Earn a professional data science master’s degree (60 ECTS) with:
With us, you graduate with three professional certifications and a master degree
Dubbed the hottest job of the 21st century, data scientists are among the most sought-after professionals in today’s job market. With the rise of generative AI, the demand for experts who can analyze data, build intelligent systems, and drive AI-powered innovations has skyrocketed. According to Glassdoor, data science remains one of the top careers globally in terms of demand, salary, and job satisfaction. In Amsterdam, the average entry-level data scientist salary is €47,422, with increasing opportunities in AI-driven fields such as machine learning, generative AI, and AI ethics. Our programme prepares you for the future of AI and data science by equipping you with cutting-edge technical and leadership skills while connecting you with top employers in the Amsterdam
Total duration of the Master Programme is 19 months.
Installments and Flexible Payment Plan available.
No application fee. Pay the 250 Euro nonrefundable registration fee only when you are accepted and are ready to book your seat.
Schoolvision is recognized by the Dutch Ministry of Education, Culture, and Sciences as a University of Applied Sciences. In close cooperation with Team Academy, it offers the accredited Bachelor of Business Administration in Entrepreneurship, registered under Isat 30117 in the Central Register of Higher Education Programmes (CROHO). The Bachelor of Business Administration in Entrepreneurship has been accredited by the Accreditation Organisation of the Netherlands and Flanders (NVAO) since May 2015. Team Academy is an accredited university; the Bachelor of Science in Software Engineering is still to be accredited under the Team Academy Framework.
Enjoy flexible self-study and being part of a learner community. Get hands-on practice with your weekly projects and assignments and learn with and from your faculty and peers. In practice, a typical week would look like this:
Find out how Team Academy can teach you how to make things happen while discovering yourself and the world!
Foundational Module 0:
Introduction to coding, thinking & statistics (Compulsory for those who can’t meet the passing criteria in the entry test)
This introductory module is designed for students who do not fully meet the basic entry knowledge level or who would like to refresh their skills. Its aim is to give those without programming/coding experience a gentle introduction to programming thinking. This introduction will give the students the required skills to keep up with the computational and statistical background needed to complete this program.
This module introduces students to the programme, facilitators, mentors, and peers. It explores the data science domain, the role of a data scientist, and required tools. Students reflect on their learning journey, set career goals, and learn action learning principles to build effective group dynamics and a strong learning mindset.
This module builds foundational skills to explore, understand, and communicate data effectively. Students learn data structures, relationships, exploratory data analysis, and advanced visualization techniques. Through practical exercises and a project, they gain hands-on experience designing impactful visuals, uncovering insights, and addressing real-world data challenges in decision-making.
This module explores leadership identity, values, and assumptions. Students learn participatory action research principles, experience community support, and understand organizational limitations. They engage with leadership theory, practice, and philosophies, considering the implications of a post-conventional leadership approach.
This module deepens understanding of ethical challenges in data and AI. Students explore bias, data ethics, and privacy, learning strategies to mitigate issues. They examine AI’s ethical implications and governance frameworks ensuring fairness and accountability. A practical project applies these concepts, addressing real-world ethical dilemmas with critical solutions.
This module explores supervised machine learning for predictive modeling, covering regression, GLMs, tree-based models, and survival analysis. Students learn explainability methods to interpret model predictions and apply non-parametric regression techniques. Through theory and projects, they gain practical skills to tackle real-world predictive modeling challenges with transparency and trust.
This module views organizations as complex, dynamic systems, exploring implications for leadership and alternative intervention approaches. Students deepen self-awareness and relational understanding in professional practice. Through action research cycles, they gather relevant data for workshops and written assignments, enhancing their leadership effectiveness within organizational contexts.
This module explores machine learning beyond supervised methods, focusing on unsupervised learning techniques like clustering and dimensionality reduction for uncovering patterns in complex data. Students learn explainability for transparent models, and refine academic writing, equipping them with technical and communication skills for advanced data science.
This module explores deep learning and its AI applications, covering neural networks, CNNs, and RNNs for tasks like image and text analysis. Students gain hands-on experience in NLP and computer vision techniques and participate in a Kaggle competition. Combining theory and projects, this module prepares students for real-world AI challenges.
This module explores Generative AI and Large Language Models (LLMs), focusing on Hugging Face, transformer architecture, and model training. Students learn LLM capabilities, including text generation, summarization, and question answering. They also study Retrieval-Augmented Generation (RAG) and gain hands-on experience fine-tuning models, preparing to apply generative AI to real-world challenges.
This module integrates leadership, ethics, and the broader context of your work. Students explore transformation, knowledge, and change, focusing on their project inquiries. They consider the nature of change they wish to promote, the ethical implications of their actions, and how these align with their leadership ambitions and future goals.
The final project is the last part of your programme and the successful completion of your professional master. It is a chance for you to put all the competencies you learned – both data science, ai and leadership – into practice. The outcome is intended to be a submission of a work-based project along with a written report, a recorded presentation and a live Q&A session. The principal question to ask is “How can I improve my practice?” With this question in mind, this part of the programme offers a practical and structured way of applying action research in a workplace, organisation or independently.
All the ins and outs of this master’s program, including learners’ stories and practical information, are combined in our brochure.
