Master's Degree (MSc) in Applied Data Science & AI
Summary
- Study level Postgraduate
- Credits 90 / 120 ECTS - EQF > / MQF > Level 7
- Duration 3 or 4 terms (12-24 months)
- Location Online / Fully remote
- Next intake September 2025
- Language English
- Tuition Fee €6750
- Accreditation Accredited >
Overview
The MSc in Applied Data Science & AI is designed for graduate students who seek a career at the intersection of management and tech departments within companies. The program has no prerequisites on Computer Science.
In Term 1, you will be exposed to foundational applicative courses on Business, Data Science, Machine Learning, Artificial Intelligence, and Problem-solving. In Term 2, you will apply methodologies and tools to real-life business problems that can be analyzed using Data Science and AI. Term 3 consolidates your profile, through an internship or a project.
EU accredited Institution
Tutors available on a daily basis
Recorded & live content
Strong exposure to real-life business application
Progressive assessments (no final exams)
Flexible pace, leading to a completion in 12-18 months
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Through this Master, you will build a unique profile at the intersection of Business and Data/AI. You’ll be able to build yourself into a well-rounded entrepreneur or apply for top management roles in the areas such as:
- Data and AI Manager
- Strategic Consultant
- Product Manager, Product Owner
- IT Business Analyst
- Business Intelligence Manager
- Entrepreneur
- Chief Data Officer or CTO
Certifications
In courses like Cloud Computing and Project Management, we align with industry-recognized certifications.Top-notch academic leaders
Our selection process focused on recruiting Professors with great academic experience, a strong knowledge of today's Computer Science, and a hands-on teaching approach.
By designing our degrees from scratch we redefined the learning experience in a way that both the academic and professional worlds require today.
Prof. Francesco Profumo
Rector & Head of Institution @ OPIT, Former Minister of Education in Italy
Moez Ali
Founder and Creator @ PyCaret, Product Director - AI @ antuit.ai
Canada
Pierluigi Casale
Former Innovation Officer (Data Science and AI) @ European Parliament, Principal Data Scientist @TomTom
Italy
Paco Awissi
Lead Instructor @ McGill University School of Continuing Studies, Former Senior Vice President @ PSL Group
Canada
Marzi Bakhshandeh
Senior Product Manager @ ING, Former Senior Data Scientist @ Randstad Office & Administration
Netherlands
Syllabus
The program is structured with 2 Terms of courses and 1 final Term for the thesis or project. The final project can be done in the form of an internship in a company or in a research lab (we will facilitate such opportunities). Every Term lasts for 13 weeks:
- Fall term from mid-late September to mid December
- Spring term from late January to late April
- Final term (Capstone): mid-May to completion
First Term 
Python Programming for Data Science
The course covers the basics of the Python programming language, with a focus on its applications in data science. Students learn how to use Python to clean, manipulate, and analyze data using various libraries and tools, including NumPy, Pandas, Matplotlib, and Scikit-learn.
Topics include data structures, control structures, functions, object-oriented programming, data visualization, and machine learning algorithms. Students complete projects that apply these concepts to real-world data sets.
Applied Machine Learning
The course introduces students to the principles and techniques of machine learning and their practical applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation. Students will learn how to implement algorithms for supervised and unsupervised learning, and how to interpret and visualize the results. They will also gain hands-on experience with popular machine learning tools and libraries such as scikit-learn and TensorFlow. The course will equip students with the knowledge and skills necessary to develop and apply machine learning solutions in real-world scenarios.
Big Data and Cloud Computing Infrastructure
The course is designed to give students a non-excessively technical introduction to the architectures, technologies, and tools used to manage and analyze large datasets in cloud computing environments. Topics covered may include Hadoop, Spark, NoSQL databases, data warehousing, and cloud infrastructure such as AWS, Azure, and Google Cloud.
Applied Artificial Intelligence
An applied Artificial Intelligence course focuses on teaching students how to use and apply various artificial intelligence (AI) techniques and technologies to solve real-world problems.
