Master in Business Analytics and Data Science

This multimedia self-assessment tool has been designed to allow you to prepare yourself and achieve the basic quantitative concepts necessary for a successful beginning in the MBDS Program.

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.

Students in this course will be introduced to learning the powerful and accessible language of Python, widely used in the data science community. Python, known for its ease of use, will be one of the main languages that students will be using in this program. It is supported by specialized packages like NumPy, SciPy, Pandas, Matplotlib, and Scikit-learn, making it versatile for scientific computing, data manipulation, graphics, and machine learning.

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 is the language of the data, widely used in every company and one of the most used tools to do Analytics. 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 that will help you a lot during your Master.

Git Hub is an online hosting service that became the standard in many areas regarding software development, content repository and version controlling. Many open-source software projects are hosted and developed there.

Every year we identify the need of assisting students from different backgrounds in mastering basic concepts that are essential for their Professional Development. This course will introduce students into the essential components of a job application package, while guiding them through the interview process. Making interviews in a tech environment is not easy, so the sooner we understand the steps to take and the skills to develop, the more probably we will succeed in an interview process.

Part one will introduce you to some of the essential machine learning algorithms commonly used for data science applications and predictive analytics. Specifically, you will review cluster, association and regression analysis (standard linear and logit).

This course provides you with a working methodology and strong knowledge base for using statistical and mathematical tools in data analysis.

This course introduces you to the rest of the program and gives you a “big picture” perspective on Big Data & Data Science. Find out how it’s going to transform the world and discover the work of data scientists—recently proclaimed by Harvard Business Review as “the sexiest job of the 21st century.

By the end of this course, you will have a firm grasp of big data enterprise architectures based on the Hadoop framework and its ecosystem, which is used by many modern companies.

This course will cover aspects of both architecture and application design and operation. The subject is grounded in learning about NoSQL technologies, which you will come to understand better as you gain experience with databases. You will begin with the modular composition of Spark, to be explored further with both practical activities and theoretical knowledge. You will later build on this knowledge as we explore the difference between SQL-based models and other non-relational databases, such as document-based or key/value-based databases.

Students will also study how cutting-edge companies are adopting trends due to evolution from big data and Cloud technologies.

The course introduces business intelligence and data warehousing: the first steps to transform a company into a data-driven organization.

In this course, you will learn about the logical and physical design of a database. You will also learn how to use SQL (Structured Query Language) to create tables that store business information and how to populate, manipulate and query that information using SQL.

This course offers you a general overview of the Python programming language, coupled with specific uses for machine learning.

Python is a clear and powerful programming language comparable to Perl, Ruby, Scheme, or Java. Specific packages make Python usable for data analysis, including NumPy and SciPy scientific computing applications, or pandas for data manipulation. With Matplotlib, you’ll learn how to produce and animate graphics, and use Scikit-learn to dive into machine learning and predictive data analysis.

This course will provide you with a working methodology and strong knowledge base to use forecasting models and econometrics techniques in business and economics. You will also learn to identify and effectively use one of the most well-known families of forecasting linear models.

By the end of this course, you will have a firm grasp of big data enterprise architectures based on the Hadoop framework and its ecosystem, which is used by many modern companies.

This course will cover aspects of both architecture and application design and operation. The subject is grounded in learning about NoSQL technologies, which you will come to understand better as you gain experience with databases. You will begin with the modular composition of Spark, to be explored further with both practical activities and theoretical knowledge. You will later build on this knowledge as we explore the difference between SQL-based models and other non-relational databases, such as document-based or key/value-based databases.

Students will also study how cutting-edge companies are adopting trends due to evolution from big data and Cloud technologies.

This course provides an overview of the analytical techniques widely used today for marketing. It also focuses on the marketing and analytics ecosystem that companies use. This course will familiarize you with real issues by studying distinct real-life situations, working together to find solutions. This course will build your intuition, allowing you to develop your knowledge to collect, analyze and activate data for marketing, with a particular focus on digital marketing.

Machine learning is the science of giving computers the ability to learn without being explicitly programmed. With strong connections to computational learning theory (Artificial Intelligence) and statistics, machine learning studies the use of mathematical models that can learn from, and make predictions on, data. As a result, machine learning algorithms are able to make predictions or decisions about future data without the need to reprogram.

