How is the program delivered?

The students get access to the e-campus on the start date with the 24X7 accessible learning content. The students get introduced to the students' success manager who will be the first point of contact for any queries resolution. The students also get access to Live BootCamps on a monthly basis in various areas such as research, subject-specific bootcamps. In addition, the student is introduced with an Industry mentor to help them build their project portfolio along with the thesis. The mentor is introduced one month before the start of the Capstone Consulting Project.

Are there any technical requirements for studying this program?

There are no specific technical requirements to access the course online. However, here are some common technical requirements: Hardware Requirements Computer: A reliable laptop or desktop computer is typically required. Internet connection: A stable and high-speed internet connection is essential for accessing course materials, participating in online discussions, and submitting assignments. Webcam: Might be necessary for video conferencing or proctored exams. Microphone: Required for audio communication during online sessions. Speakers: For listening to audio content. Software Requirements Web browser: A compatible web browser (Chrome, Firefox, Safari, or Edge) is necessary to access the LMS. Software: All software related to the Data Science study such as Python, R, Tableau, Jupyter, Hadoop, NoSQL, etc. are open source and available for free download. A guide to install these software will be provided by your respective instructor. Mobile access: You can access the LMS on mobile as well, however it is not recommended because of the project practice required while studying, which is effective only on Laptop.

Do I need Mathematics for admission in MS Data Science & AI?

Yes, mathematics as a course in Graduation is required. If a student doesn't have the course, he/she still requires an equivalent knowledge of mathematics (for instance, linear algebra, calculus). If the student doesn't have a formal Mathematics as a course in Bachelor studies, but has studied it elsewhere, he/she can submit the evidence for Recognition of prior learning. OR he/she can pursue a 1 month Mathematics Certificate with EU Global prior to enrolling into MS.

Instructor-Led Online Training with 24X7 Lifetime Support

About Master of Science (MS) in Data Science and AI

The MS in Data Science & AI offers exceptional career growth, with the field rapidly expanding and job opportunities expected to grow by 36% by 2031. As data and technology become central to industries like healthcare, digital marketing, finance, technology, retail, media, and telecommunications, the need for skilled professionals to interpret and manage data is on the rise.

Our MS Data Science & AI programme, developed and rigorously reviewed by doctoral and post-doctoral professors alongside industry experts, includes 12 modules and a Capstone Consulting Project guided by an industry mentor. Each module is assessed through project-based assignments, concluding with a Capstone Consulting Project and a Master's Thesis with industry mentorship.

Additionally, all learners gain access to our Competency Lab, where they develop career, research, entrepreneurial, and digital skills. We support students in creating public portfolios, such as publications or GitHub profiles, to enhance their professional presence and employability.

The key program highlights are as follows:

Practical and Experiential Learning – 1-to-1 mentor support with hands-on exposure through 12+ mini-projects and a Consulting Project.
Qualification on Opted Exit – Earn ECTS credits for each module with flexible defer, exit, and re-join options.
Internationally Recognized Masters Degree – Globally accepted accreditation for cross-border recognition and career mobility.
Alumni Status of an International Business School – Receive an authentic MS degree and EU Global alumni status on completion.
Exposure to Key Data Science & AI Tools – Hands-on with Matplotlib, Pandas, NumPy, Scikit-learn, TensorFlow, and more.

90 Credit Hours Program
18 Month Duration
English Medium Instruction
EQF Level 7 Degree
15–40 Weekly Study Hours
Globally Accredited Qualification
Asynchronous Learning Model
Live MasterCamps
Fully Online Program

Program Learning Outcomes

Demonstrate a deep understanding of core concepts in Data Science and Artificial Intelligence, including statistical modelling, machine learning algorithms, neural networks, big data technologies, natural language processing and computer vision.
Implement programming languages commonly used in data science and AI, such as Python and R, and be proficient in using relevant libraries and frameworks.
Develop expertise in data preprocessing, cleaning, and feature engineering to prepare data for analysis and modelling.
Design and develop research-based solutions for complex problems in data science, artificial intelligence and machine learning industry through appropriate consideration for the public health, safety, cultural, societal, and environmental concerns.

