First

Second

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Build a strong base with core competencies

While the world holds its breath in anticipation of the Age of the Petabyte, we’re getting ready for the Age of the Exabyte. The Data Science programme is the first dedicated BSc programme in the world.

Barcelona

A full-time undergraduate program to build skills and launch your career

Choose the four-year Work & Study option to balance your academic goals with practical work experience. This extended format is designed for students who want to integrate longer periods of employment with their studies, enhancing their resumes and applying classroom theories in real-world business settings as they learn.

Applied Bachelor

Data Science

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Opt for our three-year full-time study to fully immerse yourself in your education. This focused approach allows you to absorb knowledge and develop skills at a consistent pace, effectively preparing you for early opportunities. As part of this programme, you are also required to complete a 12-week internship, integrating practical experience with your academic studies.

Bachelor

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Bangkok

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The Final Capstone Project serves as the culmination of the Data Science Master’s program, allowing students to apply the full range of skills and knowledge they have acquired to solve a real-world problem. Working individually or in teams, students will design and implement data-driven solutions using advanced analytical methods, machine learning models, and visualization tools. Projects may be industry-partnered or research-focused, and they are expected to demonstrate technical depth, business relevance, and clear communication of insights. The capstone provides a platform for students to showcase their readiness for data science roles in industry, academia, or entrepreneurship.

Creativity is the engine of the 21st century. The contemporary world is full of ideas, concepts, products, and people trying to find their way through this maze. Every start-up has to have not only the idea and (future) product but first of all the story to tell both to the potential investors as well as users, now and future, which brings us to the necessity of creativity and storytelling. Whatever you do, you have to have a story. Another intent of this course is bringing together people of different backgrounds and knowledge, to engage them in working towards a common goal in a new way – through writing a piece of fiction and performing it. Uniting those two fields gives students the possibility to try out their abilities in a safe environment as well as cooperate with other students, bringing in new viewpoints and experiences. This is one of the best ways of establishing teams that have innovation at their core. 

Creative Writing, Storytelling, and Creative Performing

This course is designed to equip participants with the necessary skills to cultivate long-lasting leadership influence and make a meaningful impact, even in the face of change, crises, and criticism. While technical or "hard" skills may secure high-potential individuals top positions, this course focuses on the development of "real skills" that enable leaders to build, sustain, and thrive in their leadership roles. The course will empower future leaders with the transformative Three Pillars of Leadership: Awareness, Behavior, and Visibility to take their leadership to the next level.

Leadership

This course is a business English course for business people. It consists of authentic topics of great interest to everyone involved in or studying international business. The course reflects the latest trends in the business world. If you are into business, the course will greatly improve your ability to communicate in English in a wide range of situations. If you are a student of business, the course will develop the communication skills you need to succeed in a professional environment and will broaden your knowledge of the business world. Everybody studying this course will become more fluent and confident in using the language of business in a variety of contexts.

English Business Communication

“You can read a thousand books on how to swim in the ocean. But you have to experience and practice; otherwise, you sink. If you take this course, your pitching and public speaking will never be the same again! You will learn how to properly structure your pitch for investors, and you could easily deliver an elevator pitch. You will learn the principles of managing anxiety and fear from the audience. You will understand how to work with difficult audiences, their objections, and complex questions. You will learn to confidently control your body, voice, and gestures and hold yourself on stage while enjoying what is happening. You will know how to inspire the audience, which will listen to you by holding a breath!

Pitching to Investors

Product Analytics Fundamentals is a comprehensive course that covers the essential elements of analytics, including applied statistics, programming skills, AB-testing, visualization, and understanding data. This course is designed to provide students with a solid foundation in analytics so they can make data-driven decisions. Students will learn how to collect and analyze data using statistical methods, how to extract insights from data, how to conduct AB-testing to optimize product performance, and how to visualize data to communicate insights effectively.

Product Analytics Fundamentals

This course has two parts: (1) building and launching a user-facing software product with a special emphasis on understanding user needs, and (2) applying data-driven product development techniques to iteratively improve the product. Students will learn how to transform an idea into software requirements through user research, prototyping, and usability tests, and then they will proceed to launch the MVP version of the product. The students will apply an iterative, data-driven approach to developing a product, integrate event analytics, and run controlled experiments.

