Master in Business Analytics and Data Science

START MODULE. Pre-program. Description. Pre-Program. This self-paced material is selected to introduce the basic concepts and tools you will need during the program. Most of what you will learn here will be repeated during some of the courses, but repetition is part of the learning process, mostly for students who do not have a technical background. The Learning objectives include: Learn the foundamentals of Computational Thinking Practice the basics aspects of Python Begin using Git Hub Know the main aspects of Artificial Intelligence for Education Introduce the usage of Excel for business and analytics Understand how to use Linux Obtain a basic level on Quantitative Methods Briefly introduce yourself in how SQL works. COMPUTATIONAL THINKING. This multimedia material aims to level the knowledge of any new programming students, providing a fundamental understanding of coding before entering the course. This 2-hour preprogram features a combination of explanatory videos from the professor, interactive activities and diagrams, readings and quizzes.. QUANTITATIVE METHODS. The Quantitative Methods Preprogram, has been designed to allow you to prepare yourself and achieve the basic knowledge necessary before beginning the Program.. EXCEL. This course is a practical approach to Excel as tool to solve business problems. This course will help you understand the basic ways to work with Excel for Business. A final test will be included to check your understanding of this basic introduction and their level of Excel.. CODING IN PYTHON. Python is a clear and powerful  programming language, comparable to Perl, Ruby, Scheme, or Java. The reasons it has become so popular in the data scientist community is that it is an easy-to-use language that makes it simple to get your program working, it´s free and specific packages exists to make Python usable for data analysis. For example NumPy /SciPy, Pandas, Matplotlib, Scikit-learn, etc.. LINUX. Linux is the most important operating system for us, as all the Big Data technologies runs under Linux. It is very similar to others operating systems such as Windows and MacOS. Having knowledge of the most basic commands of Linux will be really helpful when students deal with some of the courses, for example when interacting with Hadoop through command.. SQL. SQL is the language of the data, widely used in every company and one of the most used tools to do Analytic. During the master you will learn, starting from scratch, the SQL you need to know but the following online course will give you a very good introduction to this subject, featuring insights from leading companies in the industry.. GITHUB. Git Hub is an online hosting service that became the standar in many areas regarding software development, content repository and version controlling. Many open source software projects are hosted and developed there. It will be used regularly during the program.. ARTIFICIAL INTELLIGENCE 101. AI 101 is a self-paced material about the importance and steps necessary to build a successful relationship with AI tools.. CAREERS DEVELOPMENT. IE’s Career Accelerator Preprogram is a part of the Career Success curriculum taught during the academic program. Career Accelerator content covers: All stages of your career journey, from discovering where to start to focusing your ideas, through actioning your career plans. Resources to help you identify what you want from your future, learn about different career paths, make decisions, and support you to make your next steps a success. Key information on a wide variety of sectors, industries, and career paths today. Making successful applications, preparing for interviews, practicing online assessments, and learning how to build your network.. Foundations Week. Foundations Week. This week, preceding the Opening is mandatory for all students in the face-to-face intakes and it marks a crucial stage in building the foundational knowledge and skills necessary for the program ahead.. MATHEMATICS FUNDAMENTALS. This course is designed to provide students with a strong foundation in the core concepts of mathematics, including set theory, probability and statistics, calculus, and linear algebra, enabling them to tackle various research questions through the examination of large datasets It will cover topics such as descriptive and inferential statistics to demonstrate how sample data can be employed to approximate, make choices, forecasts, or draw broader conclusions about larger populations. We will also see how common mathematical concepts such as derivatives or matrices are applied in today´s AI & ML models.. Programming Thinking. Programming Thinking empowers students with the critical thinking skills, tools, and frameworks needed to thrive as programmers. This course is less about coding and more about understanding the fundamental concepts and how they are applied towards solving problems in a systematic and logical manner. Key concepts demonstrated in this course are programming language-agnostic and can be found in most programming languages. To deepen their understanding and prepare them for future studies, students explore these concepts through interactive Python coding exercises using Google Colab. Students will also gain a high-level understanding of Generative AI and how to use it effectively as a programmer.

CORE PERIOD. TERM 1. STATISTICS FOR DATA SCIENCE. This course provides you with a working methodology and strong knowledge base for using statistical and mathematical tools in data analysis.. BIG DATA & ARTIFICIAL INTELLIGENCE IN BUSINESS STRATEGY. In the digital age, with the exponential growth of data and the advancement of Artificial Intelligence (AI) technologies, businesses are at a crossroads. Those that can effectively harness the power of AI and Big Data are poised to lead their industries, while those that don't risk obsolescence. This comprehensive course seeks to equip participants with the knowledge, tools, and strategies necessary to guide their organizations through this transformation. This course introduces you to the rest of the program and gives you a “big picture” perspective on Big Data & Analytics.  . MODERN DATA ARCHITECTURES FOR BIG DATA I. This course provides an overview of Big Data and the core technologies and architectures that support data-driven companies. Students will learn how Big Data solutions have enabled businesses to harness massive amounts of data to gain business insights. By the end of the course, students will have a broad understanding of the Big Data landscape, familiarity with leading frameworks and tools, and hands-on experience applying their knowledge. This course establishes a foundation for more advanced study of data technologies, architectures, and analytics. Students will be equipped to determine how Big Data can impact business needs in various domains.. SQL BASED DATA ARCHITECTURES I. This course provides a comprehensive introduction to SQL as a powerful tool for data exploration and decision-making. SQL is  "The language of the Data" used to interact with relational databases. Participants will gain a solid understanding of database concepts, develop proficiency in writing SQL queries to retrieve, manipulate, and update data. The course starts with essential topics from  the beginning, without requiring prior knowledge of the SQL language, and adding simple layers to the learning, will get deeper so students are capable to solve complex business queries. Learning is mainly obtained through practical examples and hands-on exercises that will enable students to learn how to solve business questions with SQL.. SQL BASED DATA ARCHITECTURES II. During SQL I students see how to load, manipulate and access data stored in relational databases using SQL language. In SQL II, we'll discover what are data models, how do they work, why are they so relevant, how many different types of data models do we have, etc. We'll be using a more advance SQL to organize and access the data stored in operational and informational systems. Furthermore, concepts like operational systems, data warehouse, data marts, online data stores, data lakes, olap servers, etc., will be included in this exciting journey through data systems.. PYTHON FOR DATA ANALYTICS I. This course provides a comprehensive introduction to Python, a versatile and widely used programming language, with a focus on practical applications in both data analytics and machine learning. Students will master the fundamentals of Python syntax and learn to solve algorithmic problems efficiently. The course emphasizes hands-on experience through exercises in Jupyter Notebooks, where participants will develop strong skills in exploring, analyzing, and manipulating tabular data using Pandas. By the end of the course, students will be well-prepared to tackle real-world challenges in data analytics and machine learning.. BIG DATA & ARTIFICIAL INTELLIGENCE IN OPERATIONS MANAGEMENT. The course's main objective is to provide students with a working methodology and a solid knowledge base for the use of forecasting models and econometric techniques in the areas of business and economics. Students will learn how to identify and how to use properly one of the most well known family of forecasting linear models and the most useful nonlinear model. Students will learn how looking at past data can turn rows of dates and numbers into clear predictions for things like demand, traffic, or production.And other tools can spot and forecast sudden changes or quiet periods, helping you understand risks ahead of time. Together, these methods help you get ready for what’s coming—whether you’re running a hospital, warehouse, power grid, or market.. TERM 2. MODERN DATA ARCHITECTURES FOR BIG DATA II. This course is the continuation of Modern Data Architectures for Big Data I, where we'll dive deeper into more advanced processing techniques with Spark such as real-time processing, graph processing and Machine Learning workloads at scale.  Students will establish a foundation for more advanced study of data technologies, Big Data Architectures and analytics. By the end of the course, you will be equipped to determine how Big Data can impact business needs in various domains and make informed decisions about implementing data-driven solutions.. MACHINE LEARNING I. Machine Learning (ML) is a a cutting-edge branch of Artificial Intelligence that's transforming industries and revolutionizing the way decisions are made, by learning from data without being explicitly programmed. In this course, you'll gain hands-on experience preparing data, applying essential ML techniques, and building predictive models that drive real-world impact. Whether you're entering finance, healthcare, tech, marketing or beyond, ML is a must-have skill for the data-driven future.. MACHINE LEARNING II. As a continuation of the introductory one, this hands-on course explores more advanced techniques and algorithms of both supervised and unsupervised learning. Through hands-on coding, increasingly complex datasets and advanced model implementation the participant will gain the skills needed to tackle high-impact ML challenges across real-world domains. Ideal for learners ready to go beyond the basics, this course helps you deepen your understanding of ML workflows and sharpen your ability to build models that deliver impact.. MLOps: MACHINE LEARNING OPERATION. In this course, students will explore the complete lifecycle of putting machine learning models into production. While training a model is often seen as the final step, MLOps reveals it is just the beginning. Students will learn about key stages such as model development, CI/CD, monitoring, validation, and governance. Through theoretical lectures and practical demonstrations, they will gain the tools to navigate real-world challenges and apply best practices for managing models in production.. PYTHON FOR DATA ANALYSIS II. Building on Python for Data Analytics 1, this course takes you further into the Python ecosystem for analytics and machine learning projects. You'll master advanced data wrangling with regex, time series analysis, and feature engineering, while learning to build robust scikit-learn pipelines. The curriculum covers essential ML project concepts like scaling, cross-validation, and hyperparameter optimization—equipping you with the practical tools and workflows needed to confidently tackle real-world machine learning challenges in Python.. DATA VISUALIZATION. This course is designed to provide students with a comprehensive understanding of data visualization within the context of business analytics and data science. It covers the theoretical foundations, design principles, chart taxonomy, tools, and applications of data visualization, with an emphasis on its significance, purpose, and impact on various domains. It will provide students with the required knowledge and skills to extract actionable insights from raw data through the use of proper visualization and storytelling, with the ultimate goal of efficiently communicating conclusions and influencing key stakeholders. Students will become proficient in some of the most in-demand business intelligence and data visualization tools on the market.