Program Certificate

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The actual certificate might slightly differ from the sample certificate

EU Global follows continuous and end of the module assessment. Continuous assessment is conducted within various units studied by the learner, and counts towards the final grades, the weightage of continuous assessment is 40%. The nature of continuous assessment is normally multiple choice questions. End of the module assessment is the final assessment, consisting of 60% weightage. The nature of final assessment is the report submission. The report can be a project, analysis, case study, research paper, etc. Formative assessments are also integrated which does not contribute to the final grade but rather helps in peer to peer learning and reflecting on the concepts used.

Master of Science (MS) in Data Science and AI Course Curriculum

Students will discover the concepts and gain expertise in the usage and applications of algorithms of Data Science and Artificial Intelligence. They will have abundant opportunities to plunge into advanced concepts. Through hands-on projects, students will gain experience on the concepts behind search algorithms, clustering, classification, optimization, reinforcement learning and other topics such as deep learning, computer vision, natural language processing techniques and incorporate the learning in Python. This programme would enable students to embrace the concepts of DS and AI and understand their extension to its application. Students will work on projects involving AI in healthcare, education, finance, manufacturing sectors etc. Meticulously designed curriculum suitable to the industry needs with a high focus on practical applications.

The course focuses on developing statistical thinking to set a foundation of various specialisation courses in their future course of study. It involves introduction to the statistical concepts and tools widely used for Data Analysis and helps in effective decision making. Statistical knowledge develops and extends the conceptual knowledge of students to infer noteworthy results/findings.

Students will be given an opportunity to work through sample data as well as the theoretical principles, tools, and procedures of statistics.

Mathematics for Data Science is a foundational course that provides essential mathematical concepts and techniques required for understanding and analysing data in various fields such as statistics, machine learning, and data analysis. Understanding these mathematical concepts and techniques provides a solid foundation for tackling real-world data science problems and developing effective solutions.

This course comprehensively addresses foundational principles essential for entry into the realm of data analytics, integrating both theoretical frameworks and practical applications. It functions as a foundational stepping stone for individuals seeking to engage with data, catering particularly to novices in the field.

The course allows students to gain an in-depth understanding of programming in Python for data analytics. Students slowly gain pace by creating a variety of basic scripts and gradually pick up advanced features with each of the course modules designed meticulously. The course will allow students to explore the large and multi-faceted Python libraries to solve a wide variety of data analytics and data visualization problems.

The foundations of good data-driven storytelling will be covered in this course. The skills that students acquire will enable them to convey data findings in visual, oral, and written contexts to a variety of audiences and the public. The associated tools will be introduced to the class. Students learn the abilities needed to be proficient Data Storytellers on this course.

They will learn where to obtain and download datasets, how to mine those databases for information, and how to present their findings in a variety of forms. Through visual data analysis, students will learn how to “connect the dots” in a dataset and identify the narrative thread that both explains what’s happening and draws their audience into a tale about the data. Additionally, students will learn how to convey data stories in various ways to various stakeholders and audiences.

This course widely covers contemporary topics in Artificial Intelligence, primarily – Machine learning. It deeply focuses on the core concepts of supervised and unsupervised learning. Learners will learn the popular Machine Learning algorithms and techniques. The exercises after each unit will extend the applications of machine learning concepts to a range of real-world problems. This course will focus on related topics like machine learning, deep learning and their applications and solutions. Learners shall be able to acquire the ability to design intelligent solutions for various business problems in a variety of domains.

Throughout the course, emphasis will be placed on both theoretical understanding and practical implementation of machine learning algorithms. By the end of the course, students will have gained a solid understanding of the fundamental concepts and techniques of machine learning and will be well-prepared to apply them to real-world problems.

The purpose of this course is to serve as an introduction to machine learning with Python. Learners will explore several clustering, classification, and regression algorithms and see how they can help us perform a variety of machine learning tasks. Then learners will apply what they have learned to generate predictions and perform segmentation on real-world data sets. In particular, learners will structure machine learning models as though they were producing a data product, an actionable model that can be used in larger programs. After this course, learners should understand the basics of machine learning and how to implement machine learning algorithms on your data sets using Python. Specifically, they should understand basic regression, classification, and clustering algorithms and how to fit a model and use it to predict future outcomes.

