Master in Data Analytics and Business Intelligence

Introduction to Business Intelligence as a key tool for business decision-making. The main components of the analytical ecosystem, the different data architectures, and information flows are presented. The difference between Business Intelligence and Big Data in the business context is analyzed, addressing how each approach adds value in different scenarios. In addition, real-world BI use cases are presented in sectors such as marketing, operations, finance, and customer service. Finally, the main professional profiles involved in analytical projects (CDO, analysts, data engineers, data scientists) are described, along with their roles and their organization in data-driven multidisciplinary teams.

Core principles of Business Intelligence and its current evolution Data architectures: layers, information flow, and storage Analytical ecosystem: tools, processes, and data culture Comparison between Business Intelligence and Big Data in the company

Identifying the main professional profiles in analytics Key roles: CDO, Data Analyst, Data Engineer, Data Scientist Using BI to support strategic decision-making Organizing multidisciplinary teams in BI projects Real-world Business Intelligence use cases across different sectors

Explores how organizations define and implement a data strategy to create sustainable competitive advantages. It covers the fundamental pillars of data governance, information quality, and a data-oriented organizational culture. It analyzes how to align data strategy with business objectives, ensuring ethical, secure, and efficient use of information. The course includes practical cases of business transformation driven by the strategic use of data in sectors such as retail, banking, and healthcare.

Data strategy as a driver of sustainable business transformation Key principles of data governance and how it works in practice Tools and methodologies to ensure data quality in the business Data-driven culture: challenges, benefits, and stages of organizational maturity

Aligning data strategy with corporate objectives Ethics, privacy, and compliance as an integral part of strategic design Developing roadmaps for data initiatives in the company Practical cases of data strategy implementation across different sectors

Introduces the essential principles of databases, both relational and non-relational. It analyzes key concepts of storage, data organization, schemas, and their role in Business Intelligence systems as support for analysis and business decision-making.

Database fundamentals in business analytical environments Relational models: structures, tables, relationships, and keys NoSQL databases: overview and key differences Comparison between structured and unstructured models

The role of databases in BI platforms Information organization and normalization schemas Use cases by database type (OLTP vs. OLAP) Leading market tools and current ecosystems

Introduces SQL as a fundamental tool for querying and preparing data in analytical environments. Students will learn to build queries relevant to reporting and analysis, combining tables and creating filters that support business decisions.

Basic SQL syntax and the structure of a query SELECT queries, WHERE filters, and aggregation functions Joins between tables to create unified business views Subqueries, aliases, sorting, and grouping

Using SQL to generate key performance indicators (KPIs) Applying SQL to real databases and analytical datasets Simulating queries for dashboards and reports Best practices in data cleaning and filtering with SQL

Introduces NoSQL database models: key-value, document, column, and graph. It explains their main characteristics, advantages over relational databases, and typical use cases in environments handling large volumes of unstructured or real-time data. 

Definition and types of NoSQL databases Key models: key-value, document, column, graph Use cases where NoSQL outperforms relational databases Flexibility and scalability in massive analytical environments

Comparison with traditional models: advantages and limitations Practical examples with MongoDB, Cassandra, Neo4j Applying NoSQL to unstructured analysis The role of NoSQL in hybrid data architectures

 This module builds the skills to construct reliable, scalable data ecosystems. It addresses the analytical data life cycle in full: from ingesting and cleaning heterogeneous sources to the advanced design of dimensional architectures. The approach combines technical execution in Power BI with the governance and ethics frameworks needed to guarantee the integrity, regulatory compliance, and accuracy of KPIs in decision-making. 

Integration of structured and unstructured data from multiple sources Techniques for cleaning, normalizing, and handling incomplete data Logical and physical modeling: star and snowflake architectures

Designing relationships, hierarchies, and measures for analysis in Power BI Semantic enrichment and dataset preparation for the business Data governance, ethics, and regulatory compliance Best practices in documenting and validating analytical models

This topic offers a comprehensive look at the fundamentals, structures, and practices of Data Governance, combining theory, real cases, and the applied use of tools. It explores the role of the Data Office, quality and traceability models, master and reference data management, and data architecture in analytical and Business Intelligence environments. Students will learn to design a governance framework with roles, policies, and processes, understanding its relationship with change management, organizational maturity, and measurement through indicators. The training culminates with the practical implementation of data governance in DataHub, connecting the concepts learned with a real environment for data cataloging and control.