ELECTIVE PERIOD. TERM 3. TERM 3 DESCRIPTION. The elective period also offers you the opportunity to sharpen your career focus, allowing you to use electives to customize and complement your program’s core courses and pave the way to your dream job. Choose electives in line with your concentration area of interest, or dive deeper into topics that best fit your career objectives. You can choose your concentration either by industry, where you will be able to apply everything you’ve learned in the program to a specific sector, or by professional pathway. While it is not mandatory to select a concentration, doing so will give you a better understanding of market focus and your specific areas of interest within the industry.Only one concentration can be obtained and offering will be subject to student demand: Healthcare & Biotech. Fintech & Banking. Retail, Luxury & E-Commerce. Sports, Media & Entertainment. Smart Manufacturing & Automation Advanced AI. Tech Consulting & Data Strategy. Tech Entrepreneurship.. CONCENTRATIONS. HEALTHCARE & BIOTECH. ARTIFICIAL INTELLIGENCE IN HEALTH. This subject focuses on how AI and big data are transforming the healthcare sector, revolutionizing the patient journey from diagnosis to treatment and disease management. Throughout the course, students will understand how emerging technologies such as machine learning and big data analytics are optimizing clinical processes, improving patient outcomes, and reducing costs in healthcare systems, and how such disruptive technologies are transforming the whole sector, from industry to patients and citizens themselves. The course will also address ethical concerns and barriers to the implementation of these technologies, and what it takes to realise the promise of Digital Transformation in Healthcare.. Clinical Research and Development of Medical Device Technology. This course bridges clinical research with data-driven decision-making in the medical device industry. Students will explore how AI, business analytics, and data science are applied across the clinical investigation lifecycle, from trial design and risk assessment to regulatory compliance and safety reporting. Emphasis is placed on interpreting clinical data, optimizing trial efficiency, and supporting innovation through predictive insights. With a strong foundation in ethical and legal frameworks, students will also develop key skills in documentation, stakeholder communication, and MedTech strategy, preparing them for high-impact roles at the intersection of health, data, and technology.. COMPUTER VISION. Unlock the fascinating world of Computer Vision and bring your Python programming skills to life in this hands-on course. Computer Vision is a field that enables machines to interpret and understand visual information from the world around us. From self-driving cars to facial recognition, Computer Vision has revolutionized various industries. In this course, you will dive deep into the core concepts, algorithms, and practical applications of Computer Vision using the powerful Python programming language.. DATA GOVERNANCE. The objective of the course is to provide students with necessary skills that will allow them to efficiently manage the life cycle of an organization's data.All organizations regardless of size, business nature or purpose, grew data’s importance, elevating its status to a strategic that every organization must manage accordingly. Managing data is a complex activity including many aspects, both in business and IT areas. On the other hand, being successful is only feasible when data is properly managed, and Data Governance is the heart of Data Management.. IoT & EMERGING TECHNOLOGIES. This course explores how Industry 4.0 isn't about classical systems or fancy automatizations, but rather about the digital transformation of the industry into a demand-oriented organization. You will learn how to identify industries with high transformation potential and how to envision and project a digital transformation program for industrial companies by leveraging emerging technologies such as IoT, AI, Cloud Computing, 3D Printing, or Robotics. The course combines three types of sessions: Sessions focused on basic concepts to understand what sets Industry 4.0 apart from other sectors. Sessions centered around use-case development and technology-driven transformation examples. Practical sessions with real hands-on lab demos on IoT and Streaming technologies applied to the industrial sector.. DEEP LEARNING. This course allows students to discover what deep learning means and why it has transformed machine learning tasks such as speech recognition, computer vision, or image recognition. Students will consolidate machine learning fundamentals, explore artificial neural networks and their connection to the human brain, and learn about different types of networks, like feedforward, convolutional, recurrent, and autoencoders, and their applications. With hands-on exercises and real-world examples, you’ll gain both foundational understanding and practical skills in deep learning.. NATURAL LANGUAGE PROCESSING. This course provides a broad overview of Natural Language Processing (NLP), focusing on the automatic understanding of human language and its practical applications. Students will explore the core principles and techniques of NLP, from basic tasks like text preprocessing and sentiment analysis to advanced models such as BERT and ChatGPT. The course emphasizes the interdisciplinary nature of NLP, real-world applications across unstructured data sources, and current industry solutions. It also addresses key challenges such as ambiguity, context dependency, and multilingualism, as well as ethical considerations like bias and privacy. Through theoretical foundations and hands-on practice, students will learn to apply NLP to drive value, innovation, and automation.. FINTECH & BANKING. ARTIFICIAL INTELLIGENCE IN BANKING. This course offers a senior-level view of how data and AI are leveraged in banking to drive value across functions and sustain competitive advantage. Students will explore the structure and key activities of banks, understand how data optimizes commercial efficiency and customer experience, and participate in forward-thinking discussions on the strategic use of big data. Through hands-on work with real-life bank data, students will develop analytical models and design new use cases to rethink an industry facing rapid digital disruption.. ALGORITHMIC TRADING. As financial markets become increasingly digital, algorithmic trading is reshaping the industry. This course explores how investment firms use algorithms to scale operations, optimize trading decisions, and reduce costs by leveraging Big Data and Artificial Intelligence. Students will learn the fundamentals of market microstructure, trading objectives (execution, market-making, investment), and the mathematical frameworks behind optimal strategies. Special focus will be given to the role of AI and emerging trends like Deep Reinforcement Learning, Bayesian Inference, and Large Language Models in the future of trading.. RISK & FRAUD ANALYTICS. This course in Risk & Fraud Analytics (RFA) is a business application course that applies Python and various Machine Learning algorithms to real data and real applications in Finance and Banking. It uses case studies to accelerate learning and encourage class participation. Students will receive hands-on training on traditional and advanced Risk and Fraud Analytics through reading and watchable materials, real-world success cases, practical model development, and forward-thinking discussions on how Big Data is reshaping the financial industry.. WEB 3, BLOCKCHAIN, CRYPTOCURRENCIES AND NFT. This course explores blockchain technology beyond Bitcoin and cryptocurrency, focusing on its potential to create trust and transform business models. It begins with the origins of blockchain and introduces key concepts such as smart contracts, oracles, Dapps, and DAOs, combining theoretical foundations with practical applications. Students will study major cryptocurrencies, blockchain platforms (with a focus on Ethereum), and learn how to develop and interact with smart contracts through simplified programming and case studies. The course also covers real-world blockchain use cases in areas like digital identity, supply chain, and education, introduces Web3, tokenomics, and different types of cryptographic tokens, and examines the rise of decentralized finance (DeFi) and its growing impact across sectors.. DATA ANALYTICS IN THE CLOUD. Data Analytics in the Cloud covers the use of cloud computing technologies to process, store and analyze large data collections. Data analytics enables organizations to gain insights, uncover trends and make decisions to ultimately improve their competitiveness. In addition, cloud computing offers several advantages, such as scalability, flexibility, cost-effectiveness and rapid provisioning of resources. The course is designed to help students learn about and get hands-on practice with the tasks, tools, and strategies that are used to ingest, transform, store and manage data for use in analytics, machine learning (ML) and Generative AI applications. Throughout the course, students will explore use cases from real-world applications, which will enable them to make informed decisions while implementing data pipelines for their particular applications. The content is aligned with the AWS Data Engineer (DEA-C01) exam, enabling students to pursue this certification more effectively upon completing the course.. PROCESS MINING AND AUTOMATION. This course introduces the principles of process mining and engineering, focusing on how to collect and use data from business processes to identify inefficiencies and drive improvements. Students will learn how to use data to enhance decision-making, automate tasks, and integrate IT tools to optimize performance. The course covers techniques for understanding processes through data exploration, and emphasizes the importance of data preparation, such as integration, homogenization, and cleansing for effective process improvement.. QUANTUM COMPUTING FOR BUSINESS. During these course students will gain a comprehensive understanding of quantum computing, and you'll be able to apply quantum computing techniques to solve complex problems in business and financial sectors. Portfolio optimization, risk analysis, supply chain management, and strategic decision-making using quantum algorithms. RETAIL, LUXURY & E-COMMERCE. ARTIFICIAL INTELLIGENCE IN RETAIL & CONSUMER GOODS. On this course you’ll be able to understand the bases of a Retail Business Model end-to-end, identifying where Data and AI can create opportunities and then apply your knowledge through a practical approach. Today’s customers have more choices than ever, creating a major challenge for retailers to stand out. Success depends on building a strong value proposition rooted in deep customer knowledge, starting with data and enhanced through AI and ML to attract, satisfy, and build loyalty across the customer journey. As technology accelerates and new competitors emerge, retailers are increasing digital investments. On the operational side, AI-driven demand forecasting and automation are transforming supply chain efficiency. On the customer side, advanced analytics power everything from store layout and product range to dynamic pricing and personalization. Generative AI is already redefining customer experience. Retail remains a human-to-human industry, and this course explores how data and technology can strengthen those connections.. BIG DATA AND AI IN MARKETING. This course explores how Big Data and Artificial Intelligence (AI) are transforming modern marketing strategies, emphasizing the shift from traditional methods to AI-driven, data-centric approaches. Students will learn to design, implement, and optimize marketing campaigns using tools such as Google Analytics 4 (GA4), advanced attribution models, and machine learning techniques. The course covers digital analytics, user behavior analysis, and AI-enhanced reporting. Students will also explore Marketing Mix Modeling and incrementality measurement using geo experiments and Causal Impact. Additionally, the course integrates Big Data tools including Google Cloud, BigQuery, and machine learning APIs to support advanced marketing analytics. Through real-world applications, students will gain practical experience and the ability to lead innovative, data-driven marketing efforts.. ADVANCED DATA VISUALIZATION. During this course, students will be equipped with the skills to design and produce advanced and compelling visualizations that effectively communicate complex data insights to a variety of audiences. Students will gain a strong knowledge in data visualization, being well-prepared to proficiently use Tableau in real-world data analysis and decision-making contexts.. AGENTIC AI. While previous AI assistants focused on making predictions or generating content, we’re now witnessing the emergence of something far more sophisticated: AI agents that can independently perform complex tasks and make decisions on our behalf. This course offers hands-on training in building autonomous AI systems that can think, learn, and act independently to solve real-world business problems. Focusing on the practical and strategic use of Agentic AI in business analytics and data science, students will explore key concepts such as generative AI, prompt engineering, multi-agent systems, and autonomous decision-making to develop AI systems can autonomously execute tasks and interact with other AI agents.. COMPUTER VISION. Unlock the fascinating world of Computer Vision and bring your Python programming skills to life in this hands-on course. Computer Vision is a field that enables machines to interpret and understand visual information from the world around us. From self-driving cars to facial recognition, Computer Vision has revolutionized various industries. In this course, you will dive deep into the core concepts, algorithms, and practical applications of Computer Vision using the powerful Python programming language.. DATA ANALYTICS IN THE CLOUD. Data Analytics in the Cloud covers the use of cloud computing technologies to process, store and analyze large data collections. Data analytics enables organizations to gain insights, uncover trends and make decisions to ultimately improve their competitiveness. In addition, cloud computing offers several advantages, such as scalability, flexibility, cost-effectiveness and rapid provisioning of resources. The course is designed to help students learn about and get hands-on practice with the