This course is designed to provide an in-depth understanding of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two fundamental architectures in the field of deep learning. Participants will gain hands-on experience in designing, implementing, and optimising these neural network types for various applications, including image recognition, natural language processing, and sequential data analysis.

The objectives are to develop understanding of the basic principles and techniques of image processing and image understanding, and to develop skills in the design and implementation of computer vision software.

To introduce students the fundamentals of image formation; To introduce students the major ideas, methods, and techniques of computer vision and pattern recognition; To develop an appreciation for various issues in the design of computer vision and object recognition systems; and To provide the student with programming experience from implementing computer vision and object recognition applications

The area of natural language processing (NLP) is expanding quickly and has broad applications in the humanities, social sciences, and hard sciences. Effective linguistic and textual data management, use, and analysis is a highly in-demand skill for academic research, in government, and in the corporate sector. The goal of this course is to provide a theoretical and methodological introduction to the most popular and successful current approaches, tactics, and toolkits for natural language processing, with a particular emphasis on those created by the Python programming language.

Students will gain extensive experience using Python to conduct textual and linguistic analyses, and by the end of the course, they will have developed their own individual projects, gaining a practical understanding of natural language processing workflows along with specific tools and methods for evaluating the results achieved through NLP-based experiments. In addition to comparing new digital methodologies to traditional approaches to philological analysis, students will gain extensive experience using Python to conduct textual and linguistic analyses.

The broad rise of large information stockpiling needs has driven the birth of databases generally alluded to as NoSQL information bases. This course will investigate the sources of NoSQL information bases and the qualities that recognize them from customary data set administration frameworks. Central ideas of NoSQL information bases will be introduced.

In this course, learners will learn exciting concepts and skills for designing data warehouses and creating data integration workflows. These are fundamental skills for data warehouse developers and administrators. Learners will have hands-on experience for data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows. In the data integration assignment, learners can use either Oracle, MySQL, or PostgreSQL databases. Learner will also gain

conceptual background about maturity models, architectures, multidimensional models, and management practices, providing an organisational perspective about data warehouse development. If a learner wants to become a data warehouse designer or administrator, this course will give accurate knowledge and skills to do that. By the end of the course, learner will have the design experience, software background, and organizational context that prepares you to succeed with data warehouse development projects. In this course, learners will create data warehouse designs and data integration workflows that satisfy the business intelligence needs of organizations.

A research methodology course equips students with the foundational skills and knowledge needed to conduct rigorous and effective research across various disciplines. Through this course, students learn the principles and techniques essential for designing, executing, and interpreting research studies. They delve into topics such as formulating research questions, selecting appropriate data collection methods, understanding sampling techniques, and mastering data analysis methods, both qualitative and quantitative. Moreover, the course covers ethical considerations, emphasising responsible and transparent research practices. Students gain proficiency in constructing research proposals, reviewing existing literature, and presenting findings with clarity and precision.

This course is highly relevant to understand the systematic scientific research writing process. This process helps in putting in perspective all conceptual learning and provides a framework for continuous growth in one’s own work environment.

The Capstone Consulting Project in Data Science and Artificial Intelligence is the culminating experience for students pursuing a specialization in these fields. This course provides students with the opportunity to apply their knowledge and skills to real-world problems through a hands-on consulting project. Working in teams, students will collaborate with industry partners or organizations to address challenging data science and AI problems.

This course requires submission of Master Thesis.

Andragogy at EU Global

EU Global follows a learner-centric, modern teaching approach designed to foster critical thinking, creativity, and real-world problem-solving. Using flipped classrooms and active learning strategies, we ensure students go beyond theory to develop practical, analytical, and innovative skills.

Key Teaching Methods

Personality Test (MBTI): Conducted via Truity, helping students understand strengths, career paths, and set personal growth goals.
Case Studies & Simulations: Real business cases and scenario-based learning to sharpen decision-making.
Research & Reading: Access to research papers, books, and scholarly work for deeper academic insights.
Multimedia Learning: Podcasts, video lectures, and documentaries to engage diverse learning styles.
Projects & Activities: Research projects, role-plays, presentations, and interactive exercises led by faculty.

