Master's Degree (MSc) in Applied Data Science & AI

Summary

Study level Postgraduate
Credits 90 / 120 ECTS - EQF > / MQF > Level 7
Duration 3 terms (12-18 months)
Location Online / Fully remote
Next intake September 2025
Language English
Tuition Fee €6750
Accreditation Accredited >

Overview
Career Outlook
Certifications
Accreditation
Syllabus
Fees & Admissions
Frequently Asked Questions

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.

Get the brochure

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

Get the brochure

Career Outlook

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

Learn More

Certifications

In courses like Cloud Computing and Project Management, we align with industry-recognized certifications.

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.

BSc 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

Learn more

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

View program details

First Term
Second Term
Third Term

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

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.

EU Accredited Institution

OPIT is a fully accredited Higher Education Institution under the European Qualification Framework (EQF) and the MFHEA Authority (Malta Qualification Framework – MQF – License number n.005-2023). As such, OPIT’s accredited programs have full legal value in Europe and are professionally recognized by employers

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.

Apply Now

Master Fees

€6750 €2250/term

MSc program of 120 credits: €9000
Different payment plans available
10% discount if you pay upfront

Apply Now

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.

Pierluigi Casale Former Innovation Officer @ European Parliament, Principal Data Scientist @ TomTom.

Moez Ali Founder @ PyCaret, AI Director @ antuit.ai, Adjunct Lecturer @ Queen's University

Panagiota Katsikouli Researcher @ University of Copenhagen, PhD in Computer Science @ University of Edinburgh

Sabya Dasgupta Lead Solution Architect @ Microsoft, Big Data & MLOps technologist.

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 OPIT Head of Institution
Former Minister of Education in Italy

Meet our Faculty

Frequently Asked Questions

See all FAQs

Can I combine study with work?

Yes, we have considered various life circumstances when designing our online study programs. Therefore, you can set your own learning pace according to your needs. 

Bachelor’s degrees of 180 ECTS last 3 academic years, with the option to shorten the study period to 2 years with the fast-track option.

Master’s degrees  of 90 ECTS last 18 months, with the option to shorten the study period to 1 year with the fast-track option.

Master’s degrees  of 120 ECTS last 2 academic years, with the option to shorten the study period to 1.5 years with the fast-track option.

Are the OPIT online degrees recognized?

Yes. OPIT – Open Institute of Technology is a fully accredited Online Higher Education Institution under the European Qualification Framework (EQF) and the MFHEA Authority (Malta Qualification Framework – MQF – License number n.005-2023) and thus meets the European standards defined by the Bologna Process.

OPIT degrees and awards, when their accreditation is completed (you can check the status on every program’s page), have full legal value in Europe and are professionally recognized by employers.

As a result, OPIT awards only official degrees.

Can I extend the duration of my studies?

What are assessments like?

Assessment types vary according to the course and may include programming exercises, group projects and presentations, website/app development, essays, and quizzes. A few examples are:

development of a website or app (Web Development course),
fine-tuning a language model on a given dataset (Applications to Data Science and AI course),
comparative essay on AI regulations (Ethics and Regulations of AI course).