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 Selection and development of key BI metrics and indicators for decision-making

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

 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

Explores cloud computing applied to analytics projects, with a practical focus on Microsoft Azure. The course covers cloud-based data storage, processing, and analytics services, compares Azure with its main competitors, AWS and Google Cloud, and includes a guided hands-on exercise in configuring an Azure environment and integrating projects with cloud services.ation that the cloud brings to BI environments.

Fundamentals of cloud computing applied to data analytics IaaS, PaaS, and SaaS services within the Azure ecosystem applied to BI Practical comparison: Azure vs. AWS and Google Cloud in analytics environments (services, pricing, and use cases) Cloud storage: Data Lakes, Data Warehouses, and Blob Storage in Azure Cloud data processing: benefits and challenges

Costs, scalability, and operational flexibility Security, access, and data governance in cloud environments Guided hands-on exercise: setting up an Azure working environment and integrating your own project with its services Integration between cloud services 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 use cases for workflow automation on the Azure platform

Introduces the student to connecting Power BI and  Google Data 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 Google Data 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

Introduces the transition from generative AI that responds on demand to AI that takes action: agents capable of planning, executing, and verifying business analysis tasks under human supervision.

Fundamentals of agentic AI: task planning, execution, and verification Human supervision (human-in-the-loop) and levels of autonomy Single-agent architectures vs. multi-agent systems

Design and deployment of custom business-focused agents using Claude Governance and best practices for the responsible use of AI agents Practical case study: building an analytics agent using a real business dataset

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

Development of a real-world machine learning use case from start to finish, incorporating process automation and intelligent agents Use of Dataiku as a visual platform for the analytics lifecycle, and Make/n8n for workflow orchestration and repetitive model-related tasks Data cleaning and preparation by connecting multiple sources through automated workflows Training and validation of predictive algorithms without programming, integrated into automated pipelines

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

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.

Combines analytics project management with the agile methodologies currently used to deliver these projects. The course covers the complete lifecycle of a Business Intelligence and Artificial Intelligence project—from defining objectives to delivering results—and the application of agile frameworks such as Scrum and Kanban to organize work into sprints, prioritize the backlog, and effectively coordinate business, data, and technology teams. 

Analytics project lifecycle Definition of business objectives and deliverables; planning and risk management Implementation of analytics models: integration, validation, and monitoring Stakeholder identification, resource estimation, and cost control Budget creation and management for BI projects Application of agile frameworks: Scrum and Kanban in analytics environments

Definition of roles: Product Owner, Scrum Master, and data team Sprint planning for iterative value delivery and backlog prioritization Agile coordination of multidisciplinary teams across business, data, and technology Use of Trello for project tracking and communication Practical methodology applied to real-world analytics projects

Combines the fundamentals of Data Governance with ethics, compliance, and impact assessment in analytics projects. The course covers the role of the Data Office, data quality and traceability, master and reference data management, and the design of a governance framework with defined roles, policies, and processes. These areas are connected to regulatory compliance (GDPR), ethical responsibility in the use of data and AI, organizational change management, and the measurement of return on investment (ROI) from analytics initiatives. The course also includes hands-on implementation of data governance using DataHub. 

Fundamentals and key pillars of data governance within organizations Structure, functions, and activities of a Data Office Master and reference data management and its impact on data quality Development and maintenance of a corporate data glossary and data catalog Assessment of data quality and traceability throughout the data lifecycle Design of a governance framework with defined roles, processes, and auditing

Practical implementation of data governance using DataHub Assessment of regulatory compliance: GDPR, privacy, and transparency Ethical principles for the responsible use of data and artificial intelligence Organizational change management in data-driven initiatives Assessment of the impact of analytics on processes and decision-making, and measurement of return on investment (ROI)