Fundamentals and pillars of data governance in the organization Structure, functions, and activities of a Data Office Master and reference data management and its impact on quality Building and maintaining the corporate data glossary and catalog

Assessing data quality and traceability throughout its life cycle Informational data architecture, BI, and the principles of the Data Mesh model Designing a governance framework with roles, processes, and auditing Practical implementation of data governance in the DataHub tool

Explores the main cloud computing platforms (AWS, Azure, Google Cloud) applied to analytical projects. It reviews cloud storage, processing, and data analysis services, as well as the advantages of scalability, flexibility, and cost optimization that the cloud brings to BI environments.

Fundamentals of cloud computing applied to data analysis IaaS, PaaS, and SaaS services focused on BI Leading platforms: Azure, AWS, Google Cloud in analytical environments Cloud storage: Data Lakes, DWH, Blob Storage

Data processing in the cloud: advantages and challenges Costs, scalability, and operational flexibility Security, access, and data governance in cloud environments Integration between the cloud and BI tools (Power BI, ETL, etc.)

Introduces the extraction, transformation, and loading (ETL/ELT) processes essential to preparing data for analysis. It covers the design of automated data pipelines, flow orchestration, integration best practices, and process optimization in business environments.

Fundamentals of ETL and ELT processes applied to data analysis Designing automated pipelines in real business environments Efficient orchestration and scheduling of complex data flows Best practices for integration and transformation in BI projects

Optimizing time and quality in data loading processes Operational view of the complete data preparation cycle Applying professional standards in scalable analytical environments Practical cases of flow automation on the Azure platform

Introduces the student to connecting Power BI and Looker Studio to different data sources (files, SQL databases, CRM, cloud). Students work on initial data transformation, basic cleaning, combining tables, and creating simple data models, preparing information for visual analysis. 

Connecting Power BI and Looker Studio to multiple data sources Importing files, SQL databases, spreadsheets, and CRMs Data integration from cloud and on-premise environments Basic transformation and initial cleaning of imported data

Combining tables and creating functional relationships Preparing simple data models for visual analysis Visual interface for modeling with no programming required Key fundamentals for building dynamic, connected reports Automating data extraction and delivery to Power BI with Power Automate

Practical application of data integration and preparation using professional tools such as Azure Data Factory and Azure Synapse Pipelines to build data flows in the cloud. It is complemented by Alteryx as a low-code platform for data transformation and cleaning, and by Octoparse, a no-code web scraping tool that makes it possible to bring in external data from the web without programming. Students will develop complete pipelines to obtain, process, and prepare datasets ready for analysis and visualization, integrating the results directly with Power BI to create interactive reports and real-time business dashboards. 

Building complete pipelines with cloud, low-code, and no-code tools Cloud data processing with Azure Data Factory and Synapse Efficient cleaning and transformation with Alteryx in enterprise environments Code-free web scraping with Octoparse to bring in external data

Automating the data flow from source to dashboard Preparing datasets connected directly to Power BI Hands-on project integrating data collection, processing, and analysis

This topic introduces the student to the fundamentals of digital analytics applied to business, offering a practical view of how the data generated in web environments is captured, validated, interpreted, and used. Throughout the module, students will learn to define measurement objectives, identify relevant metrics and events, understand the digital data flow, and turn that information into actionable insights for decision-making. The approach is eminently practical and business-oriented, combining real use cases, digital measurement tools, and analysis criteria applicable to professional contexts. 

