applied to specific scenarios. Discovering how specialized analytical methods can be applied to different types of data.

Scalable analytics. Analysis of social networks and the Internet of Things. Analysis of the financial area and customer service. Analysis of information retrieval techniques.


  • Recognizing types of research in line with the knowledge generated.
  • Study of research designs.
  • Study of the techniques and instruments of data collection during the research process.
  • Study of the criteria that characterize the study units and study population.
  • Building data collection instruments.
  • Applying reliability and validity tests to the data collection instrument(s).
  • Collecting data according to the criteria defined in the study.
  • Understanding the data analysis process and its stages.
  • Identifying analysis techniques to use in line with data codes (verbal or numerical codes).

Data collection techniques. Data collection instruments. Research study units and study population. Instrument validity and reliability procedure. Data analysis techniques.



At Master's level, the written work must be presented and students must generate scientific products from their research experience. Students are responsible for organizing the presentation of the written work.

Introduction Formal issues relating to the presentation of the work. Institutional procedures. Viva voce. Publication.


*The academic program may be subject to changes in line with the changing demand for specific skills in the market. Your employability is our goal.
Storytelling como recurso pedagógico

Una experiencia de aprendizaje única

¿Alguna vez te imaginaste formando parte de los grandes de la industria tecnológica? ¿Quieres convertirte en un experto en análisis de datos capaz de resolver problemas complejos y tomar decisiones estratégicas basadas en datos? Con este máster, podrás vivir una experiencia educativa inmersiva y adentrarte en el apasionante mundo del Big Data y la Inteligencia Artificial.

A través de un enfoque práctico y basado en el storytelling, nuestro programa te permitirá poner a prueba los conocimientos adquiridos en un entorno profesional simulado.
Outstanding Mentors


Ana Patricia Garcia

IT Internal Control Officer

Master’s in Data Science Director

Javier Castellar


Layla Scheli

Analista de BI, Big Data y Data Science

Paul Kuhle

CTO Collisio Tehnologies

Leon Beleña

Senior Data Scientist & Associate Professor

Manuel Lopez Pavón


*We are always on the lookout for the best professionals in the sector, so the team may vary from one edition of the course to another

Academic Information

Specialize from scratch in data science and become an expert: non-tech skills, code, data science, AI, and machine learning. Upskill progressively through an innovative program and benefit from our online and collaborative learning methodology.

Program Aims
  • Programming languages: Python, R, and SQL.
  • Extract, process and analyze data for decision making using the latest techniques and tools.
  • Learn to manage projects based on data science and big data. Promote advanced data analytics initiatives across different areas of the business.
  • Get a comprehensive and transversal vision of big data and cloud solutions.
  • Generate reports, dashboards and visualizations of data.
  • Anticipate and detect patterns, trends and causes using predictive analytics and machine learning.
  • Master the strategic application of data science in areas such as marketing, CRM, banking and finance, operations, HR, and the IoT among others.
Competencias profesionales del programa
  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Experto en visualización de datos
  • Experto en arquitecturas de almacenamiento y procesamiento de datos
  • Experto en machine learning
  • Experto en Business Intelligence
Career Readiness

The comprehensive training we deliver to our students thoroughly prepares them for the employment market. Through a personalized syllabus, we help them develop professional skills, establish relationships with companies and sail through recruitment processes.

Learning By Doing Methodology

It focuses on the practical application of knowledge and skills to foster meaningful and lasting learning.

Una experiencia única

Utilizamos el storytelling para crear experiencias de aprendizaje memorables que conecten con las emociones de nuestros estudiantes.

The industry is on fire
+84% Improved Employment Status
+40 Monthly Job Offers
94,5% Employability
+4,7 Job Offers/Student

An innovative and vibrant Tech Hub

We are not conventional and our campus even less so.
Designed to replicate an ecosystem of startups and tech companies, we have created a Silicon Valley oasis in the heart of Madrid. Come and check it out.

Visit the Campus
Paseo de la Castellana, 89
Co-working spaces
Meeting rooms
Rest areas
Digital classrooms
Our students are working in
Marta Pérez

Marta Pérez
Founding Partner & Managing Director at Emetepé Tech&Services S.L

"I know that I made the right choice, since everything from the management to the teaching staff, the planning, quality of teaching, and even the center itself, have exceeded my expectations."

Kay Kozaroneklogo universitat manhheim

Kay Kozaronek
Artificial Intelligence Engineer & Machine Learning Researcher

"The combination of theory and hands-on work, the incorporation of human sciences and the quality of the teaching staff are undoubtedly the strong points of IMMUNE."


IMMUNE Financing

Full Payment
Pay for the course in a single installment and receive a 5% discount.