Technology Integration

Our advanced Learning Management System (LMS) ensures seamless access to:

Induction & Resources: Policies, academic writing tools, and course materials from day one.
Assessments: Easy online submissions and feedback.
Capstone Project & Thesis: Industry-linked research on real business problems.
Career & Academic Coaching: Modules to boost employability and professional growth.

Master of Science in Data Science and AI Skills

Design and implement machine learning models for various applications, such as classification, regression, clustering, and recommendation systems.
Utilise tools like Matplotlib, Seaborn, and Tableau to create compelling visualizations that aid in decision-making processes.
Apply NLP and computer vision techniques to process and analyse human language data, image recognition, object detection, and image generation tasks.
Apply theoretical knowledge and work on capstone projects that showcase the ability to solve complex problems using data science and AI methodologies.

About EU Global

The European Global Institute of Innovation & Technology (EU Global) is an accredited higher education institution, established with the vision of delivering high-quality, and accredited education to learners worldwide, enhancing both their employability and global mobility. Our teaching approach emphasizes project-based learning, centered on evidence-based reflection, allowing students to apply conceptual frameworks to real-world decision-making.

We are deeply committed to developing future competencies through quality education that fosters lifelong employability on a global scale. Our Competency Lab offers a range of programmes in research, entrepreneurship, sustainability, and professional development, nurturing the soft skills necessary for leadership and effective interaction.

European Global Varsity, part of the same education group, facilitates partnerships between European universities and institutions around the world, expanding opportunities for global collaboration.

Assessment and Grading

Assessments & Milestones

The MS in Data Science & AI includes module-based evaluations, project submissions, and a final dissertation. Learners will progress through continuous assessments that ensure conceptual clarity and practical application of skills.

Formative & Capstone Project

Formative assessments are integrated to support peer learning and reflective practice. Learners are required to complete a Capstone Consulting Project and a Master’s Thesis under industry mentorship, with detailed guidance on structure and expectations.

Grading & Award

The grading system follows EU Global’s academic policies. Upon successful completion of all modules, project work, and the final thesis, candidates will be conferred with the “Master of Science in Data Science & Artificial Intelligence” degree.

FAQs

Program Details

Eligibility

Admission Process

Support

Online MS Data Science & AI can be completed flexibly between 12 months to 36 months depending upon the mode chosen. The typical duration, however, is about 18 months.

The students get access to the e-campus on the start date with the 24X7 accessible learning content. The students get introduced to the students' success manager who will be the first point of contact for any queries resolution.
The students also get access to Live BootCamps on a monthly basis in various areas such as research, subject-specific bootcamps.
In addition, the student is introduced with an Industry mentor to help them build their project portfolio along with the thesis. The mentor is introduced one month before the start of the Capstone Consulting Project.

The e-campus gives access to 1 course at one point of time. Each course contains around 10 units consisting of Professor’s videos, study material, forums and a minor assessment. Post completing this course, the next course access is automatically activated. This progressive learning is found to be most effective for organised dissemination of content, and also researching and setting strong foundations for advanced modules.

We do formative assessments for peer to peer collaboration and the summative assessment. Summative assessment is divided into two parts: Unit-wise assessment comprising of multiple choice questions is of 40% and end of the course assessment is of 60%. Most end of the module assessments are project-based analytical submission based assessments. We DO NOT do question and answer assessments. Post completing assessment of all courses, the learners move to their Capstone Consulting Project and a Master Thesis.

There are no specific technical requirements to access the course online.

However, here are some common technical requirements:

Hardware Requirements

Computer: A reliable laptop or desktop computer is typically required.
Internet connection: A stable and high-speed internet connection is essential for accessing course materials, participating in online discussions, and submitting assignments.
Webcam: Might be necessary for video conferencing or proctored exams.
Microphone: Required for audio communication during online sessions.
Speakers: For listening to audio content.

Software Requirements

Web browser: A compatible web browser (Chrome, Firefox, Safari, or Edge) is necessary to access the LMS.
Software: All software related to the Data Science study such as Python, R, Tableau, Jupyter, Hadoop, NoSQL, etc. are open source and available for free download. A guide to install these software will be provided by your respective instructor.
Mobile access: You can access the LMS on mobile as well, however it is not recommended because of the project practice required while studying, which is effective only on Laptop.