Technical Product Management

Building great tech requires more than great product knowledge and management - it requires a deep understanding of Agile ways of working. “Being agile” is so much more than dev team practices, it is being mentally flexible and highly adaptable to constant change. In this class, students will learn how to navigate the jargon and use agile concepts to achieve terrific results with teams and organizations. In addition to learning about the frameworks scrum and kanban, we will review the philosophical principles behind why these frameworks can be useful, how to modify or recombine them, and how to be sensitive to the human psychology that makes it all work. Through a combination of lectures, small-group work, learning games, workshops, and projects (both structured and self-directed), students will learn to avoid the pitfalls and leverage Agile for their product and business success.

Agile Product Development

Understanding Machine Learning requires fundamental knowledge in mathematical areas such as linear algebra, calculus, optimization, probability and statistics. The Math Refresher course focuses, through practical examples and assignments, on revising the necessary topics that will allow students to join future Machine Learning courses and gain thorough knowledge about modern Artificial Intelligence.

Math Refresher for Masters

The course covers basic Python methods for data analysis: pandas, numpy, scipy, and sklearn, along with advanced techniques for their application. We’ll also review basic integrations of Python with external libraries like xgboost, tensorflow, and pytorch, along with data wrangling and some hyperparameter optimisation methods. Jupyter notebook usage and tricks will also be given as an organic part of the course. At the end of the module, everyone is expected to be ready to work with ssh and git as long as they come up with a simple data wrangling system.

Python Refresher for Masters 

This advanced course aims to give students knowledge about statistical analysis methods with a focus on applications. We’ll consider various data science tasks that require statistics, study the taxonomy of statistical methods, learn their requirements and limitations, and apply them to different real-world problems and datasets using R.

Statistical Data Analysis

This course aims to introduce students to the contemporary state of Machine Learning and Artificial Intelligence. It combines theoretical foundations of Machine Learning algorithms with comprehensive practical assignments. The course covers materials from classical algorithms to Deep Learning approaches and recent achievements in the field of Artificial Intelligence. This course is accompanied by Deep Learning in Applications course, which brings the most recent achievements in the field and their applications. Programming assignments will be implemented in Python 3. PyTorch framework will be used for Deep Learning practice.

Masters Machine Learning

State-of-the-art approaches in different domains of artificial intelligence are based on deep learning techniques (e.g. computer vision, natural language processing, reinforcement learning, etc.). Deep neural architectures show great potential and promise even better results, so now is definitely the time to explore this field. In this course, we will start from the basics and rapidly dive into the latest results in deep learning, focusing on the NLP and RL domains. This course focuses both on practical skills and theoretical background to provide the students with thorough theoretical knowledge and the ability to work on their own in the deep learning area. This course accompanies the Master’s Machine Learning course. Programming assignments will be implemented in Python 3. The PyTorch framework will be used for deep learning practice.

Deep Learning in Applications

All contemporary software platforms, whether developed by large corporations (Facebook, Google, OpenAI, etc.) or small businesses, rely on the use of databases or data storage. The foundation of this course is the notion that data storage is an answer or a solution to a problem rather than a technology in and of itself. During this course, we will study what problems modern software can solve with data storage. We will study the whole spectrum of existing data storages, such as classical RDBMS, key-value storages, NOSQL, document storages, column storages, OLAP, vector databases, embedded and serverless databases, and their weak and strong points. Students will learn to understand how to identify requirements for the data storages in a given software system and how to wisely choose a particular data storage (or multiple storages), taking into consideration both business requirements and the chosen software architecture (monolithic, microservice, etc.). We will study all concepts and mental models needed to understand data storage, wisely choose them, and embed them into software - manually, as a manager, or using an LLM, such as ChatGPT.

Data Storages

Machine Learning is a powerful tool that is widely used today and is specified in computer science education, while Statistics and Probability are traditionally studied in mathematical departments.  Nevertheless, there is a strong relationship between these theories, and it is desirable to be aware of both general principles and differences in approaches. Indeed, machine learning uses mathematical and/or statistical models to gain an overview of the data to make predictions. The novel contributions in data mining are mostly informal and usually linked with the Bayesian point of view on statistics. But, probability theory itself is more than just Kolmogorov's axiomatic approach or Bayesian reasoning. This course tries to show the existing diversity of approaches while staying within classical Kolmogorov's probability theory. The necessary classical theoretical material will be explained as well. We aim to consider several paradoxical situations that arise in practice. Most examples will be taken from natural sciences and simple situations in data analysis. The outcome is expected to be practical training in understanding the popular statistical models and the ability to critically read a professional text.