Fundamentals of digital analytics applied to business Defining measurement objectives, KPIs, and key metrics Events, parameters, conversions, and the structure of digital data Capturing, validating, and interpreting data in web environments Introduction to the use of tagging and digital analytics tools Applying Google Tag Manager and Google Analytics 4 in measurement scenarios Basic technical validation of events and data tracking with debugging tools

Identifying measurement errors and data quality best practices Interpreting digital data to optimize acquisition, behavior, and conversion Practical application of digital analytics to real business cases Reading insights and detecting improvement opportunities from data The connection between digital measurement, decision-making, and business strategy

Optional extension session introducing Python applied to data analysis: basic syntax and data manipulation with libraries such as Pandas and NumPy, working in a professional development environment (VSCode) with GitHub Copilot as a coding assistant.

The main supervised machine learning algorithms applied to the business environment are introduced, such as linear regression, logistic regression, decision trees, random forest, and support vector machines (SVM). Students will learn to build predictive models, validate their performance, and apply key metrics such as precision, recall, F1-score, or AUC to assess their effectiveness in real business problems. All of this is done using visual, no-code tools that make it possible to apply advanced predictive analytics techniques without programming knowledge, facilitating learning and practical application for business profiles.

Practical introduction to supervised machine learning in business environments Applying key algorithms: regression, trees, SVM, random forest Building predictive models with no programming required Validating models with metrics such as precision, recall, and F1-score

Applying supervised data mining techniques to uncover useful predictive patterns in real business contexts Interpreting results to support strategic decisions Real classification and prediction cases across different sectors Developing analytical skills for profiles without a technical background

Unsupervised learning techniques aimed at detecting hidden patterns and performing advanced segmentation in business data are presented. Students work with clustering algorithms such as K-means and hierarchical clustering, dimensionality reduction techniques such as PCA (Principal Component Analysis), and association rule algorithms such as Apriori. These techniques are applied to practical cases such as customer segmentation, anomaly detection, and behavior analysis, making it possible to uncover hidden insights in large volumes of data and support strategic decision-making based on emerging patterns.

Applying unsupervised machine learning techniques to business data Clustering algorithms: K-means and hierarchical clustering explained from a business perspective Dimensionality reduction with PCA for simplified analysis Discovering hidden patterns and structures in large datasets

Behavior analysis and anomaly detection in processes Advanced customer segmentation based on real data Association rules with the Apriori algorithm for consumption patterns Practical cases oriented to strategic decisions based on clustering

This topic introduces the student to the practical use of generative artificial intelligence applied to the business environment, using no-code tools such as KNIME and Julius AI. Through visual, intuitive workflows, students will explore how to automate tasks such as content generation, summaries, document analysis, or virtual assistance, without programming.

Introduction to generative AI and its business application Designing code-free workflows with KNIME Task automation with Julius AI Real cases: marketing, customer service, and documentation

Practical comparison between KNIME and Julius AI Integrating generative AI into analytical processes Ethical and responsible use of generative AI No-code tools applied to business profiles

Hands-on development of a complete machine learning project using the Dataiku platform. Students work from data preparation and cleaning, model training, validation, and tuning, through to presenting results applied to a real business case. 

Developing a real machine learning case from start to finish with process automation and intelligent agents Using Dataiku as a visual platform for the analytical cycle and Make/n8n to orchestrate workflows and repetitive model tasks Data cleaning and preparation, connecting multiple sources through automated workflows Training and validating predictive algorithms without programming, integrated into automatic pipelines

Designing agents in n8n capable of triggering models, processing results, and making decisions based on business rules Automating model deployment and report generation through scenarios in Make Applying machine learning in a real environment with direct integration into business processes

Practical masterclass on building agents with Claude Code: designing and configuring an orchestrator agent capable of supporting data collection, analysis, and report production, applied to the master's real workflows.

Introduces visual design principles applied to data communication. The course covers visual storytelling techniques, chart selection, KPI structuring, and how to build clear, understandable dashboards geared toward decision-making for both technical and business audiences.