tasks, tools, and strategies that are used to ingest, transform, store and manage data for use in analytics, machine learning (ML) and Generative AI applications. Throughout the course, students will explore use cases from real-world applications, which will enable them to make informed decisions while implementing data pipelines for their particular applications. The content is aligned with the AWS Data Engineer (DEA-C01) exam, enabling students to pursue this certification more effectively upon completing the course.. TECH PRODUCT MANAGEMENT. This course is designed for aspiring product managers, founders and business leaders who want to gain a better understanding of the product development process. This course will provide the tools to help you navigate the pitfalls of product management, sustain innovation and give you an entrepreneurial edge. The course runs through the entire product development process from ideation to market entry and growth. It will provide you with the practical knowledge you need to profitably launch new technology into the market, either through an existing company or one you plan to start. Students will develop product management strategies for real-world technologies and innovations.. SPORTS, MEDIA & ENTERTAINMENT . SPORTS ANALYTICS. This course in Sports Analytics is designed to provide students with a comprehensive introduction to the cutting-edge methods and technologies used to analyze and interpret sports data. Integrating a blend of theoretical insights and practical applications, students will develop a nuanced understanding of how data shapes strategies in sports, from player recruitment to in-game decision- making, and marketing. This course introduces students to the field of sports analytics, focusing on the application of big data and business analytics techniques in sports. Students will learn to analyze and interpret complex datasets, develop predictive models, and gain insights into team and player performance, strategy, and sports business.. Sports Management: Creating Value with Technology. This course explores the intersection of business and technology within the sports industry, offering an in-depth look at how these disciplines contribute to the sustainable development of amateur and professional sports properties. Students will gain insights into the role of technology in transforming sports organizations, with a focus on how digital advancements support growth, improve efficiency, and enhance fan engagement.. ARTIFICIAL INTELLIGENCE IN TELECOMMUNICATIONS. During this course you will sharpen your skills as a "business translator" by linking analytical talent and practical solutions to business questions in the telecom and utilities industry. So, why is this useful? In addition to being data-savvy, business translators need to have deep organizational knowledge and functional expertise to ask the data science team the right questions, deriving the right insights from their findings. While it is possible to outsource analytics activities, a business translator should have proprietary knowledge and be deeply involved in the organization.. SPATIAL COMPUTING. AUGMENTED (AR) AND VIRTUAL REALITY (VR). This course provides a comprehensive overview of Spatial Computing, the emerging Web3 economy, and how businesses can use them for growth and success. Through lectures, case studies, and hands-on exercises, students will explore current trends, opportunities, and challenges in the virtual world. They will gain practical knowledge and skills for designing, launching, and managing Web3 businesses, analyzing market trends, managing virtual teams, and creating immersive Spatial Computing experiences.. DATA ANALYTICS IN THE CLOUD. Data Analytics in the Cloud covers the use of cloud computing technologies to process, store and analyze large data collections. Data analytics enables organizations to gain insights, uncover trends and make decisions to ultimately improve their competitiveness. In addition, cloud computing offers several advantages, such as scalability, flexibility, cost-effectiveness and rapid provisioning of resources. The course is designed to help students learn about and get hands-on practice with the tasks, tools, and strategies that are used to ingest, transform, store and manage data for use in analytics, machine learning (ML) and Generative AI applications. Throughout the course, students will explore use cases from real-world applications, which will enable them to make informed decisions while implementing data pipelines for their particular applications. The content is aligned with the AWS Data Engineer (DEA-C01) exam, enabling students to pursue this certification more effectively upon completing the course.. GENERATIVE AI. This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness.. SOCIAL NETWORK ANALYSIS. This course introduces Social Network Analysis (SNA) as a powerful tool to uncover hidden patterns in interconnected systems, such as the flow of goods, services, and information among people, teams, and organizations. Beyond social media, SNA is applicable to fields like marketing, sports, fraud analysis, communication, biology, and organizational studies. The course equips students with the theoretical and practical principles, models, techniques, and metrics to analyze networks. Students will learn key SNA concepts, algorithms for node and link ranking, and how to apply them to real-world problems.. SMART MANUFACTURING & AUTOMATION. Unmanned Aircraft Systems: Strategy, Entrepreneurship and Technology. This cutting-edge course explores the rapidly evolving world of Unmanned Aircraft Systems (UAS), combining deep technological insight with strategic vision and entrepreneurial opportunity. Led by a world-class faculty of global experts, this course empowers students to understand the full landscape of UAS — from drone design and regulation to market disruption and venture creation. You'll gain practical tools to evaluate UAS technologies, develop business models, and navigate the regulatory and ethical frameworks shaping the future of autonomous flight. Whether you're an aspiring entrepreneur, a tech-savvy strategist, or an innovator ready to take off in aerospace, logistics, security, or media, this course will give you the knowledge, network, and inspiration to lead in a skyward industry.. IoT & EMERGING TECHNOLOGIES. This course explores how Industry 4.0 isn't about classical systems or fancy automatizations, but rather about the digital transformation of the industry into a demand-oriented organization. You will learn how to identify industries with high transformation potential and how to envision and project a digital transformation program for industrial companies by leveraging emerging technologies such as IoT, AI, Cloud Computing, 3D Printing, or Robotics. The course combines three types of sessions: Sessions focused on basic concepts to understand what sets Industry 4.0 apart from other sectors. Sessions centered around use-case development and technology-driven transformation examples Practical sessions with real hands-on lab demos on IoT and Streaming technologies applied to the industrial sector.. ROBOTICS. This course covers the key concepts needed to build autonomous robotic systems that can sense, plan, and act in simulated environments. Students will learn how robots interact with the world using sensors and controllers to achieve goals. The course combines theory-based lectures with simulation-based exercises using ROS (Robot Operating System), the standard platform for robotics research. By the end of the course, students will understand the foundations of sensing, planning, and actuation, be able to program and simulate controllers for robotic systems, and gain familiarity with current robotics research.. PROCESS MINING AND AUTOMATION. This course introduces the principles of process mining and engineering, focusing on how to collect and use data from business processes to identify inefficiencies and drive improvements. Students will learn how to use data to enhance decision-making, automate tasks, and integrate IT tools to optimize performance. The course covers techniques for understanding processes through data exploration, and emphasizes the importance of data preparation, such as integration, homogenization, and cleansing for effective process improvement.. REINFORCEMENT LEARNING AND AUTONOMOUS SYSTEMS. This course introduces Reinforcement Learning (RL), a branch of Machine Learning focused on agents interacting with stochastic environments to discover the best actions for achieving goals in dynamic settings. While RL dates back to the 1980s, it has gained significant relevance in the last five years thanks to deep learning techniques. RL enables automation and optimization in complex scenarios, such as autonomous vehicles or strategic gameplay. Students will learn the basics of RL and apply advanced tools to real-life challenges across various fields. Using Python, they will tackle foundational problems to understand the core elements of the discipline.. AGENTIC AI. While previous AI assistants focused on making predictions or generating content, we’re now witnessing the emergence of something far more sophisticated: AI agents that can independently perform complex tasks and make decisions on our behalf. This course offers hands-on training in building autonomous AI systems that can think, learn, and act independently to solve real-world business problems. Focusing on the practical and strategic use of Agentic AI in business analytics and data science, students will explore key concepts such as generative AI, prompt engineering, multi-agent systems, and autonomous decision-making to develop AI systems can autonomously execute tasks and interact with other AI agents.. SUSTAINABLE TECHNOLOGY & INNOVATION. The topic of sustainability has become increasingly important in recent years as the negative impacts of human activity on the environment have become more evident. The technology industry, in particular, has a significant role to play in addressing sustainability challenges and finding innovative solutions.In this course, we will explore how technology and innovation can be used to create a more sustainable future. We will examine the ways in which the tech industry is currently addressing sustainability issues and consider the potential for emerging technologies to drive positive change.. ADVANCED AI. DEEP LEARNING. This course allows students to discover what deep learning means and why it has transformed machine learning tasks such as speech recognition, computer vision, or image recognition. Students will consolidate machine learning fundamentals, explore artificial neural networks and their connection to the human brain, and learn about different types of networks, like feedforward, convolutional, recurrent, and autoencoders, and their applications. With hands-on exercises and real-world examples, you’ll gain both foundational understanding and practical skills in deep learning.. GENERATIVE AI. This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness.. AGENTIC AI. While previous AI assistants focused on making predictions or generating content, we’re now witnessing the emergence of something far more sophisticated: AI agents that can independently perform complex tasks and make decisions on our behalf. This course offers hands-on training in building autonomous AI systems that can think, learn, and act independently to solve real-world business problems. Focusing on the practical and strategic use of Agentic AI in business analytics and data science, students will explore key concepts such as generative AI, prompt engineering, multi-agent systems, and autonomous decision-making to develop AI systems can autonomously execute tasks and interact with other AI agents.. NATURAL LANGUAGE PROCESSING. This course provides a broad overview of Natural Language Processing (NLP), focusing on the automatic understanding of human language and its practical applications. Students will explore the core principles and techniques of NLP, from basic tasks like text preprocessing and sentiment analysis to advanced models such as BERT and ChatGPT. The course emphasizes the interdisciplinary nature of NLP, real-world applications across unstructured data sources, and current industry solutions. It also addresses key challenges such as ambiguity, context dependency, and multilingualism, as well as ethical considerations like bias and privacy. Through theoretical foundations and hands-on practice, students will learn to apply NLP to drive value, innovation, and automation.. COMPUTER VISION. Unlock the fascinating world of Computer Vision and bring your Python programming skills to life in this hands-on course. Computer Vision is a field that enables machines to interpret and understand visual information from the world around us. From self-driving cars to facial recognition, Computer Vision has revolutionized various industries. In this course, you will dive deep into the core concepts, algorithms, and practical applications of Computer Vision using the powerful Python programming language.. REINFORCEMENT LEARNING AND AUTONOMOUS SYSTEMS. This course introduces Reinforcement Learning (RL), a branch of Machine Learning focused on agents interacting with stochastic environments to discover the best actions for achieving goals in dynamic settings. While RL dates back to the 1980s, it has gained significant relevance in the last five years thanks to deep learning techniques. RL enables automation and optimization in complex scenarios, such as autonomous vehicles or strategic gameplay. Students will learn the basics of RL and apply advanced tools to real-life challenges across various fields. Using Python, they will tackle foundational problems to understand the core elements of the discipline.. DATA ANALYTICS IN THE CLOUD. Data Analytics in the Cloud covers the use of cloud computing technologies to process, store and analyze large data collections. Data analytics enables organizations to gain insights, uncover trends and make decisions to ultimately improve their competitiveness. In addition, cloud computing offers several advantages, such as scalability, flexibility, cost-effectiveness and rapid provisioning of resources. The course is designed to help students learn about and get hands-on practice with the tasks, tools, and strategies that are used to ingest, transform, store and manage data for use in analytics, machine learning (ML) and Generative AI applications. Throughout the course, students will explore use cases from real-world applications, which will enable them to make informed decisions while implementing data