The programme has strong ties with industry partners, offering students opportunities for internships, industry-sponsored projects, and networking events. These collaborations provide practical experience and can lead to job placements post-graduation.

The programme typically requires students to complete a thesis or a capstone project, which involves conducting original research or applying advanced AI techniques to solve a real-world problem.

Study time differs from candidate to candidate, depending upon individual’s academic ability and learning style. As a general guide, it is recommended to provision for about 15 to 40 hours of study per week. Students who develop an effective learning style may require less effort to complete the prescribed curriculum.

The program has strong ties with industry partners, offering students opportunities for internships, industry-sponsored projects, and networking events. These collaborations provide practical experience and can lead to job placements post-graduation.

Yes, mathematics as a course in Graduation is required. If a student doesn't have the course, he/she still requires an equivalent knowledge of mathematics (for instance, linear algebra, calculus).

If the student doesn't have a formal Mathematics as a course in Bachelor studies, but has studied it elsewhere, he/she can submit the evidence for Recognition of prior learning.
OR he/she can pursue a 1 month Mathematics Certificate with EU Global prior to enrolling into MS.

The admissions are purely based on merit substantiated by the transcripts of Bachelor’s or equivalent degree and there are no entrance exams to qualify for admissions into the MS Data Science & AI program.

Eligibility Requirements:

Bachelor’s academic transcript and degree certificate in any discipline OR equivalent completion of Level 6 qualification with at least 180 ECTS. The applicant must have studied Mathematics (Undergraduate Diploma/Certificate) or equivalent knowledge of mathematics (for instance, linear algebra, calculus).

English Proficiency: Medium of instruction during school and graduation or work experience should be English OR IELTS score of 6 or equivalent.
Statement of Purpose/Motivational Letter: 200–300 words.
Other Documents: Scan of passport size photo.

Credit transfer is possible but subject to approval. The courses you wish to transfer must be equivalent in content and level to those offered in the program and must meet our minimum grade requirements. For more details, please refer to our Recognition of Prior Learning Policy.

The certificate awarded only will mention the programme of study and is of the similar content and format as the certificate being awarded to the regular on campus students.

For Online programmes, we prepare you for interviews in the Global reputed companies. All learners get access to our Competency Lab along with a Career Coach until they get employed. Competency Lab provides you contemporary skills required to get employed such as building your LinkedIn profile, research skills, resume preparation, with verifiable shareable certificates to boost your CV.

You may choose to opt out of this program and request for a refund any time before commencement vide an email. Your refund will be processed after deduction of processing charges of EUR 150. No refund request will be considered or processed, once the cohort commences and any amount paid will be forfeited.

European Global MS in Data Science and AI

Accreditations

Market Opportunities

About Master of Science (MS) in Data Science and AI

Enroll in MS in Data Science and AI

Program Learning Outcomes

Program Certificate

Program Eligibility

Data Science and AI Career Opportunities

Master of Science (MS) in Data Science and AI Course Curriculum

Statistics of Data Science (6 ECTS, 150 Hours)

Mathematics for Data Science (6 ECTS, 150 Hours)

Programming for Analytics using Python (6 ECTS, 150 Hours)

Data Visualization and Storytelling with Tableau (6 ECTS, 150 Hours)

Artificial Intelligence and Machine Learning (6 ECTS, 150 Hours)

Machine Learning Methods using Python (6 ECTS, 150 Hours)

Convolutional and Recurrent Neural Networks (6 ECTS, 150 Hours)

Computer Vision and Image Recognition (6 ECTS, 150 Hours)

Natural Language Processing (6 ECTS, 150 Hours)

Big Data and NoSQL (6 ECTS, 150 Hours)

Data Warehousing and Management (6 ECTS, 150 Hours)

Research Methods (6 ECTS, 150 Hours)

Capstone Consulting Projects (18 ECTS, 450 Hours)

Andragogy at EU Global

Technology Integration

About EU Global

Industry Expert Message

László Grad-Gyenge,

Industry Expert Message