This course offers you a general overview of the Python programming language, coupled with specific uses for machine learning.

This course will uncover the objectives, methodologies, tools and principles of effective data visuals. You will gain a deep understanding of the broad spectrum of data visualization applications and present data in a hands-on manner.

This will be coupled with learning the key principles of data visualization to help develop a critical mindset.

During this course, you will come to understand the role of data science in health and its application in current and future healthcare scenarios. You will focus on many different topics including technology, analytics, decision-making and business (from a beginner perspective) in a rich data context. Over eight sessions, you will explore theoretical and practical scenarios from several stakeholder perspectives, such as patient, healthcare provider, entrepreneur and data scientist.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

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.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

During ten sessions, you will gain an in-depth perspective on the most impactful analytics use cases for the financial services industry. You will leverage R as the base tool to explore the machine learning methods underlying the use cases. However, the focus of the course lies in understanding and solving problems in the day-to-day life of a senior financial industry manager, rather than in-depth study of machine learning algorithms.

• You will participate in forward-thinking discussions on exploiting big data to improve industry fundamentals and sustain its current core competitive advantages.
• You will also explore your curiosity by developing new use cases to reshape an industry under siege of digital new entrants.
• You will delve into the development of analytical models by leveraging real-life bank data.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

Risk & Fraud Analytics is an advanced analytics course that uses Python to apply various machine learning algorithms to four real-life data applications in the Finance and Banking industry. Following the hands-on approach of IE Business School, this course uses case studies to facilitate applied understanding and encourage class participation.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course will allow you to describe MLOPS standard practices and design a framework for the development, deployment and monitoring of machine learning models that comply with industry standards. You will also study the basics of the ML flow tool for tracking, registering and deploying models.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This course will introduce students to basic concepts of algorithmic trading, with the focus on programming robust and automated trading strategies. Gain firsthand knowledge in everything from connecting scripts with an online trading brokerage—including the placement and query of stock orders—up to using basic Machine Learning in the process.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course will allow you to describe MLOPS standard practices and design a framework for the development, deployment and monitoring of machine learning models that comply with industry standards. You will also study the basics of the ML flow tool for tracking, registering and deploying models.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction. 

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

This course will enable you to identify, frame and develop analytics opportunities in the retail and consumer goods sector. As a result, you will be able to successfully apply your knowledge to real-life business situations in your career.

This course provides students with a basic understanding on pricing and revenue optimization, learning how to use analytical techniques to set prices in VUCA environments, how to model pricing decision processes and how to produce reports visualizing insights to create value to the different stakeholders.

Risk & Fraud Analytics is an advanced analytics course that uses Python to apply various machine learning algorithms to four real-life data applications in the Finance and Banking industry. Following the hands-on approach of IE University, this course uses case studies to facilitate applied understanding and encourage class participation.

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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

Over ten sessions, 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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

The Quantitative Methods Pre-Program is designed to give you the basic knowledge needed for the Master in Business Analytics & Data Science Part-Time. The fundamentals of this research strategy emphasize objective measurement and statistical or mathematical analysis of data. Using quantitative methods, you will learn to collect data through questionnaires or surveys in order to determine relationships between variables within a given population.

Python is a clear and powerful programming language comparable to Perl, Ruby, Scheme, or Java. Specific packages make Python usable for data analysis, including NumPy and SciPy scientific computing applications, or pandas for data manipulation. With Matplotlib, you’ll learn how to produce and animate graphics, and use Scikit-learn to dive into machine learning and predictive data analysis.

Linux is a vital operating system that all big data technologies run under. It’s similar to other operating systems like Windows and macOS, so even students with zero Linux experience will quickly get the hang of its capabilities. Knowledge of the most basic Linux commands provides an important basis for later elements of the program, such as interacting with the Hadoop software library through command.

SQL is the language of data, widely used by companies and one of the most-used tools in analytics. During the Master’s in Business Analytics & Data Science, you will learn the SQL functions from scratch, as well as the essential skills for managing data in a relational database management system. The SQL Pre-Program provides a necessary head start to reach advanced SQL proficiency later in the program.

This course is the first of three machine learning courses. Part one will introduce you to some of the essential machine learning algorithms commonly used for data science applications and predictive analytics. Specifically, you will review cluster, association and regression analysis (standard linear and logit).