pipelines for their particular applications. The content is aligned with the AWS Data Engineer (DEA-C01) exam, enabling students to pursue this certification more effectively upon completing the course.. TECH CONSULTING & DATA STRATEGY. STRATEGIC TECHNOLOGY CONSULTING. This is a dynamic course designed for students seeking to excel in the tech consulting arena. The course provides a comprehensive overview of the consulting sector, emphasizing the strategic role of technology in driving business success. Students will explore essential problem-solving frameworks, agile and waterfall project management methodologies, datadriven decision-making, effective communication techniques, and change management strategies. Through a mix of interactive lectures, hands-on exercises, and real-world case studies led by industry professionals, this course equips you with the practical skills and insights needed to thrive in strategic technology consulting roles.. EXCEL FOR DATA SCIENCE & CONSULTING. Excel is used by an impressive number of users globally (between 1.1 and 1.5 bilion) since its introduction in 1985. Despite its longevity, many reports and forecasts today continue to be constructed using Excel's original capabilities, which have been developed and refined over the years. Although not a substitute for advanced data modeling and machine learning techniques, the truth is that Excel is the most widely used tool in the world and the only one that millions of business professionals use to consume and develop data analysis. It is in many scenarios the best way to prototype, communicate and confirm analytical solutions to business problems. The aim of this course is to help students to increase their usage of the spreadsheet solving a varied set of exercises. You will develop data preparation and modeling, apply regression analysis to business scenarios, forecast demand, while communicating data insights effectively.. DATA GOVERNANCE. The objective of the course is to provide students with necessary skills that will allow them to efficiently manage the life cycle of an organization's data.All organizations regardless of size, business nature or purpose, grew data’s importance, elevating its status to a strategic that every organization must manage accordingly. Managing data is a complex activity including many aspects, both in business and IT areas. On the other hand, being successful is only feasible when data is properly managed, and Data Governance is the heart of Data Management.. TECH PRODUCT MANAGEMENT. This course is designed for aspiring product managers, founders and business leaders who want to gain a better understanding of the product development process. This course will provide the tools to help you navigate the pitfalls of product management, sustain innovation and give you an entrepreneurial edge. The course runs through the entire product development process from ideation to market entry and growth. It will provide you with the practical knowledge you need to profitably launch new technology into the market, either through an existing company or one you plan to start. Students will develop product management strategies for real-world technologies and innovations.. SALES ENGINEERING AND CONSULTING SKILLS. This course explores the intersection of technology sales, data analytics, and digital strategy. Students will learn to apply data-driven methods to identify high-potential leads, optimize sales pipelines, and enhance B2B engagement. By combining sales fundamentals with analytics tools and industry insights, the course prepares students to drive value in tech-driven, customer-centric business environments.. TECH ENTREPRENEURSHIP. DEEP TECH VENTURING AND INVESTMENT. This course explores Deep Tech, advanced and disruptive technologies driven by scientific breakthroughs such as AI, robotics, quantum computing, and biotechnology. It prepares students to pursue impactful careers in innovation-driven fields and equips them with the skills to navigate the evolving landscape of companies like Tesla, OpenAI, NASA, and more. Students will gain insight into how technologies like self-driving cars, AI algorithms, and space exploration are shaping the future. The course encourages creativity, innovation, and entrepreneurial spirit, opening the door to meaningful and inspiring career opportunities in deep tech.. VENTURE LAB. VENTURE LAB CAPSTONE PROJECT. TECH PRODUCT MANAGEMENT. This course is designed for aspiring product managers, founders and business leaders who want to gain a better understanding of the product development process. This course will provide the tools to help you navigate the pitfalls of product management, sustain innovation and give you an entrepreneurial edge. The course runs through the entire product development process from ideation to market entry and growth. It will provide you with the practical knowledge you need to profitably launch new technology into the market, either through an existing company or one you plan to start. Students will develop product management strategies for real-world technologies and innovations.. SUSTAINABLE TECHNOLOGY & INNOVATION. The topic of sustainability has become increasingly important in recent years as the negative impacts of human activity on the environment have become more evident. The technology industry, in particular, has a significant role to play in addressing sustainability challenges and finding innovative solutions. In this course, we will explore how technology and innovation can be used to create a more sustainable future. We will examine the ways in which the tech industry is currently addressing sustainability issues and consider the potential for emerging technologies to drive positive change.. AEROSPACE TECHNOLOGY. COMPUTER VISION. This elective course offers an introduction to the principles and applications of Computer Vision, the field of computer science that enables machines to interpret and understand visual information from the world. Students will learn about image processing, feature detection, object recognition, and image classification, as well as explore techniques. Unmanned Aerial Systems  . This elective course introduces students to Unmanned Aerial Systems (UAS), commonly known as drones, focusing on their design, operation, and diverse applications. The course covers the fundamentals of UAS components, flight dynamics, navigation, control systems, and mission planning. Students will also explore key regulatory, ethical, and safety considerations, as well as current and emerging uses of UAS in fields such as agriculture, surveillance, environmental monitoring, logistics, and disaster management. Practical sessions and case studies will help students develop hands-on skills in UAS operation and data analysis, preparing them for careers in this rapidly growing technological field.. Geoimaging and Geolocalization. This elective course explores the technologies and methodologies behind geoimaging and geolocalization, focusing on how spatial data is captured, processed, and used to understand and interact with the physical world. Students will study remote sensing, satellite and aerial imagery, GPS technologies, and geographic information systems (GIS). The course emphasizes image interpretation, spatial analysis, and location-based services, highlighting applications in urban planning, environmental monitoring, navigation, and smart cities. Through theoretical learning and practical exercises, students will gain the skills to analyze geospatial data and develop solutions that leverage location intelligence.. Space Supply Chain Technology. This elective course examines the emerging field of Space Supply Chain Technology, focusing on the unique challenges and innovations involved in supporting space missions and commercial space operations. Students will explore the design, management, and optimization of supply chains for space systems, including launch vehicles, satellites, space stations, and lunar or planetary missions. Key topics include logistics in extreme environments, space-grade manufacturing, risk management, regulatory frameworks, and the role of emerging technologies such as additive manufacturing, AI, and the like. The course combines theoretical foundations with real-world case studies to prepare students for careers in the evolving space industry and its global supply networks.. Space Tech Venturing. This elective course explores the intersection of space technology and entrepreneurship, focusing on how innovative ventures are driving the commercialization of space. Students will gain insights into the space tech startup ecosystem, investment trends, and the pathways for turning space-related innovations into viable businesses. Topics include opportunity identification, business model development, funding strategies, regulatory considerations, and go-to-market approaches specific to the space industry. Through case studies, guest lectures, and hands-on projects, students will learn how to evaluate, launch, and scale ventures in areas such as satellite services, space tourism, launch systems, and space-based data applications. The course is ideal for aspiring entrepreneurs, investors, and innovators seeking to participate in the New Space economy.. EXCHANGES. Spring Intake. Broaden your perspective by spending your Elective Period in term 3 on an International Exchange at one of our world-class partner universities. Over the course of the exchange, Master in Business Analytics and Data Science students will add depth to their academic knowledge, expand their international experience, and widen their professional network. While schools available for exchange vary according to the timing of the Electives Period, this program offers exchange agreements for both the Spring and Fall intakes:. Fall Intake. INTERNSHIPS. During the Elective Period, you can apply to do an internship. This option has been created for those who wish to gain specific, real-world experience that will support their career change to another industry, sector, region and/or role. There are two ways you may apply to an internship: The IE School of Science and Technology has a panel of partner companies you can apply to intern with. These companies are very diverse and could also include international internships. Alternatively, you can find an internship by other means, in which case your course coordinator would need to approve it based on the conditions and eligibility criteria.. Electives. In the third term, in addition to the option of pursuing a concentration, you may instead choose from a diverse list of electives, enabling you to tailor your academic experience to your personal and professional goals.. Advanced Data Visualization Algorithmic Trading Artificial Intelligence In Banking Artificial Intelligence In Health Artificial Intelligence In Retail & Consumer Goods Big Data And AI In Marketing Computer Vision Data Analytics In The Cloud Deep Learning Excel For Data Science & Consulting Generative AI Iot & Emerging Technologies Natural Language Processing Quatum Computing For Business Reinforcement Learning & Autonomous Systems Risk & Fraud Analytics Web 3, Blockchain, Cryptocurrencies And Nft. Social Network Analysis Spatial Computing. Augmented (Ar) And Virtual Reality (Vr) Sports Analytics Strategic Technology Consulting Sustainable Technology & Innovation Tech Product Management Deep Tech Venturing And Investment Process Mining & Automation Agentic AI Sales Engineering And Consulting Skills Unmanned Aircraft Systems: Strategy, Entrepreneurship And Technology Robotics Artificial Intelligence In Telecommunications Sports Management: Creating Value With Technology Clinical Research And Development Of Medical Device Technology

CAPSTONE PROJECTS. CAPSTONE PROJECTS. The culmination of your learning experience will be a Capstone Project. In essence, it is a final integrative exercise that will take place over the course of two months. You can select a Venture Lab Business Plan in the area of Data Science and Business Analytics or a Corporate Project with a firm.. CORPORATE PROJECT. The Corporate Project is a consulting mini-project that addresses an organization’s real-world needs. The goal is for students to use the business skills they learned during the programme to make a genuine impact. With the help of a mentor, teams of students collaborate with a company, NGO, startup, or institution to solve a Data Science or Business Analytics challenge. During the Master Electives Period, the students will develop a proposal over the course of two months. The Corporate Project allows students to use their “toolkit” to help solve a real-world problem. The Corporate Project is a course offered as part of the Master in Business Analytics & Data Science program, an alternative to the Venture Project. Some of the companies that have participated in Corporate Projects include: MICROSOFT, BCG, AIRBUS, BULGARI, INDITEX, KPMG, IBM, ALLFUNDS, CAPGEMINI, REPSOL, ZURICH DIGITAL, FITIZENS, FERROVIAL, and NAVANTIA.. VENTURE PROJECT. The Venture Project trains and mentors teams to investigate, validate, and develop and MVP and a pilot, with the target to make it a ready-to-launch start-up venture, in Data Science and/or Business Analytics. It will be created through the Venture Lab. The ultimate goal of the Venture Lab is to finish the programme with an MVP and a fully operative pilot of a product/service in the field of Data Science and Business Analytics. The entire focus is on applying what is learned in the program, gathering enough data and creating an operative tool that will help to easily move from Pre-Launch to Launch activities. The primary objective of a Venture Project is to give teams the opportunity to initiate a ready-to-launch start-up project. This is accomplished through first-hand experience in the world of high impact entrepreneurship. The Venture Project is a course offered as part of the Master in Business Analytics & Data Science programme, alternative to the Corporate Project.. RESEARCH PROJECT. Students who choose the Academic Research Project as their Final Impact Project will be required to demonstrate the application and integration of the knowledge acquired throughout the program through an academic research initiative. Students will explore a specific area of interest related to business analytics and data science in depth. Each student will be assigned a full-time faculty researcher as their academic supervisor. The ultimate goal of this project is to develop a rigorous research study on the selected topic, which must include a well-structured academic paper with a relevant literature review and a robust research methodology.