Probability and Statistics: Theory and Implementation

Deep Learning, i.e., training multilayered neural architectures, was one of the oldest tools in machine learning but has revolutionized the industry over the last decade. In this course, we begin with the fundamentals of deep learning and then proceed to modern architectures related to basic computer vision problems: image classification, object detection, segmentation, and others. Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures, while at the same time staying focused and not going into too wide of a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localization and mapping, and scene understanding, which are all key problems for creating industrial and home robots.

Neural Networks and Computer Vision

Nowadays, machine learning technologies are widely used in various fields such as retail, mass media, PR and marketing, banking, telecommunications, manufacturing, science and many others. Even though it is very important to use the appropriate methods in every project, often the choice of a particular machine learning algorithm does not play a key role. Most of the time, the most important factors are the appropriate formulation of the problem from the business point of view, the correct mathematical formalization of the problem, and an accurate assessment of the potential economic effect. In the course, we will learn the structure and the lifecycle of the machine learning-based project and cover topics ranging from the problem statement definition to the final model quality assessment, as well as an estimation of the economic effect.

Industrial Machine Learning

In three weeks, we're going to learn the principles of ML System Design and apply it to build an application that we and other people can use. This will include some amount of software development, product thinking, and machine learning. ML will be the backbone of our product, SE will be the means to make it work, and product thinking will lead us through. We’ll start with an idea, outline user scenarios an app should fulfil, proceed with system design, and dive into implementation. We’ll use the app ourselves, collect and analyse our feedback, work on improving the user experience, and finally publish this to the outside world to get real feedback and reflect on our experience. This class will help you understand how complex ML systems are built and will be part of your portfolio that you can showcase and reason about.

ML System Design 

This three-week hands-on course provides a jumpstart into design and development of modern MLOps platforms. From initial project scoping to exploratory analysis and data engineering, from feature engineering to model training, to ML pipelines and CI/CD automation, productization, monitoring, and operations. You are going to create an ML project in Amazon SageMaker Studio and go through all stages of development such as data exploration, interactive experimentation, setting up MLOps pipelines, and finally deliver the project into production. You’ll learn how to work with a feature store, model registry, pipelines, model and data monitor, and CI/CD projects. Through this course, you will work with Amazon SageMaker to get you inspired to implement your own MLOps solution. The course provides recommended patterns and practical architecture blueprints for real-world ML projects. The course gives you an overview of the industry-standard ML platforms and MLOps products, like MLflow, Apache Airflow, H20, DataIKU. As the course's main deliverable, you’re required to implement an end-to-end ML project with the main components of MLOps, such as reproducible ML pipelines, scalable data processing and model training, model registry, experiment tracking, observability, and event-driven workflows.

Development of Modern MLOps Platforms

During this course, students will master and sharpen their knowledge of basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce, Hive, Spark (especially real-time instruments like Spark Streaming and Kafka Streams), and NoSQL frameworks like HBase and Cassandra. Of particular interest for the course will be the subject of efficient data warehousing using Hive and Spark. Under the teacher’s supervision, students will study the intricacies of the system’s internals and their applications. They will also learn distributed file systems, the purpose of their existence, and the ways to apply them. The learners will also practice using the MapReduce and Spark frameworks, a workhorse for many modern Big Data applications. The key element of this course is to apply knowledge into practice, and to process texts and solve sample business cases. The participants will deal with Spark, the next-generation computational framework, from its basic concepts up to advanced applications made to squeeze maximum performance. Finally, they will get acquainted with the modern NoSQL frameworks like HBase and Cassandra.

Big Data Technology

This course is about modeling real-world problems with optimization and numerical linear algebra tools and how to solve them. We will discuss the most important algorithms from NLA, classical convex optimization, and stochastic optimization methods which are crucial for efficient training of large neural networks. The selected topics will be discussed from both theoretical and practical perspectives. In particular, practice sessions will include introducing the standard open-source packages for solving considered problems. Every block of the course will be equipped with multiple practical demos and home assignments that help students go deeper into the discussed topics.  