This course provides you with a working methodology and strong knowledge base for using statistical and mathematical tools in data analysis.

This course introduces you to the rest of the program and gives you a “big picture” perspective on Big Data & Analytics. Find out how it’s going to transform the world and discover the work of data scientists—recently proclaimed by Harvard Business Review as “the sexiest job of the 21st century.”

By the end of this course, you will have a firm grasp of big data enterprise architectures based on the Hadoop framework and its ecosystem, which is used by many modern companies.

This course will cover aspects of both architecture and application design and operation. The subject is grounded in learning about NoSQL technologies, which you will come to understand better as you gain experience with databases. You will begin with the modular composition of Spark, to be explored further with both practical activities and theoretical knowledge. You will later build on this knowledge as we explore the difference between SQL-based models and other non-relational databases, such as document-based or key/value-based databases.

Students will also study how cutting-edge companies are adopting trends due to evolution from big data and Cloud technologies.

The course introduces business intelligence and data warehousing: the first steps to transform a company into a data-driven organization. In this course, you will learn about the logical and physical design of a database. You will also learn how to use SQL (Structured Query Language) to create tables that store business information and how to populate, manipulate and query that information using SQL.

This course offers you a general overview of the Python programming language, coupled with specific uses for machine learning.

Python is a clear and powerful programming language comparable to Perl, Ruby, Scheme, or Java. Specific packages make Python usable for data analysis, including NumPy and SciPy scientific computing applications, or pandas for data manipulation. With Matplotlib, you’ll learn how to produce and animate graphics, and use Scikit-learn to dive into machine learning and predictive data analysis.

This course will provide you with a working methodology and strong knowledge base to use forecasting models and econometrics techniques in business and economics. You will also learn to identify and effectively use one of the most well-known families of forecasting linear models.

By the end of this course, you will have a firm grasp of big data enterprise architectures based on the Hadoop framework and its ecosystem, which is used by many modern companies.

This course will cover aspects of both architecture and application design and operation. The subject is grounded in learning about NoSQL technologies, which you will come to understand better as you gain experience with databases. You will begin with the modular composition of Spark, to be explored further with both practical activities and theoretical knowledge. You will later build on this knowledge as we explore the difference between SQL-based models and other non-relational databases, such as document-based or key/value-based databases.

Students will also study how cutting-edge companies are adopting trends due to evolution from big data and Cloud technologies.

This course provides an overview of the analytical techniques widely used today for marketing. It also focuses on the marketing and analytics ecosystem that companies use. This course will familiarize you with real issues by studying distinct real-life situations, working together to find solutions. This course will build your intuition, allowing you to develop your knowledge to collect, analyze and activate data for marketing, with a particular focus on digital marketing.

Machine learning is the science of giving computers the ability to learn without being explicitly programmed. With strong connections to computational learning theory (Artificial Intelligence) and statistics, machine learning studies the use of mathematical models that can learn from, and make predictions on, data. As a result, machine learning algorithms are able to make predictions or decisions about future data without the need to reprogram.

This course offers you a general overview of the Python programming language, coupled with specific uses for machine learning.

This course will uncover the objectives, methodologies, tools and principles of effective data visuals. You will gain a deep understanding of the broad spectrum of data visualization applications and present data in a hands-on manner. This will be coupled with learning the key principles of data visualization to help develop a critical mindset.

During this course, you will come to understand the role of data science in health and its application in current and future healthcare scenarios. You will focus on many different topics including technology, analytics, decision-making and business (from a beginner perspective) in a rich data context. Over eight sessions, you will explore theoretical and practical scenarios from several stakeholder perspectives, such as patient, healthcare provider, entrepreneur and data scientist.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

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.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

During ten sessions, you will gain an in-depth perspective on the most impactful analytics use cases for the financial services industry. You will leverage R as the base tool to explore the machine learning methods underlying the use cases. However, the focus of the course lies in understanding and solving problems in the day-to-day life of a senior financial industry manager, rather than in-depth study of machine learning algorithms.

