Master of Science (MS)
in Data Science and AI

Project based learning for immediate
career opportunities in Data Science

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Key Information Highlights Curriculum Andragogy Assessments Fees FAQs

Programme Accreditation

MS Data Science & AI is a 90 ECTS, European Qualifications Framework (EQF) full-degree Level 7 programme. This programme is fully accredited by Council for Higher Education Development, USA and is also fully approved by Malta Further & Higher Education Authority (MFHEA).

EU Global accepts both experiential Recognition of Prior Learning (RPL) and credit transfer through the use of learning outcomes for either an advanced entry into the programme, or module exemptions for an advanced progression in a programme.

About the Programme

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.

Learning Outcome


Knowledge and Outcomes


Skills


Key information

  • Programme Start Date:

    May 2025

  • Credit Hours:

    90 ECTS Credits

  • Duration

    18 months

  • Language:

    English

  • Mode:

    Online

  • Registration Deadline:

    17th April 2025

  • EQF Level:

    Masters Degree, Level 7

  • Weekly Hours:

    15-40 Hours per week

  • Accreditation:

    Accredited

  • Teaching:

    Asynchronous, Live Residencies

Programme Highlights

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.

Module/Unit Title Compulsory / Elective ECTS
Statistics of Data Science Compulsory 6
Mathematics for Data Science Compulsory 6
Programming for analytics using Python Compulsory 6
Data Virtualization and storytelling with tableau Compulsory 6
Artificial Intelligence and Machine Learning Compulsory 6
Machine Learning Methods using Python Compulsory 6
Convolution and Recurrent Neural Networks Compulsory 6
Computer Vision and Image Recognition Compulsory 6
Natural Language Processing Compulsory 6
Big Data and NoSQL Compulsory 6
Data Warehousing and management Compulsory 6
Research Methods Compulsory 6
Capstone Consulting Projects Compulsory 18
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Eligibility

Scanned copies of the following documents are required to be submitted to be eligible to enrol

  • 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).
  • Mathematics as a course in Graduation OR equivalent knowledge of mathematics (for instance, linear algebra, calculus) etc. is a mandatory prerequisite.
  • English Proficiency - Medium of instruction during school and graduation or work experience should be English OR IELTS score of 6 or equivalent.
  • 200-300 words Statement of Purpose/Motivational Letter
  • Scan of passport size photograph

Andragogy

To promote learning in accordance with the desired levels of the further higher education framework, EU Global uses modern teaching aids to facilitate learning such as flipped classrooms where learners are provided content access to pre-read to allow better understanding and promote engaging discussions on application of the concept. Active learning strategies are adopted to ensure development of cognition of learners so that they develop analytical, critical thinking and creative skills.

The following are key teaching aids employed within our didactic model:

Personality Test

The goal of the MBTI is to allow respondents to further explore and understand their own personalities including their likes, dislikes, strengths, weaknesses, possible career preferences, and compatibility with other people. This survey is conducted via Truity (https://www.truity.com/) for all our new admissions. This reflationary exercise helps the mentors and students set the expectations and targets for self-development for the further academic duration of study.

Learning Resources

a) Case Studies: Case studies from Harvard and other sources, and caselets like daily business news set the base for almost every course. Case studies help in review of real-life scenarios and the way a conceptual framework is related to real-life scenarios to provide solutions and recommendations.

b) Simulations: A simulation helps students imitate the real-life scenario, and to take probabilistic decisions to witness the results in terms of efficiency of the decision.

c) Research papers: Literature and conclusions derived from research papers is a very important source of learning from other scholars. These provide wider perspective and apprises of what have been already researched in the field of study. Books are an essential source of study to learn concepts in a systematic manner and to practice exercises.

d) Audio-video learning: Audio-video learning has been considered as one of the imperative tools that suits well with varied learning personalities. It includes podcasts, videos from Professors, documentaries from BBC, etc.

e) Research Projects: Seminars aim to thoughtfully design research activities such as surveys, etc so that students can learn primary research to investigate a business problem.

f) Miscellaneous activities: We promote innovation which every faculty brings. The faculty is advised to prepare academic delivery in an engaging manner. They are motivated to bring in activities like role-plays, presentations, etc.

Use of Technology

EU Global has a very well-developed Learning Management System which is instrumental in exchange of information between the School’s administration, faculty and the students. Each student will be provided an access to our learning management system from day 1 of their enrolment. The system will have the following key components:

a) Induction: The induction module is called “Student Services” which allows access to all the School’s regulations and policies, where students can ask questions, academic writing resources, and all essential information that are instrumental in getting the students to start with us.

b) Course-wise Resources: All the information, and learning resources related to the chosen courses are provided via our learning management system. This provides better communication.

c) Assessments: The students are required to upload all submission-type assessments via the learning management system.

d) Capstone Consulting Project & Thesis: Research on a real business problem with an industry expert and write a Master thesis.

e) Career Coaching and Academic Coaching: The students are also provided additional modules to enhance employability via our learning management system.

Assessments

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.

Total Programme Fee EUR 4600 / USD 5379

Call +91 74288 43369 to know more about attractive limited time upfront payment discounts up to 40% on the Programme Fee that you may be eligible for!

Payment Option 1

Make a downpayment of USD 300. Upon selection into the programme, make upfront payment of the full programme fee and avail discount of up to 40% on the total fee.

Payment Option 2

Make a downpayment of USD 300. Upon selection into the programme, pay 50% of the Programme Fee and remaining 50% within 6 months of Registration.

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Industry Expert Message

“Welcome to this programme on MS Data Science & Artificial Intelligence. I have the honour of reviewing the curriculum and teaching “Computer Vision Course”.

Overall, I am impressed with the depth of curriculum. More so, I find the hybrid style of teaching highly effective.

The courses are well and thoughtfully designed with the tools taught that are used in the industry. I being the founder of the Creo Group, an IT consulting company in Hungary takes this immense pleasure to mentor future generation, learners enrolled in this programme.

Best Wishes,
László Grad-Gyenge,

Managing Director, Creo Group

Professor, European Global

About EU

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.

Frequently Asked Questions

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 programme.

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.
  • • 200-300 words Statement of Purpose/Motivational Letter
  • • Scan of passport size photo

  • • 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 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.

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.
  • • All Software related to the Data Science study such as Python, R, Tableau, Jupyter, Hadoop, NoSQL, etc are open source software, and are available for free download. A guide to install these software will be provided by your respective instructor.
  • • 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

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.

Graduates can pursue careers as Data Scientists, Machine Learning Engineers, AI Researchers, Data Analysts, Business Intelligence Analysts, and more. The skills acquired are in high demand across industries including technology, finance, healthcare, and automotive sectors.

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 programme and must meet our minimum grade requirements. For more details, please refer to our Recognition of Prior Learning Policy.

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 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.

You may choose to opt out of this programme 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.