Optimization Methods

This course focuses on giving students the skills required to participate and excel in real-world ML tasks by practicing in ML competitions. The syllabus is designed to teach students how to solve ML problems going step-by-step. Students will create a framework for their solutions and test the problem by creating simple no-ML solutions, by preparing Python scripts, building Docker images, or deploying models. The course covers several topics, such as ML metrics understanding, optimization, EDA, feature generation, ensembling models, and debugging solutions.

Practicing DS Skills in ML Competitions

Because the world is overwhelmed with information and people need automatic systems to filter and rank relevant information, recommendation systems are widespread technology today. We see them in video hosting services, social networks, online shops, etc. Recommendation systems are here to solve this task and save people’s time and effort on information retrieval. This course aims to introduce students to a full spectrum of recommendation systems tool belts, starting from simple user-based collaborative filtering to sequential recommendations based on transformers. Materials include both a theoretical part (matrix decompositions, SLIM) and a practical part addressing production techniques such as knn indexes, embedding quantization, and scalable item-to-item service.

Recommendation Systems

In our data-driven world, the technological revolution has simplified the generation and collection of data across various aspects of our personal, social, and professional lives. The interpretation of this data empowers us to make informed decisions, generate ideas, recognize trends, and either validate or challenge our beliefs. This course aims to equip you with effective techniques for visualizing data, enabling you to comprehend and effectively communicate your findings to others, whether through a research paper, a corporate meeting, or a landing webpage. Additionally, we will cultivate a critical approach to data, enabling us to identify both its limitations and strengths.

Data: Interpretation, Visualization & Presentation

Large Language Models

The course gives a 360° view of time series analysis methods and real-world problems. The main focus is on forecasting, with a range of solutions from simple averaging to NN-based sequential models. The course also covers unsupervised learning problems such as clustering and anomaly detection, as well as causal inference on time series data.

Time Series Analysis

Job Interview in AI & Data Analysis

This course focuses on key and in-depth algorithms and data structures that form a modern computer specialist’s toolkit. The computational complexity of algorithms and their comparative analysis will also be discussed. Students will be extensively trained in implementing data structures and algorithms on many problems reducible to the discussed data structures and techniques. The programs will be tested against carefully prepared test cases using an automated testing system. 

Advanced Algorithms and Data Structures

Clouds are an essential part of today’s life. Therefore, cloud computing skills are in high demand right now. The course curriculum includes the foundations of clouds: virtualisation and fundamental principles of distributed systems. In this module, different aspects of the design and implementation of distributed systems are explained. We will deep-dive into technologies like: google file system, spanner, dynamo DB, s3, and consensus algorithms like Raft and Paxos. The course also covers public clouds (AWS, GCP and Azure), and cost analysis of cloud solutions. Throughout the course, we will design and deploy cloud-native applications.

Distributed Systems & Clouds

Cloud technologies have become increasingly prevalent in the business world, providing companies with the opportunity to deploy products and infrastructure with greater speed and scalability than ever before. However, clouds bring new security risks, threats, and tools that organizations must be prepared to tackle. Why is cloud security so different from “in-house” infrastructure and product security? How does cloud migration impact security risks? In this class, we get to know cloud technologies from a security point of view, and students will learn how to assess security risks and how to protect our products and infrastructure in clouds. 

Cloud Security

In this course, students will learn the basic principles of technological entrepreneurship. Students will be able to create a Minimal Viable Prototype (MVP) by applying principles of paper prototyping, create and implement a customer development pipeline and  evaluate a product’s market fit and unit economics. They will will also learn how to create a pitch deck for their project from scratch, evaluate the quality of the early-stage venture capital, and implement a fund-raising plan. By the end of the course, students will have a good understanding of the overall properties of venture capital markets.

Entrepreneurship for Engineers

The course represents a deep dive into concepts and frameworks introduced during orientation. It is designed to teach students the process of creating a new venture. It introduces its participants to the concepts, challenges, and tools needed to create a successful new venture. Business model design & validation frameworks are studied and applied to the teams’ startups.

From Zero to Hero

A full-time graduate program to advance expertise and open career opportunities

An innovative format that blends academic learning with professional work experience. Study for the first six months, spend a year in a work placement, and return to complete the final six months of coursework. Perfect for those who wish to balance education with hands-on experience, this structure enhances practical skills and career development.

Applied Master

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A focused one-year program designed for those who want to fully immerse themselves in academic study. This format builds strong theoretical and practical expertise through an intensive and comprehensive curriculum.