• You will participate in forward-thinking discussions on exploiting big data to improve industry fundamentals and sustain its current core competitive advantages.
• You will also explore your curiosity by developing new use cases to reshape an industry under siege of digital new entrants.
• You will delve into the development of analytical models by leveraging real-life bank data.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

Risk & Fraud Analytics is an advanced analytics course that uses Python to apply various machine learning algorithms to four real-life data applications in the Finance and Banking industry. Following the hands-on approach of IE Business School, this course uses case studies to facilitate applied understanding and encourage class participation.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course will allow you to describe MLOPS standard practices and design a framework for the development, deployment and monitoring of machine learning models that comply with industry standards. You will also study the basics of the ML flow tool for tracking, registering and deploying models.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This course will introduce students to basic concepts of algorithmic trading, with the focus on programming robust and automated trading strategies. Gain firsthand knowledge in everything from connecting scripts with an online trading brokerage—including the placement and query of stock orders—up to using basic Machine Learning in the process.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course will allow you to describe MLOPS standard practices and design a framework for the development, deployment and monitoring of machine learning models that comply with industry standards. You will also study the basics of the ML flow tool for tracking, registering and deploying models.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction. 

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

This course will enable you to identify, frame and develop analytics opportunities in the retail and consumer goods sector. As a result, you will be able to successfully apply your knowledge to real-life business situations in your career.

This course provides students with a basic understanding on pricing and revenue optimization, learning how to use analytical techniques to set prices in VUCA environments, how to model pricing decision processes and how to produce reports visualizing insights to create value to the different stakeholders.

Risk & Fraud Analytics is an advanced analytics course that uses Python to apply various machine learning algorithms to four real-life data applications in the Finance and Banking industry. Following the hands-on approach of IE Business School, this course uses case studies to facilitate applied understanding and encourage class participation.

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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is an introduction to the implementation of computer vision systems, including image processing and camera calibration, detection and reconstruction.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.

Over ten sessions, 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.

It is well-known that analytics relies on quality data. Therefore, the first step towards successful analytics is to understand, manage and protect existing company data. Data governance allows companies to understand what data they have, document it for effective and appropriate use, understand the quality of the data, and establish business rules for the data.

This course allows you to discover what deep learning means, and understand why it has changed the cutting-edge of machine learning including tasks such as speech recognition, computer vision or image recognition. In this course, you can expect to:

• Consolidate machine learning fundamentals (i.e. definition, concept, ingredients and applications).
• Learn about artificial neural networks and what they share with the human brain.
• Learn about the different types of artificial neural networks and how they are applied for machine learning.

This course acts as an introduction to Natural Language Processing (NLP). It focuses on practical solutions with the resources available and their applicability to IT products. You will also cover the main techniques used for parsing and extracting meaning from English texts, which several representative projects use. Finally, you will explore the combination of NLP and big data, discovering how technologies such as translation and marketing analysis can benefit from NLP.

This course aims to uncover the main theoretical and practical principles, techniques, models, and metrics for analyzing social networks. You will learn the key concepts of Social Network Analysis (SNA), some existing algorithms for node and link ranking, and how to apply them to real-world problems, such as influencer detection. You will apply techniques for analyzing information diffusion in social networks and community detection.

Reinforcement Learning (RL) is an area of machine learning that covers systems with complete agents interacting with stochastic environments. This means allowing computers to determine the best actions to achieve a goal in a dynamic environment. You will study the past, present and future of reinforcement learning.

In this course, students will learn how to automate processes using technologies to build, deploy and manage software and/or robots, while interacting with digital systems and software.

This subject is focused on techniques relating to the fields of data science and process management to support operational process analysis through event logs. The goal of process mining is to turn event data into insights and actions.

This subject focuses on the fundamentals of developing metaverse, VR and AR experiences for the next generation of technologies. Dive deep into the basic elements, their architectures and the main devices for virtual and augmented reality systems.

This course is perfectly suited for students willing to learn the technological challenges linked to decentralization by exploring the fundamentals and applications of blockchain technology, distributed ledgers, smart contracts, cryptocurrencies and Decentralized Finances.

This course provides students with a basic understanding of pricing and revenue optimization. Learn how to use analytical techniques to set prices in VUCA environments, model pricing decision processes, and produce reports visualizing insights to create value for different stakeholders.

In this subject, students will understand what it takes to rationalize a vision into a product; how to use different cutting-edge tools to make it; and how to become a product owner within any organization.

This subject helps students acquire a data-driven approach to managing people by exploring the state-of-the-art analytical techniques used to recruit, evaluate performance, hire and promote, and design jobs or compensation. Master the art of making data-based decisions on talent selection and development.