9 / 16 Interest-free Installments
Payment can be made in 9 installments for on campus courses and 16 installments for online master’s courses.

External Financing

Pay in installments, even if you are unemployed and cannot guarantee the loan.

Pay in installments, even if you are unemployed and cannot guarantee the loan.

Pay for your training through the Spanish Employment Training Foundation
*Aimed at active workers who wish to finance their program through the subsidized training program.


We are here to answer your questions!

What are the admission requirements?

It is not necessary to demonstrate any prior training for admission, only to go through the admission process consisting of an evaluation of your resume and a personal interview with our admissions team.

Will the tools I need be included in the price of the program?

The tools used throughout the program are licensed for free use, in some cases because we use educational licenses and in others because it is free software.

Is there a careers and employment guidance service?

We have an employability area which, through our Talent Hub program, is responsible for supporting the efforts of our students to enter the employment market. The services we offer include resources to help you search for and prepare for interviews, English tests, resume and/or Linkedin profile guidance, interview and elevator pitch training, and access to our exclusive internship and employment pool.

Do I need prior knowledge or experience?

No prior knowledge is required since all programs start from scratch. It is advisable however, to have user-level knowledge and a keenness for technology.

What are the requirements for my computer?

You will need to have access to a laptop with a camera, microphone and minimum requirements of 8 GB of RAM and an i5 processor.

What is the Capstone Project?

The final project is where everything you have learned throughout the program is applied and consolidated. You will present the project to a panel of professionals from companies in the sector, which represents a unique opportunity for students to demonstrate their knowledge to potential employers and also to network.

What certification or qualification will I receive on completion of the course?

Once you have finished and passed the program, you will receive a diploma issued by IMMUNE Technology Institute in digital format and verifiable using blockchain technology.

Are there grants or scholarships available?

Yes, there are scholarships or study grants as well as financing options depending on students’ circumstances. Check out our scholarship and financing options.

Can the course be delivered online?

Yes, the program is delivered online with live classes. As such, you will be in direct contact and under the supervision of the teachers, which will enable you to follow the classes and interact in a flexible and natural way.

This program is for you if...

  • Networking de asistentes a evento de tecnología en immune
    Do you want to level up?

    Do you want to stay in your field or sector, but you want to continue learning and explore new challenges? It's time to give your professional profile a boost and align it with the latest trends in technology.

  • Estudiantes del centro tecnológico immune dando clase de tecnología
    Are you finishing your degree, and you want an upgrade in technology?

    We love your profile, because you dare to dream. And in the professional world, fortune favors the bold. If you are an entrepreneur or freelancer, this program will help take your professional projects to the next level. 

  • profesor de máster enseñando conceptos tecnológicos
    Want to change your professional career?

    If you want your career to take a new direction and enter the world of tech with a bang, the program will help you specialize and shape your professional profile. 

  • Alumnos de programa tecnológico en clase de ciberseguridad, data science y programación
    Are you an entrepreneur or freelancer?

    This program will put you in the spotlight, as technology is the engine of innovation and the key to staying competitive in a constantly evolving market.

Admissions Process

Our students are characterized by their passion for technology. Our admissions process focuses on who you are, how you think, what you have accomplished, and then sharing your goals.

Our aim is to get to know you better, see what makes you unique and ensure that the IMMUNE educational model adapts to your profile.

Personal Interview
Academic Committee
En UMECIT Afianza tu Conocimiento Profesional
Entry requirements

All applicants must present the following documentation, scanned from the original and sent to the promoter to be hosted on the respective platform:

  • Bachelor's degree or equivalent degree credits or grades.
  • For foreign qualifications, the above documents must be certified by apostille from the Ministry of Foreign Affairs of the respective country.
  • In the event that the country in question does not have a consulate, you must carry out the procedure with the corresponding official authorities.
  • Identity document (passport in the case of foreigners residing in Panama).
  • A current passport-sized photo
  • Complete the admission form in electronic format with the promoter.

Note: Students enrolled in virtual mode whose nationality is not Panamanian must present a copy of their personal identification document from the country of origin. If you are a foreign student, these documents must be provided with an apostille stamp or legalization through the corresponding diplomatic channels and translated into Spanish.

Requirements for attending the course

Students’ entitlement to remain at the University will depend on their motivation, ability, and vision to obtain their chosen degree.