Master

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Launch your Dream Career Early with Harbour.Space Foundation Course

Foundation

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Single courses from any program, offering flexible, self-paced learning

Single Courses

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Every career looks different: it depends on individual aspirations as a
well as changes in the industry. We've selected some stories to
illustrate and celebrate the diversity of the various career paths.

This is a typical starter role for data
science BSc graduates. The position
requires strong knowledge of
mathematics with an emphasis on
statistics and machine learning as well
as on concepts related to
programming and databases.
As part of a team, junior data scientists
begin gaining first hand experience
with industrial data analysis while
learning from more senior colleagues.

Junior Data Scientist

Data scientists combine a
mathematical background and
practical experience with domain
expertise which enables them to
conduct advanced data analysis
necessary to make predictions that
facilitate good business decisions. The
job requires an understanding of both
mathematics and business objectives.
Data scientists need to be technical
experts with the competencies
necessary to handle large volumes of
diverse data as well as possess strong
programming skills.

Data Scientist

A senior data scientist becomes a link
between the core of the business
management and the entire analysis
team. The role requires the ability to
communicate effectively, in order to
both grasp business goals and to
communicate the insights brought to
them by the analysts. Senior data
scientists also direct relevant
programming and application-building
projects, as well as the formulation of
experiment design and they have a
leading role within a data science
team.

Senior Data Scientist

A principal data scientist is a data
visionary who finds ways to answer
business questions by analysing
available data within the enterprise
and on the internet. The job often
requires the creation of new methods
of analysis and working at the
intersection between industry and
academia.

Principal Data Scientist

A chief data officer is a business leader
who handles responsibilities at the
intersection of data analysis,
information technology and business
strategy. The CDO determines the
long- term direction for controlling
risks and generating value from
information governance as well as
data management and analysis. Most
importantly, the CDO is an agent of
change uncovering new levels of
efficiency by reporting in-depth
insights based on knowledge
harvested from data.

Chief Data Officer

CAREER PATH

Combine bachelor education with work experience

Combine graduate education with industry expertise

Sergey Nikolenko

Archontis Giannakidis

Evgeniy Riabenko

Alexey Dral

Pavel Klemenkov

Leah Isakov

Mikhail Shupletsov

Deepa Gupta

Attila Sali

Emeli Dral

Victor Kantor

Pavel Scherbinin

Sergey Khoroshenkikh

Radoslav Neychev

Timo Koski

Konstantin Mertsalov

Alexey Davydov

Anastasia Ianina

Maxim Musin

Andrey Khokhlov

Anastasia Lubennikova

Vladislav Goncharenko

Ana Milovanovic

Iurii Efimov

Ivan Provilkov

Nikolay Karpachev

Oleg Ivchenko

Nikolay Golov

Julia Ivanova

Ilia Sklonin

Juan Galeano

Irina Rudenko

Mikhail Anukhin

Pavel Marvin

Ivan Yamshchikov

Valery Marchenkov

Yevgeniy Ilyin

Nikita Fedkin

Alexander Guschin

Andrei Nartsev

Murtadha Al-Tameemi

Sasha Shapoval

Alexandra Sumarokova

Anna Rudenko

Aleksei Shabanov

Alina Samokhina

Nataliia Kozlovskaia

Michael Diskin

Anna Aksenova

Mikhail Rozhkov

Mohammad Sanaee

Parit (Wit) Sripakdeevong

Olga Filippova

Polina Proskura

Evgenii Nazarov

Timofey Vilkov

Nikolay Taran

Stanislav Don

Nikita Vasiliev

Maxim Novikov

Data Science - programme about big data in Barcelona - Harbour.Space

Ekaterina Podruzhko

Here, you learn not only from amazing teachers but also from your classmates. It's rare to find a place with so many highly motivated individuals eager to share their experiences. Harbour.Space brings together people from diverse backgrounds, helping you acquire different skills and perspectives, and making you a better overall professional.

Ariel Cruz Cruz

Harbour.Space changed my life for the better, opening doors to endless possibilities. It empowered me to embrace growth and learning, not only from amazing teachers but also from my classmates.

Aleksandra Medvedeva

Harbour.Space inspired me and equipped me with the knowledge and opportunities to put it into practice. This magical cycle made me believe in myself.

Mohammed (Jim) Sanae

This exposure to diverse perspectives and global experiences at Harbour.Space fosters cross-cultural learning, which is preparing me to thrive in an increasingly globalised and interconnected world.