MAKE THE MOST OF YOUR PROGRAM. CERTIFICATIONS. CERTIFICATION DESCRIPTION. CERTIFICATIONS. This program helps you boost your career through certifications from AWS, Microsoft, and others. These certifications are designed to strengthen your profile and support your progress along your chosen career path: Amazon web services academy. Amazon Web Services Academy Microsoft learn AWS Certified Data Engineer Certified Tableau Data Analyst Certified Tableau Desktop Specialist IE Certificate in Foundations of Sustainability. SUSTAINABIILITY CERTIFICATE. In today’s world, organizations are placing sustainability and environmental, social and governance (ESG) concerns at the heart of their businesses. At IE, we offer an optional certificate to help you learn how to tackle these challenges and adopt a sustainability mindset. With the IE Certificate on Foundations of Sustainability, you’ll get prepared to tackle the social, economic and environmental challenges of today’s world in any organization. This certificate can be completed alongside your core studies. In order to earn the certificate, students must obtain ten relevant credits. Students may obtain these credits through eligible courses, electives, and extracurricular activities. Some of the aforementioned may already be included in your program, while others will need to be added. Additionally, there’s one mandatory component, the Online Learning Journey and the Sustainability Datathon. Companies that hace been sponsors:NTT Data, Acciona, Ryanair, EDP Renewables, Repsol,INFOSYS, Acqualia Maximum flexibility has been assured, providing several ways to earn the required number of credits.. MENTORSHIP PROGRAM. MENTORSHIP PROGRAM. The Tech Mentorship Program is an initiative designed to establish a personal and professional relationship of learning and trust between a mentor and a mentee.Students who choose to participate will be matched with a mentor from our global mentors community, which includes over 120 professionals. The program focuses on personalized growth, expanding professional networks, and enhancing technical and professional skills.. INTERNATIONAL EXPERIENCES. BERKELEY IMMERSION WEEK. Berkeley Immersion Week. Fast track your tech career: Berkeley Immersion Week opens doors to Silicon Valley's top companies and minds.. TECH IMMERSION WEEK. Opportunities, challenges and networking. Experience global tech hubs such as Amsterdam, Dublin or Dubai among others, through the Global Immersion Week. This international journey offers the opportunity to visit leading companies, engage with industry professionals, explore diverse cultures, and gain valuable insights into the tech landscape and your future career.. TECH INITIATIVES. Berkeley Immersion Week. Joined by other students from IE School of Science & Technology, you'll participate in a hands-on, one-week program where you'll meet industry leaders from Silicon Valley.. Tech Venture Bootcamp. The Tech Venture Bootcamp is a pioneering 6-day program designed for innovators who are eager to create groundbreaking ventures in their own unique way.. IE SUSTAINABILITY DATATHON. The IE Sustainability Datathon is a data-driven sustainability challenge where students collaborate with an industry partner to analyze real datasets and design innovative solutions to environmental issues.. Rise Europe Alliance. The Rise Europe Alliance, comprising 20 academic and entrepreneurial institutions, shapes the next generation of European talent through diverse perspectives and collaboration, fostering global market success and driving innovation at its annual summit.. Berkeley Startup Semester. As a Global Partner with Berkeley SCET, IE enables students to enroll in the Startup Semester at Berkeley after completing their programs at IE. This full-time program offers a unique opportunity to unleash creativity, gain global experience, and immerse themselves in the heart of Silicon Valley. Guided by top-tier faculty, mentors, entrepreneurs, accelerators, and investors, participants also engage in cultural activities throughout the Bay Area. Students can choose from two tailored tracks to support their entrepreneurial journey: Venture Discovery Track – Ideal for those looking to explore and develop a new venture. Venture Validation Track – Designed for students with an existing project who want to accelerate its growth. The program includes hands-on courses at SCET and concludes with an official certificate from UC Berkeley, equipping students with real-world skills and global credentials in entrepreneurship.. Management Xponential Technology (MXT). Designed for students with extraordinary ambition to transform the world through disruptive technologies, this program fosters a growth mindset and combines the expertise of IE School of Science and Technology and IE Business School.  Students gain the knowledge, tools, and networks to create impact at the intersection of business and technology, while engaging with leading entrepreneurs, innovators, and global leaders who inspire their transformative journeys.. ACADEMIC ESSENTIALS. IMPACT SKILLS ACCELERATOR. In the ever-evolving landscape of data science and AI, the ability to tackle complex challenges, leverage emerging AI tools for productivity and efficiency, and master high-impact skills has become a hallmark of success. As the field continues to advance, the demand for professionals who demonstrate strong critical thinking, solve problems creatively, lead cross-functional teams, and communicate insights with clarity and influence is greater than ever. To develop well-rounded professionals fully aligned with market needs, the following courses are integrated into the academic journey. Prompt Engineering. GitHub Portfolio Management. Communication Skills. Building High-Performance Teams. Diversity Workshop. Critical Thinking. Agile Thinking. Design Thinking. Project Management. Influence and Persuasion.. Career Accelerator Program . The Career Accelerator Program is a comprehensive career development journey designed to support students at every stage of their professional path — from discovering where to start, to focusing their ideas, and putting their career plans into action. It offers resources to help students identify what they want from their future, learn about different career paths, make informed decisions, and successfully take their next steps. The program provides: Key information on a wide variety of sectors, industries, and career paths today. Guidance on making successful applications, preparing for interviews, practicing online assessments, and learning how to build your network. This hands-on program equips students with the tools, confidence, and strategies needed to succeed in today’s dynamic job market.. COMPANY VISITS. Throughout the program, you’ll have the opportunity to visit leading tech companies, gaining first-hand exposure to cutting-edge technologies and connecting with professionals who are driving innovation in computer science and digital transformation. You'll visit companies such as:

START MODULE. Pre-program. Description. Pre-Program. This self-paced material is selected to introduce the basic concepts and tools you will need during the program. Most of what you will learn here will be repeated during some of the courses, but repetition is part of the learning process, mostly for students who do not have a technical background. The Learning objectives include: Learn the foundamentals of Computational Thinking Practice the basics aspects of Python Begin using Git Hub Know the main aspects of Artificial Intelligence for Education Introduce the usage of Excel for business and analytics Understand how to use Linux Obtain a basic level on Quantitative Methods Briefly introduce yourself in how SQL works. COMPUTATIONAL THINKING. This multimedia material aims to level the knowledge of any new programming students, providing a fundamental understanding of coding before entering the course. This 2-hour preprogram features a combination of explanatory videos from the professor, interactive activities and diagrams, readings and quizzes.. QUANTITATIVE METHODS. The Quantitative Methods Preprogram, has been designed to allow you to prepare yourself and achieve the basic knowledge necessary before beginning the Program.. EXCEL. This course is a practical approach to Excel as tool to solve business problems. This course will help you understand the basic ways to work with Excel for Business. A final test will be included to check your understanding of this basic introduction and their level of Excel.. CODING IN PYTHON. Python is a clear and powerful  programming language, comparable to Perl, Ruby, Scheme, or Java. The reasons it has become so popular in the data scientist community is that it is an easy-to-use language that makes it simple to get your program working, it´s free and specific packages exists to make Python usable for data analysis. For example NumPy /SciPy, Pandas, Matplotlib, Scikit-learn, etc.. LINUX. Linux is the most important operating system for us, as all the Big Data technologies runs under Linux. It is very similar to others operating systems such as Windows and MacOS. Having knowledge of the most basic commands of Linux will be really helpful when students deal with some of the courses, for example when interacting with Hadoop through command.. SQL. SQL is the language of the data, widely used in every company and one of the most used tools to do Analytic. During the master you will learn, starting from scratch, the SQL you need to know but the following online course will give you a very good introduction to this subject, featuring insights from leading companies in the industry.. GITHUB. Git Hub is an online hosting service that became the standar in many areas regarding software development, content repository and version controlling. Many open source software projects are hosted and developed there. It will be used regularly during the program.. ARTIFICIAL INTELLIGENCE 101. AI 101 is a self-paced material about the importance and steps necessary to build a successful relationship with AI tools.. CAREERS DEVELOPMENT. IE’s Career Accelerator Preprogram is a part of the Career Success curriculum taught during the academic program. Career Accelerator content covers: All stages of your career journey, from discovering where to start to focusing your ideas, through actioning your career plans. Resources to help you identify what you want from your future, learn about different career paths, make decisions, and support you to make your next steps a success. Key information on a wide variety of sectors, industries, and career paths today. Making successful applications, preparing for interviews, practicing online assessments, and learning how to build your network.. Foundations Week. This week, preceding the Opening is mandatory for all students in the face-to-face intakes and it marks a crucial stage in building the foundational knowledge and skills necessary for the program ahead.. MATHEMATICS FUNDAMENTALS. This course is designed to provide students with a strong foundation in the core concepts of mathematics, including set theory, probability and statistics, calculus, and linear algebra, enabling them to tackle various research questions through the examination of large datasets It will cover topics such as descriptive and inferential statistics to demonstrate how sample data can be employed to approximate, make choices, forecasts, or draw broader conclusions about larger populations. We will also see how common mathematical concepts such as derivatives or matrices are applied in today´s AI & ML models.. Programming Thinking. Programming Thinking empowers students with the critical thinking skills, tools, and frameworks needed to thrive as programmers. This course is less about coding and more about understanding the fundamental concepts and how they are applied towards solving problems in a systematic and logical manner. Key concepts demonstrated in this course are programming language-agnostic and can be found in most programming languages. To deepen their understanding and prepare them for future studies, students explore these concepts through interactive Python coding exercises using Google Colab. Students will also gain a high-level understanding of Generative AI and how to use it effectively as a programmer.