Topics include: Data preprocessing and cleaning, Building and training AI models (using popular software frameworks and tools such as TensorFlow, PyTorch, and scikit-learn), and Deploying AI models in real-world applications, including ethical considerations, security concerns, and privacy issues.
Overall, the goal of the course is to equip students with the skills and knowledge they need to build and apply AI models to solve real-world problems in various industries and domains.
Business Problem Solving
This course covers various problem-solving methods and tools to help students analyze and address complex business issues.
Specific topics covered include techniques for decision-making and creative problem-solving. The course also explores different types of business problems, such as financial, operational, marketing, and strategic issues.
Students are introduced to case studies and simulations that require them to apply problem-solving skills to real-world business situations. The goal of the course is to equip students with the skills and strategies needed to identify, analyze, and solve problems in a business context.
Project Management
This course is designed to teach students the knowledge, skills, and techniques necessary to effectively plan, execute, and control projects.
provides an overview of projects and their backgrounds, and a hands-on approach to project management. It helps identify components, categorize them into types, and discuss the organization's culture. Students explore different methodologies (Agile, Scrum), identify project boundaries, ensure resources, organize them, define governance and reporting, estimate resources, draw responsibility charts, and execute efficiently.
In addition to classroom lectures and discussions, the project management course includes case studies, simulations, and group projects to help students apply the concepts and techniques they learn to real-world scenarios.
Second Term
Business Communication
The course various aspects of communication in a business context, such as effective writing, public speaking, interpersonal communication, and the use of digital media.
Students will learn strategies for creating and delivering presentations, writing reports and proposals, and communicating with stakeholders within and outside of an organization.
Applications in Data Science and Artificial Intelligence – Part 1
This is a thematic module showcasing applications of Data Science and AI tools to different fields e.g. drug design and drug discovery, physiological/physical analysis of athletes to improve performance, risk analysis in the insurance business, finance, and energy distribution optimization.
The course will allow you to put into practice the knowledge, tools, and methodologies learned in the previous modules, by applying the to real-life applicative scenarios.
Applications in Data Science and Artificial Intelligence – Part 2
This is a thematic module showcasing applications of Data Science and AI tools to different fields e.g. drug design and drug discovery, physiological/physical analysis of athletes to improve performance, risk analysis in the insurance business, finance, and energy distribution optimization.
The course will allow you to put into practice the knowledge, tools, and methodologies learned in the previous modules, by applying the to real-life applicative scenarios.
Research Methods and Tools
The course covers various methods and tools that are essential for conducting research in different academic fields. The course is designed to help students understand the research process and develop the necessary skills and knowledge to conduct high-quality research.
Topics include Research Design, Data Collection Methods, Data Analysis, Research Writing and Presentation, Literature Review, Research Ethics.
Ethics and Regulations of Artificial Intelligence
The Ethics and Regulations of Artificial Intelligence (AI) module explores the various implications of AI technologies developed by practitioners, also in relation to the upcoming EU regulation in the field of AI.
Third Term
Capstone Project and Dissertation
The MSc Thesis is a significant project aimed at consolidating skills acquired during the program through a term-long research project. Students, alongside an OPIT supervisor, develop and realize a project proposal in the final term, investigating methodological and practical aspects in program domains. Internships with industrial partners are encouraged. The thesis should demonstrate mastery of the field and familiarity with relevant literature.
Entry requirements
This MSc degree does not require previous knowledge in Data Science and AI. The program is structured so that students coming from a non-tech background will be introduced to foundational concepts during Term 1.
Bachelor degree - even from non-tech fields
English proficiency as B2-level certificate or equivalent
If you don't have an English certificate, try our test
Value for money
Thanks to several funding options, our fees start at an affordable rate per term. Plus, online learning will save you money as you won't need to relocate to pursue your degree.
Fully inclusive
Our course fees are all inclusive, with no hidden charges.
Scholarships & Funding
We have different financing options to fund your degree.
Master Fees
€6750
€2250/term
-
MSc program of 120 credits: €9000 -
Different payment plans available
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10% discount if you pay upfront
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