Study Plan
Learn Data Analytics from scratch. Students will understand the logic of the Python programming language, use UNIX terminals and the most popular tools for data visualization. By the end of the program, they will have acquired the most widelyused skills and techniques in the world of data analytics.
Prework
Presentation of the curriculum, work tools, program operation and presentation of the group.
Data Engineering Fundamentals
Fundamentos De La Programación
This module focuses on introducing the fundamentals of programming using Python, a widely used and versatile language that is ideal for beginners and professionals alike. Python is used in web applications, data analysis, artificial intelligence, task automation and much more. Its simple and readable syntax makes it easy to learn and develop, providing a solid foundation for the rest of the bootcamp.
- Python Introduction and Basic Features
- Data Types, Variables and Text Manipulation
- Python Data Structures
- Random Data Generation
- Flow Control Structures
- Python Functions
- Date and Time Manipulation
- Lambda functions
- Regular expressions
- Working with JSON Data
Bases De Datos
This module provides a comprehensive introduction to the world of databases, from basic modeling principles to practical implementation using SQL. Through this course, students will learn to define, manipulate and manage data within structured database systems, using SQL as the primary tool. The objective is to equip students with the skills necessary to design efficient databases and perform complex queries to support business decisions.
- General Concepts and Database Modeling
- Bases de datos SQL y NoSQL
- Introduction to the SQL Standard: Data Definition Language (DDL) and Data Manipulation Language (DML)
- Advanced SQL Standard: Subqueries and Common Table Expressions (CTEs)
- SQL Scripting
- PostgreSQL
- MongoDB
Transformación Y Modelado De Datos
This module focuses on data transformation and data modeling, essential techniques for turning raw data into valuable information in business contexts. Through methodologies such as ETL, ELT, and EL, and using modeling standards such as dimensional modeling and Data Vault, students will learn how to create sophisticated data products. In addition, the course will address the use of modern tools for the orchestration of data flows and the creation of applications and APIs that enable the effective exploitation of transformed data.
- 1. Transformación del Dato
- Modeling Techniques
- ETL/ELT/EL
- Data Flow Orchestrators
- Data Exploitation
- APIs creation
- Data Applications
Exploratory Analysis
El análisis exploratorio de datos (EDA, por sus siglas en inglés) es una etapa crucial en cualquier proyecto de análisis o modelado. Nos ayuda a comprender la estructura, el contenido y las relaciones dentro de los datos, lo que facilita la preparación para el desarrollo de modelos de Machine Learning
- Introduction to Exploratory Data Analysis (EDA)
- Python Environment Configuration for EDA
- Descriptive Statistics with Python
- Creation of Interactive Graphics and Visualizations
- EDA applications in Machine Learning
Data Analyst
Principios Y Técnicas De Visualización
The module is designed to provide students with an in-depth understanding of how to transform complex data into clear, effective and actionable visualizations. This course addresses both the fundamental principles of data visualization and the advanced techniques needed to create charts and dashboards that support data-driven decision making.
- Introduction to Data Visualization
- Types of Graphics and Their Applications
- Designing Effective Visualizations
- Visualization Tools
- Interactive Visualization and Dashboards
- Advanced Visualization
- Case Studies and Applied Projects
Visualización De Datos Avanzada
Advanced data visualization is essential to convert complex information into clear insights that aid strategic decision making in a business environment. In this module, the focus is on using business intelligence tools such as Power BI and Tableau to generate interactive reports and dashboards that present valuable information to decision makers.
- Concept and Relevance of Storytelling in the Data World
- Key Elements of a Good Data Narrative
- Tools for Effective Dashboard Implementation: Power BI
- Python Integration for Preprocessing and Visualization
Data Science & AI Fundamentals
AI Fundamentals: Machine Learning
This module sets the starting point for the world of Machine Learning, introducing you to the key concepts and essential techniques of the field. Through hands-on and applied learning, you will discover how models can unravel hidden patterns in data. The goal is to prepare you to handle more sophisticated challenges and dive into more advanced techniques in later modules.
- Machine Learning (ML) Introduction
- ML Project Life Cycle
- ML Fundamental Concepts
- Supervised Learning: Regression
- Supervised Learning: Classification
- Supervised Learning: Decision Tree and Random Forest
- Unsupervised Learning: Clustering
- Dimensionality Reduction
Certificación
Módulo asíncrono en el que se habilitará el tiempo para preparar y realizar los exámenes de certificación incluidos en el programa. IMMUNE, en este caso, actúa de facilitador en la conexión entre la entidad certificadora y el estudiante, facilitando el proceso pero sin tener la autoridad sobre el examen ni las calificaciones obtenidas por los estudiantes.
Capstone Project
- Team building.
- Choice of topic for final project.
- Assigning tutors.
- Project development with assigned tutor.
- Project delivery.
Presentación De Capstone Project
Presentation of final project before a panel of experts.
Innovation Catalyst