CORE PERIOD. TERM 1. STATISTICS FOR DATA SCIENCE. This course provides you with a working methodology and strong knowledge base for using statistical and mathematical tools in data analysis.. BIG DATA & ARTIFICIAL INTELLIGENCE IN BUSINESS STRATEGY. In the digital age, with the exponential growth of data and the advancement of Artificial Intelligence (AI) technologies, businesses are at a crossroads. Those that can effectively harness the power of AI and Big Data are poised to lead their industries, while those that don't risk obsolescence. This comprehensive course seeks to equip participants with the knowledge, tools, and strategies necessary to guide their organizations through this transformation. This course introduces you to the rest of the program and gives you a “big picture” perspective on Big Data & Analytics.  . MODERN DATA ARCHITECTURES FOR BIG DATA I. This course provides an overview of Big Data and the core technologies and architectures that support data-driven companies. Students will learn how Big Data solutions have enabled businesses to harness massive amounts of data to gain business insights. By the end of the course, students will have a broad understanding of the Big Data landscape, familiarity with leading frameworks and tools, and hands-on experience applying their knowledge. This course establishes a foundation for more advanced study of data technologies, architectures, and analytics. Students will be equipped to determine how Big Data can impact business needs in various domains.. SQL BASED DATA ARCHITECTURES I. This course provides a comprehensive introduction to SQL as a powerful tool for data exploration and decision-making. SQL is  "The language of the Data" used to interact with relational databases. Participants will gain a solid understanding of database concepts, develop proficiency in writing SQL queries to retrieve, manipulate, and update data. The course starts with essential topics from  the beginning, without requiring prior knowledge of the SQL language, and adding simple layers to the learning, will get deeper so students are capable to solve complex business queries. Learning is mainly obtained through practical examples and hands-on exercises that will enable students to learn how to solve business questions with SQL.. SQL BASED DATA ARCHITECTURES II. During SQL I students see how to load, manipulate and access data stored in relational databases using SQL language. In SQL II, we'll discover what are data models, how do they work, why are they so relevant, how many different types of data models do we have, etc. We'll be using a more advance SQL to organize and access the data stored in operational and informational systems. Furthermore, concepts like operational systems, data warehouse, data marts, online data stores, data lakes, olap servers, etc., will be included in this exciting journey through data systems.. PYTHON FOR DATA ANALYTICS I. This course provides a comprehensive introduction to Python, a versatile and widely used programming language, with a focus on practical applications in both data analytics and machine learning. Students will master the fundamentals of Python syntax and learn to solve algorithmic problems efficiently. The course emphasizes hands-on experience through exercises in Jupyter Notebooks, where participants will develop strong skills in exploring, analyzing, and manipulating tabular data using Pandas. By the end of the course, students will be well-prepared to tackle real-world challenges in data analytics and machine learning.. BIG DATA & ARTIFICIAL INTELLIGENCE IN OPERATIONS MANAGEMENT. The course's main objective is to provide students with a working methodology and a solid knowledge base for the use of forecasting models and econometric techniques in the areas of business and economics. Students will learn how to identify and how to use properly one of the most well known family of forecasting linear models and the most useful nonlinear model. Students will learn how looking at past data can turn rows of dates and numbers into clear predictions for things like demand, traffic, or production.And other tools can spot and forecast sudden changes or quiet periods, helping you understand risks ahead of time. Together, these methods help you get ready for what’s coming—whether you’re running a hospital, warehouse, power grid, or market.. TERM 2. TERM 2. MODERN DATA ARCHITECTURES FOR BIG DATA II. This course is the continuation of Modern Data Architectures for Big Data I, where we'll dive deeper into more advanced processing techniques with Spark such as real-time processing, graph processing and Machine Learning workloads at scale.  Students will establish a foundation for more advanced study of data technologies, Big Data Architectures and analytics. By the end of the course, you will be equipped to determine how Big Data can impact business needs in various domains and make informed decisions about implementing data-driven solutions.. MACHINE LEARNING I. Machine Learning (ML) is a a cutting-edge branch of Artificial Intelligence that's transforming industries and revolutionizing the way decisions are made, by learning from data without being explicitly programmed. In this course, you'll gain hands-on experience preparing data, applying essential ML techniques, and building predictive models that drive real-world impact. Whether you're entering finance, healthcare, tech, marketing or beyond, ML is a must-have skill for the data-driven future.. MACHINE LEARNING II. As a continuation of the introductory one, this hands-on course explores more advanced techniques and algorithms of both supervised and unsupervised learning. Through hands-on coding, increasingly complex datasets and advanced model implementation the participant will gain the skills needed to tackle high-impact ML challenges across real-world domains. Ideal for learners ready to go beyond the basics, this course helps you deepen your understanding of ML workflows and sharpen your ability to build models that deliver impact.. MLOps: MACHINE LEARNING OPERATION. In this course, students will explore the complete lifecycle of putting machine learning models into production. While training a model is often seen as the final step, MLOps reveals it is just the beginning. Students will learn about key stages such as model development, CI/CD, monitoring, validation, and governance. Through theoretical lectures and practical demonstrations, they will gain the tools to navigate real-world challenges and apply best practices for managing models in production.. PYTHON FOR DATA ANALYSIS II. Building on Python for Data Analytics 1, this course takes you further into the Python ecosystem for analytics and machine learning projects. You'll master advanced data wrangling with regex, time series analysis, and feature engineering, while learning to build robust scikit-learn pipelines. The curriculum covers essential ML project concepts like scaling, cross-validation, and hyperparameter optimization—equipping you with the practical tools and workflows needed to confidently tackle real-world machine learning challenges in Python.. DATA VISUALIZATION. This course is designed to provide students with a comprehensive understanding of data visualization within the context of business analytics and data science. It covers the theoretical foundations, design principles, chart taxonomy, tools, and applications of data visualization, with an emphasis on its significance, purpose, and impact on various domains. It will provide students with the required knowledge and skills to extract actionable insights from raw data through the use of proper visualization and storytelling, with the ultimate goal of efficiently communicating conclusions and influencing key stakeholders. Students will become proficient in some of the most in-demand business intelligence and data visualization tools on the market.. MODERN DATA ARCHITECTURES FOR BIG DATA II. This course is the continuation of Modern Data Architectures for Big Data I, where we'll dive deeper into more advanced processing techniques with Spark such as real-time processing, graph processing and Machine Learning workloads at scale.  Students will establish a foundation for more advanced study of data technologies, Big Data Architectures and analytics. By the end of the course, you will be equipped to determine how Big Data can impact business needs in various domains and make informed decisions about implementing data-driven solutions.. MACHINE LEARNING I. Machine Learning (ML) is a a cutting-edge branch of Artificial Intelligence that's transforming industries and revolutionizing the way decisions are made, by learning from data without being explicitly programmed. In this course, you'll gain hands-on experience preparing data, applying essential ML techniques, and building predictive models that drive real-world impact. Whether you're entering finance, healthcare, tech, marketing or beyond, ML is a must-have skill for the data-driven future.. MACHINE LEARNING II. As a continuation of the introductory one, this hands-on course explores more advanced techniques and algorithms of both supervised and unsupervised learning. Through hands-on coding, increasingly complex datasets and advanced model implementation the participant will gain the skills needed to tackle high-impact ML challenges across real-world domains. Ideal for learners ready to go beyond the basics, this course helps you deepen your understanding of ML workflows and sharpen your ability to build models that deliver impact.. MLOps: MACHINE LEARNING OPERATION. In this course, students will explore the complete lifecycle of putting machine learning models into production. While training a model is often seen as the final step, MLOps reveals it is just the beginning. Students will learn about key stages such as model development, CI/CD, monitoring, validation, and governance. Through theoretical lectures and practical demonstrations, they will gain the tools to navigate real-world challenges and apply best practices for managing models in production.. PYTHON FOR DATA ANALYSIS II. Building on Python for Data Analytics 1, this course takes you further into the Python ecosystem for analytics and machine learning projects. You'll master advanced data wrangling with regex, time series analysis, and feature engineering, while learning to build robust scikit-learn pipelines. The curriculum covers essential ML project concepts like scaling, cross-validation, and hyperparameter optimization—equipping you with the practical tools and workflows needed to confidently tackle real-world machine learning challenges in Python.. DATA VISUALIZATION. This course is designed to provide students with a comprehensive understanding of data visualization within the context of business analytics and data science. It covers the theoretical foundations, design principles, chart taxonomy, tools, and applications of data visualization, with an emphasis on its significance, purpose, and impact on various domains. It will provide students with the required knowledge and skills to extract actionable insights from raw data through the use of proper visualization and storytelling, with the ultimate goal of efficiently communicating conclusions and influencing key stakeholders. Students will become proficient in some of the most in-demand business intelligence and data visualization tools on the market.

