According to leading technology consulting firms, the demand for specialists in Business Intelligence and Big Data will grow exponentially in the coming years. This master’s program enables you to master data analysis techniques, advanced statistics, machine learning, and visualization, transforming large volumes of information into strategic decisions. Prepare to lead Business Analytics projects and turn data into a competitive advantage for any organization.
Objectives
- Distinguish between the concepts of Big Data, Business Intelligence, and Analytics, avoiding confusion in a world where everything is labeled “Big Data.” - Gain a comprehensive overview of the Big Data & Analytics ecosystem, including technologies, tools, and professional roles. - Identify data-driven business strategies and opportunities while effectively managing projects and teams. - Understand the legal and ethical aspects of data management, as well as the associated technical and scientific complexities. - Develop communication and leadership skills in Big Data environments, while becoming familiar with the most relevant tools on the market.
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world cases, digital tools, and support from a team of specialized instructors.
Program
Data Information
Knowledge and Wisdom
Data Management (I)
Data Management (II)
Corporate Performance Management
Databases
Business Intelligence
Data Warehousing
Big data
Hadoop Spark
Hadoop Ecosystem (I)
Hadoop Ecosystem (II)
Hadoop Ecosystem (III)
Spark Ecosystem
Installation and Configuration of Big Data Architectures
Analytics
Major Algorithms (I)
Major Algorithms (II)
Machine Learning and Deep Learning
Internet of Things
Introduction to Power BI
Different Types of Power BI: Is It Really Free?
Let's dive right in: First Simple Report
Power Query: Data Source
Data Transformation
Data Modeling
Getting Started with DAX (I)
Getting Started with DAX (II)
Mastering the DAX (I)
Mastering the DAX (II)
Table and Matrix
Trends
How to Filter Your Data Properly
Bookmarks
Obtaining Details
Understanding the Power BI Service
Sharing Content in Power BI Service
Comparing Power BI Service and Power Report Service
Integrating Python and R into Power BI Desktop
Introducing Bravo for Power BI Desktop
Introduction to SQL
Database Management
Data Types
Standardization
Creating Tables in SQL
Working with Tables
Querying Tables in SQL
Joining Tables in SQL
Combinations of Tables and Views
Other SQL Commands
Functions for Strings and Numeric Functions (I)
Numerical Functions (II)
Date and Time Functions
Other features
Loops, Conditionals, and Triggers in SQL
Introduction to Data Warehousing
Databases in a Data Warehouse. Stage
Databases in a Data Warehouse: ODS (I)
Data in a Data Warehouse. ODS (II)
Databases in a Data Warehouse. DDS
Introduction to Python
Features and Applications
Installing Python
Setting Up a Development Environment
Basic Python Syntax
Variables and Data Types
Operators and Expressions
Using Comments
Introduction to Flow Control
Conditional Structures (if, elif, else)
Loops (for and while)
Loop Control (break and continue)
Data Analysis with NumPy
Pandas
Matplotlib
How to Use `loc` in Pandas
How to Delete a Column in Pandas
Pivot Tables in Pandas
The group of pandas
Python Pandas: Merging DataFrames
Matplotlib
Seaborn
Introduction to R
What do you need?
Data Types
Descriptive and Predictive Statistics with R
Integrating R with Hadoop
Data Acquisition and Cleaning (ETL)
Statistical Inference
Regression Models
Hypothesis Testing
Business Analytics
Graph Theory and Social Network Analysis
Presentation of Results
Concepts of NoSQL Databases
Advantages and Disadvantages of NoSQL Databases
Key Features of NoSQL Databases
Document Databases
Column Databases
Key-Value Databases
Graph Database
Introduction to MongoDB
MongoDB Features and Architecture
Data Modeling in MongoDB
Queries and Operations in MongoDB
Scalability and Performance in MongoDB
Apache Cassandra
CouchDB
Redis
Amazon DynamoDB
NeoJS
Data Structure Design
Setting Up the Development Environment
Installing and Configuring MongoDB
Creating and Manipulating Collections in MongoDB
Importing and Exporting Data in MongoDB
Indexes and Query Optimization in MongoDB
Data Aggregation in MongoDB
Transactions in MongoDB
Replication and High Availability in MongoDB
Backups and Recovery in MongoDB
Web and Mobile Apps
Big Data and Data Analysis
Internet of Things (IoT)
Recommendation Systems
Social media and social networks
Introduction to Data Integration
Integration with programming languages (Python, Java, etc.)
Integration with Business Intelligence (BI) Tools
Integration with cloud storage systems
Security Concepts in NoSQL Databases
Authentication and Authorization in MongoDB
Data Encryption in NoSQL Databases
Auditing and Access Control in NoSQL Databases
Introduction
Data Literacy
Working with Data
Data Processing Solutions and Techniques
Data Quality Management
Working with Data in Excel
Dataset (DATASET)
Data Cleaning with Excel
Data Wrangling with Excel
Data Blending in Excel
Installing Talend Data Preparation Desktop
Working with Data in Talend
Data Cleansing with Talend
Data Wrangling with Talend
Data Blending with Talend
Sign Up for Dataprep by Trifacta
Working with Data Using Dataprep by Trifacta
Data Cleansing with Trifacta
Data Wrangling with Dataprep by Trifacta
Data Blending with Dataprep by Trifacta
Introduction
Linear, Multiple, and Logistic Regression (I)
Linear, Multiple, and Logistic Regression (II)
Support Vector Machine (SVM)
Decision Trees
KNN (K-Nearest Neighbors)
Naive Bayes
Evaluation of Supervised Models
Sample Exercise
Suggested Exercise
Introduction to Clustering: Purpose and Metrics
K-means clustering
Hierarchical clustering, other techniques, and examples
Principal Component Analysis (PCA)
PCA Example Exercise
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Sample Exercise
Suggested Exercise
Introduction
Classification of Machine Learning Algorithms
Examples of Machine Learning
Differences Between Machine Learning and Deep Learning
Types of Machine Learning Algorithms
The Future of Machine Learning
Introduction
Algorithms
Introduction
Collaborative filtering
Clustering
Hybrid Recommendation Systems
Classifiers
Algorithms
Components
Learning
Introduction
The Process of Transitioning from DSS to IDSS
Use Cases
Deep Learning
Deep Learning Environment with Python
Machine Learning and Deep Learning
Neural Networks
Deep networks and shallow networks
Single-layer and multilayer perceptrons
Example of a perceptron
Types of Deep Networks
Data Input and Output
Training a Neural Network
Computer Graphics
Implementation of a Deep Network
The Direct Propagation Algorithm
Multilayer deep neural networks
The State of the Art in Artificial Intelligence
Philosophy of Artificial Intelligence
The Future of Artificial Intelligence
Project Development Processes Using Artificial Intelligence
Data: Your Greatest Asset
Machine Learning
Deep Learning
Transformers
Synthetic Data Generation
Hyperparameters in Artificial Intelligence Models
Linear Regression
Nonlinear Regression and Support Vector Machines (SVM)
Decision Trees and Random Forests
Fuzzy Logic and Gradient Descent
Recommendation Systems
Setting Up the Development Environment: Anaconda, Visual Studio Code, and Python
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows you to track progress through dashboards and reports.
It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
It is based on best practices in security, access control, learning traceability, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate identity and offers a total of more than 900 courses, it provides tailored training for each organization. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
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Digital Transformation and Smart Infrastructure 
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60 ECTS
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