Given the profile of the university's ‘CYBERHUMANIST’ educational model, students must:

  • Study both as a team and individually.
  • Develop research skills based on learning through discovery.
  • Take an in-depth interest in the knowledge related to your discipline.
  • Respect and comply with the regulations established by the statutes that regulate relations between the University and students.
  • Follow attendance requirements and attend at least 80% of the meetings in the partial on- campus format.
  • For virtual format studies, attendance is regulated through constant interaction in all the activities planned on the platform, including synchronous and asynchronous meetings, as well as the development of the student's independent work.
  • Any prolonged absence must be duly justified.
  • All students in specialization programs must have basic IT knowledge.

Contents: Maintain a minimum cumulative score of 2 on a scale of 1 to 3.

Note: Pass each subject with a minimum grade of 81 on a scale of 1 to 100, which is equivalent to a B.

Graduation Requirements

To obtain the qualification of Specialist, the following are required:

  • Contents: Maintain a minimum academic cumulative score of 2 on a scale on a scale of 1 to 3.
  • Hold a qualification for a second language recognized by UNESCO, in accordance with internal regulations.
  • Take and pass all subjects on the curriculum with a minimum grade of 81 points, equivalent to a B.
  • Complete an 80-hour internship and present a final report with a case study. This must be aligned with the last subject of the specialization: Investigation I: Planning, Monitoring and Evaluation of Projects.
  • To have no outstanding debt, loans or work with the institution (financial, academic and library).

To obtain the Master’s qualification, the following are required:

  • Maintain a minimum academic cumulative score of 2 on a scale on a scale of 1 to 3.
  • Second language recognized by UNESCO
  • Take and pass all subjects on the curriculum with a minimum grade of 81 points, equivalent to a B.
  • Hold a qualification for a second language recognized by UNESCO, in accordance with internal regulations.
  • Final work (Degree)

To obtain the Master’s qualification, the following are required:

  • Final work (Degree)
  • Prepare, defend and pass the degree work.
  • Align Action Research: Carry out the degree work
  • Take and pass all the academic content of the enrolled program.
  • To have no outstanding debt, loans or work with the institution (financial, academic and library).
Graduate Profile


  • Understand data, its importance, its use, its interpretation and how to extract value from it.
  • Discover the main tools in data science that allow a company or institution to benefit from the data it collects.
  • Learn how packages or libraries are used to extend the functionalities of a programming language.
  • Understand what big data is, the causes that have led to the emergence of big data technologies and learn about the evolution of big data in its historical context.
  • Understands the concept of data science, its relationship with other disciplines, its methodology and practical applications.
  • Master data processing techniques and understand their relevance, both in improving the quality of data and in increasing its usefulness for models, algorithms, consolidations, visualizations and other elements.
  • Understand the principles and benefits of business intelligence.
  • Identify a complete big data solution in a Hadoop ecosystem and put it into practice on a virtual machine.
  • Understand that the techniques that allow to extract knowledge of the population from the sample are the most evident for the approximation to the reality of the population the more representative the sample is of the population.
  • Understand machine learning and how it can help businesses; as well as understanding the main types of machine learning algorithms and when each type should be applied.
  • Know and understand artificial intelligence, its implications and the type of problems it can resolve; decision-making techniques (expert systems and supervised learning), as well as their applications.


  • Interpreting the information extracted from data and knowing what questions can be answered by the data, means really understanding what the data represents.
  • Use the main data science tools to be able to present data with accuracy and value.
  • Install and use packages or libraries to extend the functionalities of a programming language.
  • Apply the fundamental elements of programming, both for Python and R.
  • Develop the model of a data-driven company and how this is constructed through the leverage of its data platform; characterizing new profiles in the company that emerge as a consequence of the adoption of a strategy based on data exploitation.
  • Carry out a data science project based on a problem to be solved, including the different stages, the type of tools to use, the professional profiles involved, the type of obstacles that could arise and the ways to overcome them.
  • Make effective visualizations of information and convey the appropriate message.
  • Solve a data processing solution with Spark in an environment with notebooks.
  • Analyze and interpret data in order to promote value and applied innovation across various industries.
  • Use other tools and services of companies that are ready to support your own projects.
  • Process natural language, what types of problems it solves and its applications.


  • Analyze situations critically and propose viable solutions with objectivity and realism.
  • Develop a global and complete vision of the complex socio-labor relations that arise in the various historical and current methods of production.
  • Perfect the theoretical bases of the specialization and focus it in the best possible direction.


  • Feel the dynamics of data management and turn it into information, with support from a specialist workforce.
  • Understand the importance of human resources within the development of a company.
  • Contribute to the construction of the social fabric by managing the human talent of the company


  • Present proposals for the optimization of IT resources.
  • Generate data analysis and optimization models in a company.
  • Have the ability to generate new companies through feasibility assessment based on data and how to create value from them.

Paseo de la Castellana 89, 28046 Madrid

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