ELECTIVE PERIOD. TERM 3. TERM 3 DESCRIPTION. The elective period also offers you the opportunity to sharpen your career focus, allowing you to use electives to customize and complement your program’s core courses and pave the way to your dream job. Choose electives in line with your concentration area of interest, or dive deeper into topics that best fit your career objectives.  While it is not mandatory to select a concentration, doing so will give you a better understanding of market focus and your specific areas of interest within the industry. Only one concentration can be obtained and offering will be subject to student demand: Advanced AI. Fintech & Banking. Retail & FMCG. Smart Manufacturing & Automation.. CONCENTRATIONS. ADVANCED AI​. DATA GOVERNANCE. The objective of the course is to provide students with necessary skills that will allow them to efficiently manage the life cycle of an organization's data.All organizations regardless of size, business nature or purpose, grew data’s importance, elevating its status to a strategic that every organization must manage accordingly. Managing data is a complex activity including many aspects, both in business and IT areas. On the other hand, being successful is only feasible when data is properly managed, and Data Governance is the heart of Data Management.. Deep Learning. This course allows students to discover what deep learning means and why it has transformed machine learning tasks such as speech recognition, computer vision, or image recognition. Students will consolidate machine learning fundamentals, explore artificial neural networks and their connection to the human brain, and learn about different types of networks, like feedforward, convolutional, recurrent, and autoencoders, and their applications. With hands-on exercises and real-world examples, you’ll gain both foundational understanding and practical skills in deep learning.. Natural Language Processing. This course provides a broad overview of Natural Language Processing (NLP), focusing on the automatic understanding of human language and its practical applications. Students will explore the core principles and techniques of NLP, from basic tasks like text preprocessing and sentiment analysis to advanced models such as BERT and ChatGPT. The course emphasizes the interdisciplinary nature of NLP, real-world applications across unstructured data sources, and current industry solutions. It also addresses key challenges such as ambiguity, context dependency, and multilingualism, as well as ethical considerations like bias and privacy. Through theoretical foundations and hands-on practice, students will learn to apply NLP to drive value, innovation, and automation.. Social Network Analysis. This course introduces Social Network Analysis (SNA) as a powerful tool to uncover hidden patterns in interconnected systems, such as the flow of goods, services, and information among people, teams, and organizations. Beyond social media, SNA is applicable to fields like marketing, sports, fraud analysis, communication, biology, and organizational studies. The course equips students with the theoretical and practical principles, models, techniques, and metrics to analyze networks. Students will learn key SNA concepts, algorithms for node and link ranking, and how to apply them to real-world problems.. Reinforcement Learning & Autonomous Systems. This course introduces Reinforcement Learning (RL), a branch of Machine Learning focused on agents interacting with stochastic environments to discover the best actions for achieving goals in dynamic settings. While RL dates back to the 1980s, it has gained significant relevance in the last five years thanks to deep learning techniques. RL enables automation and optimization in complex scenarios, such as autonomous vehicles or strategic gameplay. Students will learn the basics of RL and apply advanced tools to real-life challenges across various fields. Using Python, they will tackle foundational problems to understand the core elements of the discipline.. Generative & Agentic AI. Generative AI: This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness.. FINTECH & BANKING. ARTIFICIAL INTELLIGENCE IN BANKING. This course offers a senior-level view of how data and AI are leveraged in banking to drive value across functions and sustain competitive advantage. Students will explore the structure and key activities of banks, understand how data optimizes commercial efficiency and customer experience, and participate in forward-thinking discussions on the strategic use of big data. Through hands-on work with real-life bank data, students will develop analytical models and design new use cases to rethink an industry facing rapid digital disruption.. Risk & Fraud Analytics. This course in Risk & Fraud Analytics (RFA) is a business application course that applies Python and various Machine Learning algorithms to real data and real applications in Finance and Banking. It uses case studies to accelerate learning and encourage class participation. Students will receive hands-on training on traditional and advanced Risk and Fraud Analytics through reading and watchable materials, real-world success cases, practical model development, and forward-thinking discussions on how Big Data is reshaping the financial industry.. DATA GOVERNANCE. The objective of the course is to provide students with necessary skills that will allow them to efficiently manage the life cycle of an organization's data.All organizations regardless of size, business nature or purpose, grew data’s importance, elevating its status to a strategic that every organization must manage accordingly. Managing data is a complex activity including many aspects, both in business and IT areas. On the other hand, being successful is only feasible when data is properly managed, and Data Governance is the heart of Data Management.. Deep Learning. This course allows students to discover what deep learning means and why it has transformed machine learning tasks such as speech recognition, computer vision, or image recognition. Students will consolidate machine learning fundamentals, explore artificial neural networks and their connection to the human brain, and learn about different types of networks, like feedforward, convolutional, recurrent, and autoencoders, and their applications. With hands-on exercises and real-world examples, you’ll gain both foundational understanding and practical skills in deep learning.. Natural Language Processing. This course provides a broad overview of Natural Language Processing (NLP), focusing on the automatic understanding of human language and its practical applications. Students will explore the core principles and techniques of NLP, from basic tasks like text preprocessing and sentiment analysis to advanced models such as BERT and ChatGPT. The course emphasizes the interdisciplinary nature of NLP, real-world applications across unstructured data sources, and current industry solutions. It also addresses key challenges such as ambiguity, context dependency, and multilingualism, as well as ethical considerations like bias and privacy. Through theoretical foundations and hands-on practice, students will learn to apply NLP to drive value, innovation, and automation.. Generative & Agentic AI. Generative AI: This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness.. RETAIL & FMCG. ARTIFICIAL INTELLIGENCE IN RETAIL & CONSUMER GOODS. On this course you’ll be able to understand the bases of a Retail Business Model end-to-end, identifying where Data and AI can create opportunities and then apply your knowledge through a practical approach. Today’s customers have more choices than ever, creating a major challenge for retailers to stand out. Success depends on building a strong value proposition rooted in deep customer knowledge, starting with data and enhanced through AI and ML to attract, satisfy, and build loyalty across the customer journey. As technology accelerates and new competitors emerge, retailers are increasing digital investments. On the operational side, AI-driven demand forecasting and automation are transforming supply chain efficiency. On the customer side, advanced analytics power everything from store layout and product range to dynamic pricing and personalization. Generative AI is already redefining customer experience. Retail remains a human-to-human industry, and this course explores how data and technology can strengthen those connections.. Risk & Fraud Analytics. This course in Risk & Fraud Analytics (RFA) is a business application course that applies Python and various Machine Learning algorithms to real data and real applications in Finance and Banking. It uses case studies to accelerate learning and encourage class participation. Students will receive hands-on training on traditional and advanced Risk and Fraud Analytics through reading and watchable materials, real-world success cases, practical model development, and forward-thinking discussions on how Big Data is reshaping the financial industry.. Industry 4.0. Industry 4.0 is considered the last industrial revolution in history—and in this course, you’ll learn why. Over ten sessions, you will discover the fundamentals of Industry 4.0. This includes visiting the main use cases and typical Industry 4.0 scenarios that you may encounter in real life. You will also learn to analyze and understand how industrial data brings new capabilities to lead this emergent industry paradigm.. Big Data & Artificial Intelligence in Marketing. This course explores how Big Data and Artificial Intelligence (AI) are transforming modern marketing strategies, emphasizing the shift from traditional methods to AI-driven, data-centric approaches. Students will learn to design, implement, and optimize marketing campaigns using tools such as Google Analytics 4 (GA4), advanced attribution models, and machine learning techniques. The course covers digital analytics, user behavior analysis, and AI-enhanced reporting. Students will also explore Marketing Mix Modeling and incrementality measurement using geo experiments and Causal Impact. Additionally, the course integrates Big Data tools including Google Cloud, BigQuery, and machine learning APIs to support advanced marketing analytics. Through real-world applications, students will gain practical experience and the ability to lead innovative, data-driven marketing efforts.. Social Network Analysis. This course introduces Social Network Analysis (SNA) as a powerful tool to uncover hidden patterns in interconnected systems, such as the flow of goods, services, and information among people, teams, and organizations. Beyond social media, SNA is applicable to fields like marketing, sports, fraud analysis, communication, biology, and organizational studies. The course equips students with the theoretical and practical principles, models, techniques, and metrics to analyze networks. Students will learn key SNA concepts, algorithms for node and link ranking, and how to apply them to real-world problems.. Generative & Agentic AI. Generative AI: This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness. Agentic AI: While previous AI assistants focused on making predictions or generating content, we’re now witnessing the emergence of something far more sophisticated: AI agents that can independently perform complex tasks and make decisions on our behalf. This course offers hands-on training in building autonomous AI systems that can think, learn, and act independently to solve real-world business problems. Focusing on the practical and strategic use of Agentic AI in business analytics and data science, students will explore key concepts such as generative AI, prompt engineering, multi-agent systems, and autonomous decision-making to develop AI systems can autonomously execute tasks and interact with other AI agents.. SMART MANUFACTURING & AUTOMATION. Industry 4.0. Industry 4.0 is considered the last industrial revolution in history—and in this course, you’ll learn why. Over ten sessions, you will discover the fundamentals of Industry 4.0. This includes visiting the main use cases and typical Industry 4.0 scenarios that you may encounter in real life. You will also learn to analyze and understand how industrial data brings new capabilities to lead this emergent industry paradigm.. DATA GOVERNANCE. The objective of the course is to provide students with necessary skills that will allow them to efficiently manage the life cycle of an organization's data.All organizations regardless of size, business nature or purpose, grew data’s importance, elevating its status to a strategic that every organization must manage accordingly. Managing data is a complex activity including many aspects, both in business and IT areas. On the other hand, being successful is only feasible when data is properly managed, and Data Governance is the heart of Data Management.. Big Data & Artificial Intelligence in Marketing. This course explores how Big Data and Artificial Intelligence (AI) are transforming modern marketing strategies, emphasizing the shift from traditional methods to AI-driven, data-centric approaches. Students will learn to design, implement, and optimize marketing campaigns using tools such as Google Analytics 4 (GA4), advanced attribution models, and machine learning techniques. The course covers digital analytics, user behavior analysis, and AI-enhanced reporting. Students will also explore Marketing Mix Modeling and incrementality measurement using geo experiments and Causal Impact. Additionally, the course integrates Big Data tools including Google Cloud, BigQuery, and machine learning APIs to support advanced marketing analytics. Through real-world applications, students will gain practical experience and the ability to lead innovative, data-driven marketing efforts.. Natural Language Processing. This course provides a broad overview of Natural Language Processing (NLP), focusing on the automatic understanding of human language and its practical applications. Students will explore the core principles and techniques of NLP, from basic tasks like text preprocessing and sentiment analysis to advanced models such as BERT and ChatGPT. The course emphasizes the interdisciplinary nature of NLP, real-world applications across unstructured data sources, and current industry solutions. It also addresses key challenges such as ambiguity, context dependency, and multilingualism, as well as ethical considerations like bias and privacy. Through theoretical foundations and hands-on practice, students will learn to apply NLP to drive value, innovation, and automation.. Social Network Analysis. This course introduces Social Network Analysis (SNA) as a powerful tool to uncover hidden patterns in interconnected systems, such as the flow of goods, services, and information among people, teams, and organizations. Beyond social media, SNA is applicable to fields like marketing, sports, fraud analysis, communication, biology, and organizational studies. The course equips students