Visual design principles applied to business dashboards Data storytelling techniques for effective communication Choosing the right chart based on data type and message Structuring and visually prioritizing KPIs that matter to the business

Creating reports that technical and non-technical audiences can understand Designing clear, visual, decision-oriented dashboards Best practices in color, typography, and element layout Real-world cases of strategic dashboards in corporate environments

Hands-on training in using Power BI to create interactive dashboards. Students learn to import data, build models, create visualizations, add interactivity (drill-down, filters), and publish and share reports professionally in collaborative environments. The course also covers automating data flows with Power Automate to ensure refresh reliability and efficiency in real-world settings.

Building interactive dashboards with Power BI step by step Importing data from multiple connected sources Data modeling and creating relationships between tables Designing effective, business-oriented visualizations Adding filters, slicers, and drill-down navigation

Professional report publishing in collaborative environments Automating refreshes and controlling dashboard access Best practices for presenting and distributing interactive reports Automating data flows with Power Automate

Introduction to using Google Data Studio and Tableau as cloud reporting tools. The course covers connecting to different cloud data sources, creating interactive and shareable reports, and designing lightweight dashboards geared toward real-time collaboration.

Introduction to Google Data Studio and Tableau as cloud reporting tools Connecting to multiple cloud data sources Creating interactive, visually accessible reports Designing lightweight, clear, business-focused dashboards

Setting up filters, slicers, and key visualizations Sharing reports with real-time collaborative access Visual customization based on objectives and audience type

A practical course focused on analyzing how companies apply Business Intelligence to make data-driven decisions. Working from real dashboards, published reports, and industry examples, students will explore cases in areas such as marketing, sales, customer analytics, and quality control. The course encourages critical analysis, in-class debate, and proposals for improving visualizations and key performance indicators (KPIs). 

Analysis of real business dashboards applied to strategic decisions BI case studies in marketing, sales, advertising, and CRM Identifying key KPIs and assessing their operational relevance Interpreting insights with a focus on business impact

Proposing improvements to visualizations and report structure In-class debate on evidence-based versus intuition-based decisions Developing critical thinking applied to business analytics Understanding the role of BI in competitive, dynamic environments

A practical session on planning and building a professional portfolio of BI projects: what to include for each dashboard (context, data, KPIs, and insights), how to host it depending on the tool used (Tableau Public, screenshots, or video for Power BI/Looker Studio), and how to present it on LinkedIn or a personal website during hiring processes.

Introduces the life cycle of analytics projects, from defining objectives to delivering results. The course covers planning, risk management, defining deliverables, and coordinating multidisciplinary teams in Business Intelligence and Artificial Intelligence projects.

The life cycle of analytics projects Implementing analytical models: integration, validation, and monitoring Project management, deliverables, issues, and changes Defining business objectives and deliverables Project planning and risk management

Identifying stakeholders and resources in data projects Practical methodology applied to real analytics project cases Resource estimation and cost control in analytics environments Creating and managing budgets in BI projects Coordinating multidisciplinary teams (business, data, technology)

Shows how to apply agile methodologies (Scrum, Kanban) in data projects. The course reviews role definition, sprint organization, task prioritization, backlog management, and effective coordination among business, technology, and analytics.

Fundamentals of agile management in data and AI projects Applying agile frameworks: Scrum and Kanban in analytics environments Defining roles: Product Owner, Scrum Master, data team Resource planning and efficient allocation in analytics teams Organizing sprints for iterative value delivery

Techniques for prioritizing tasks in the project backlog Agile coordination across business, data, and technology teams Using Trello for tracking and communication Best practices for adopting an agile culture in data-driven organizations

Covers governance, ethics, and compliance in analytics projects. It examines organizational change management, regulatory compliance, privacy, ethical responsibility in the use of data, and measuring the impact and return of data projects.

Fundamentals of data governance in BI and AI projects Assessing regulatory compliance: GDPR, privacy, and transparency Ethical principles in the responsible use of data and artificial intelligence Organizational change management in data-driven initiatives

Aligning projects with business strategy and culture Assessing the impact of analytics on processes and decisions Measuring the return (ROI) of analytics initiatives