with the theoretical and practical principles, models, techniques, and metrics to analyze networks. Students will learn key SNA concepts, algorithms for node and link ranking, and how to apply them to real-world problems.. Generative & Agentic AI. Generative AI: This course offers an in-depth exploration of generative AI, combining technical foundations with ethical considerations. Through real-world case studies and hands-on practice in cloud environments, students will learn to build, fine-tune, and optimize models like GANs, VAEs, and Transformers. Key topics include prompt engineering, responsible AI, and the integration of generative AI with other paradigms like reinforcement learning. Students will critically assess model performance and societal impact, culminating in a final project that demonstrates both technical skill and ethical awareness. Agentic AI: While previous AI assistants focused on making predictions or generating content, we’re now witnessing the emergence of something far more sophisticated: AI agents that can independently perform complex tasks and make decisions on our behalf. This course offers hands-on training in building autonomous AI systems that can think, learn, and act independently to solve real-world business problems. Focusing on the practical and strategic use of Agentic AI in business analytics and data science, students will explore key concepts such as generative AI, prompt engineering, multi-agent systems, and autonomous decision-making to develop AI systems can autonomously execute tasks and interact with other AI agents.. EXCHANGES. Broaden your perspective by spending your Elective Period in term 3 on an International Exchange at one of our world-class partner universities. Over the course of the exchange, Master in Business Analytics and Data Science students will add depth to their academic knowledge, expand their international experience, and widen their professional network. While schools available for exchange vary according to the timing of the Electives Period, this program offers exchange agreements with:. INTERNSHIPS. During the Elective Period, you can apply to do an internship. This option has been created for those who wish to gain specific, real-world experience that will support their career change to another industry, sector, region and/or role. There are two ways you may apply to an internship: The IE School of Science and Technology has a panel of partner companies you can apply to intern with. These companies are very diverse and could also include international internships. Alternatively, you can find an internship by other means, in which case your course coordinator would need to approve it based on the conditions and eligibility criteria.. Electives. In the third term, in addition to the option of pursuing a concentration, you may instead choose from a diverse list of electives, enabling you to tailor your academic experience to your personal and professional goals.. Advanced Data Visualization Algorithmic Trading Artificial Intelligence In Banking Artificial Intelligence In Health Artificial Intelligence In Retail & Consumer Goods Big Data And AI In Marketing Computer Vision Data Analytics In The Cloud Deep Learning Excel For Data Science & Consulting Generative AI Iot & Emerging Technologies Natural Language Processing Quatum Computing For Business Reinforcement Learning & Autonomous Systems Risk & Fraud Analytics Web 3, Blockchain, Cryptocurrencies And Nft. Social Network Analysis Spatial Computing. Augmented (Ar) And Virtual Reality (Vr) Sports Analytics Strategic Technology Consulting Sustainable Technology & Innovation Tech Product Management Deep Tech Venturing And Investment Process Mining & Automation Agentic AI Sales Engineering And Consulting Skills Unmanned Aircraft Systems: Strategy, Entrepreneurship And Technology Robotics Artificial Intelligence In Telecommunications Sports Management: Creating Value With Technology Clinical Research And Development Of Medical Device Technology

CAPSTONE PROJECTS. CAPSTONE PROJECTS. The culmination of your learning experience will be a Capstone Project. In essence, it is a final integrative exercise that will take place over the course of two months. You can select a Venture Lab Business Plan in the area of Data Science and Business Analytics or a Corporate Project with a firm.. CORPORATE PROJECT. The Corporate Project is a consulting mini-project that addresses an organization’s real-world needs. The goal is for students to use the business skills they learned during the programme to make a genuine impact. With the help of a mentor, teams of students collaborate with a company, NGO, startup, or institution to solve a Data Science or Business Analytics challenge. During the Master Electives Period, the students will develop a proposal over the course of two months. The Corporate Project allows students to use their “toolkit” to help solve a real-world problem. The Corporate Project is a course offered as part of the Master in Business Analytics & Data Science program, an alternative to the Venture Project. Some of the companies that have participated in Corporate Projects include: MICROSOFT, BCG, AIRBUS, BULGARI, INDITEX, KPMG, IBM, ALLFUNDS, CAPGEMINI, REPSOL, ZURICH DIGITAL, FITIZENS, FERROVIAL, and NAVANTIA.. VENTURE PROJECT. The Venture Project trains and mentors teams to investigate, validate, and develop and MVP and a pilot, with the target to make it a ready-to-launch start-up venture, in Data Science and/or Business Analytics. It will be created through the Venture Lab. The ultimate goal of the Venture Lab is to finish the programme with an MVP and a fully operative pilot of a product/service in the field of Data Science and Business Analytics. The entire focus is on applying what is learned in the program, gathering enough data and creating an operative tool that will help to easily move from Pre-Launch to Launch activities. The primary objective of a Venture Project is to give teams the opportunity to initiate a ready-to-launch start-up project. This is accomplished through first-hand experience in the world of high impact entrepreneurship. The Venture Project is a course offered as part of the Master in Business Analytics & Data Science programme, alternative to the Corporate Project.. RESEARCH PROJECT. Students who choose the Academic Research Project as their Final Impact Project will be required to demonstrate the application and integration of the knowledge acquired throughout the program through an academic research initiative. Students will explore a specific area of interest related to business analytics and data science in depth. Each student will be assigned a full-time faculty researcher as their academic supervisor. The ultimate goal of this project is to develop a rigorous research study on the selected topic, which must include a well-structured academic paper with a relevant literature review and a robust research methodology.

MAKE THE MOST OF YOUR PROGRAM. CERTIFICATIONS. CERTIFICATION DESCRIPTION. CERTIFICATIONS. This program helps you boost your career through certifications from AWS, Microsoft, and others. These certifications are designed to strengthen your profile and support your progress along your chosen career path: Amazon web services academy. Amazon Web Services Academy. Microsoft learn. AWS Certified Data Engineer. Certified Tableau Data Analyst. Certified Tableau Desktop Specialist. IE Certificate in Foundations of Sustainability.. SUSTAINABILITY CERTIFICATE. In today’s world, organizations are placing sustainability and environmental, social and governance (ESG) concerns at the heart of their businesses. At IE, we offer an optional certificate to help you learn how to tackle these challenges and adopt a sustainability mindset. With the IE Certificate on Foundations of Sustainability, you’ll get prepared to tackle the social, economic and environmental challenges of today’s world in any organization. This certificate can be completed alongside your core studies. In order to earn the certificate, students must obtain ten relevant credits. Students may obtain these credits through eligible courses, electives, and extracurricular activities. Some of the aforementioned may already be included in your program, while others will need to be added. Additionally, there’s one mandatory component, the Online Learning Journey and the Sustainability Datathon. Companies that hace been sponsors:NTT Data, Acciona, Ryanair, EDP Renewables, Repsol,INFOSYS, Acqualia Maximum flexibility has been assured, providing several ways to earn the required number of credits.. MENTORSHIP PROGRAM. MENTORSHIP PROGRAM. The Tech Mentorship Program is an initiative designed to establish a personal and professional relationship of learning and trust between a mentor and a mentee.Students who choose to participate will be matched with a mentor from our global mentors community, which includes over 120 professionals. The program focuses on personalized growth, expanding professional networks, and enhancing technical and professional skills.. INTERNATIONAL EXPERIENCES. BERKELEY IMMERSION WEEK. Berkeley Immersion Week. Fast track your tech career: Berkeley Immersion Week opens doors to Silicon Valley's top companies and minds.. TECH IMMERSION WEEK. Opportunities, challenges and networking. Experience global tech hubs such as Amsterdam, Dublin or Dubai among others, through the Global Immersion Week. This international journey offers the opportunity to visit leading companies, engage with industry professionals, explore diverse cultures, and gain valuable insights into the tech landscape and your future career.. TECH INITIATIVES. Berkeley Immersion Week. Joined by other students from IE School of Science & Technology, you'll participate in a hands-on, one-week program where you'll meet industry leaders from Silicon Valley.. Tech Venture Bootcamp. The Tech Venture Bootcamp is a pioneering 6-day program designed for innovators who are eager to create groundbreaking ventures in their own unique way.. IE SUSTAINABILITY DATATHON. The IE Sustainability Datathon is a data-driven sustainability challenge where students collaborate with an industry partner to analyze real datasets and design innovative solutions to environmental issues.. Rise Europe Alliance. The Rise Europe Alliance, comprising 20 academic and entrepreneurial institutions, shapes the next generation of European talent through diverse perspectives and collaboration, fostering global market success and driving innovation at its annual summit.. Berkeley Startup Semester. As a Global Partner with Berkeley SCET, IE enables students to enroll in the Startup Semester at Berkeley after completing their programs at IE. This full-time program offers a unique opportunity to unleash creativity, gain global experience, and immerse themselves in the heart of Silicon Valley. Guided by top-tier faculty, mentors, entrepreneurs, accelerators, and investors, participants also engage in cultural activities throughout the Bay Area. Students can choose from two tailored tracks to support their entrepreneurial journey: Venture Discovery Track – Ideal for those looking to explore and develop a new venture. Venture Validation Track – Designed for students with an existing project who want to accelerate its growth. The program includes hands-on courses at SCET and concludes with an official certificate from UC Berkeley, equipping students with real-world skills and global credentials in entrepreneurship.. ACADEMIC ESSENTIALS. IMPACT SKILLS ACCELERATOR. In the ever-evolving landscape of data science and AI, the ability to tackle complex challenges, leverage emerging AI tools for productivity and efficiency, and master high-impact skills has become a hallmark of success. As the field continues to advance, the demand for professionals who demonstrate strong critical thinking, solve problems creatively, lead cross-functional teams, and communicate insights with clarity and influence is greater than ever. To develop well-rounded professionals fully aligned with market needs, the following courses are integrated into the academic journey. Prompt Engineering. GitHub Portfolio Management. Communication Skills. Building High-Performance Teams. Diversity Workshop. Critical Thinking. Agile Thinking. Design Thinking. Project Management. Influence and Persuasion.. Career Accelerator Program . The Career Accelerator Program is a comprehensive career development journey designed to support students at every stage of their professional path — from discovering where to start, to focusing their ideas, and putting their career plans into action. It offers resources to help students identify what they want from their future, learn about different career paths, make informed decisions, and successfully take their next steps. The program provides: Key information on a wide variety of sectors, industries, and career paths today. Guidance on making successful applications, preparing for interviews, practicing online assessments, and learning how to build your network. This hands-on program equips students with the tools, confidence, and strategies needed to succeed in today’s dynamic job market.

FORMAT. WHAT’S THE PART-TIME FORMAT LIKE?. This 17-months program is both online and face-to-face—in other words, it’s liquid. We align and combine the best of technology, pedagogy and our world-class faculty, so you can experience multi-layered learning in a multi-faceted environment.. Online periods. Through our virtual campus, you get a challenging and highly interactive educational experience. The well-structured, faculty-led sessions fit into the modern professional’s busy schedule and can be accessed anywhere there is an internet connection. Engage with other global professionals in synchronous and asynchronous sessions including interactive small groups where you work on real-world, industry-based case studies.. LIVE SESSIONS – ON SATURDAYS. You will connect to live video conference sessions engaging with your peers and professors in real time, discussing previously established topics for the week. These sessions are truly an extension of the traditional classroom experience, allowing you to negotiate and collaborate with your classmates in the same space, despite connecting from all over the globe.. ASYNCHRONOUS ONLINE DISCUSSIONS. The forums allow you to participate in the faculty-led asynchronous sessions every week from Monday to Thursday. Professors will moderate these written discussions about chosen topics in order to achieve the learning objectives.. Face-to-face Periods. Get to know your diverse classmates even more through all-day workshops and classes that further develop your soft skills and teamwork. Explore Madrid after class and get to know your peers on a deeper level.. ROUGHLY FIVE WEEKS. Of full-time course work and networking with your peers in the heart of the Spanish capital, spread throughout the duration of the program.. INTERNATIONAL DESTINATION. One week of face-to-face sessions in an international destination.