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Master of Science(MS) in Data Science and AI (Top-up)

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Programme Accreditation

Master of Science(MS) in Data Science and AI (top-up) is a 30 ECTS, Malta Qualifications Framework (MQF) / European Qualifications Framework (EQF) Level 7 Higher Education 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.

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Programme Overview

“MS Data Science & AI (Top-up) provides huge career growth by allowing you to complete your Master degree from your former diploma. The data science industry is booming, with job opportunities projected to increase by 36 percent by 2031. As data and technology become integral to various sectors such as healthcare, digital marketing, financial services, technology, retail, media, and telecommunications, the demand for professionals who can manage and interpret this data continues to rise. Our MS Data Science & AI (top-up) programme is designed to lay foundation in research and data analysis that enable you to write your thesis with the allocated mentor.

In addition, all our learners become member of our Competency Lab to develop Career, Research and Entrepreneurial Skills, along with Digital Skills in the programme. Our learners are guided in preparing public portfolio such as publications, or own GitHub, etc. which allows them boost their profile and promote employability.”

Learning Outcomes

Knowledge and Outcomes
Skills

Key Facts

  • Programme Start Date:
    1st working day of every month
  • ECTS Credits:
    30 ECTS Credits
  • Duration:
    6-12 months
  • Total Learning Hours:
    750
  • Contact Hours - 150
    Practice Hours - 150
    Self-Study Hours - 300
    Assessment Hours - 150
  • Mode:
    Online
  • Language:
    English
  • Application Due Date:
    Day 20th of every month
  • MQF/ EQF Level:
    Masters Degree, Level 7
  • Weekly Hours:
    15-40 Hours per week
  • Accreditation:
    Fully Accredited
  • Teaching:
    Asynchronous, Live Residencies
All Key Data
Download Brochure
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Download Brochure
Apply Now

Curriculum And Structure

Curriculum And Structure

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.
Review Curriculum
Review Structure

Exit Awards/Qualifications

Enrolling in Master of Science(MS) in Data Science and AI (Top Up) provides you the award in only the cleared modules of the programme.
Review Exit Awards

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.
We also integrate formative assessment which does not contribute to the final grade, rather helps in peer to peer learning and reflecting on the concepts used.
Grading system can be accessed via Quality Policies in Download section

Target Learners Age

Ages 19 – 30
Age 31 – 65

Prospective Job Titles

  • Data Analyst
  • Natural Language Processing Engineer
  • Research Scholar
  • Computer Vision Engineer
  • Data Scientist
  • Machine Learning Engineer
  • AI Engineer
  • Software Engineer
Target Learners

Ready for the Global Data Science Career!

“EU Global Master of Science(MS) in Data Science and AI (Top Up) focuses on allwoing you to complete your Masters degree if you have already achieved a diploma in Data Science & AI.
Our students study research, and data analysis and employ teh tools learned before in practical problem to recommend solutions.
● Research Project
● Research Residency & Patent Conclave
● Industry Networking & Career Coaching”
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Learning Andragogy

Industry Expert Message

“Welcome to this program 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

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Design Tomorrow’s Innovation with Data and AI Skills
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Regulations

Admissions

Design Tomorrow’s Innovation with Data and AI Skills

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Criteria

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Application And Contact

  • Programme Start Date:
    1st working day of every month
  • Application Due Date:
    20th of every month
Have questions?
Attend our next open house Register here
OR
Email at admissions@euglobal.edu.eu
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Attend our Next Virtual Open House

Knowledge and Outcomes

The learner will be able to:
PA1Demonstrate 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.
PA2Implement programming languages commonly used in data science and AI, such as Python and R, and be proficient in using relevant libraries and frameworks.
PA3Develop expertise in data preprocessing, cleaning, and feature engineering to prepare data for analysis and modelling.
PA4Design 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

Skills

The learner will be able to:
PA5Design and implement machine learning models for various applications, such as classification, regression, clustering, and recommendation systems
PA6Utilise tools like Matplotlib, Seaborn, and Tableau to create compelling visualisations that aid in decision-making processes.
PA7Apply NLP and computer vision techniques to process and analyse human language data, image recognition, object detection, and image generation tasks.
PA8Apply theoretical knowledge and work on capstone projects that showcase the ability to solve complex problems using data science and AI methodologies.

All Key Data

Title of the Qualification: Master of Science (M.S) in Data Science and Artificial Intelligence
Department: Information and Communication Technologies
MQF/ EQF Level: Level 7 (90 ECTS)
Exit Awards (details in programme structure):
1. Post-Graduate Diploma in Data Science & Artificial Intelligence (MQF level 7, 60 ECTS, 9- 24 months)
2. Post-Graduate Certificate in Data Science (MQF Level 7, 30ECTS, 4 -12 months)
3. Micro-credentials for each module (MQF Level 7, respective module ECTS, 5-10 weeks)
Mode of Delivery: 100% Online
Asynchronous, with 1:1 mentor support
Scheduled and pre-announced bootcamps, recordings provided
Capstone Consulting Project
Address where the programme will be offered:
100% Online via our following e-campus
https://campus.europeanglobalvarsity.com/
Mode of Attendance & Duration:
● Full-time – Accelerated – 12 Months, Regular – 18 – 36 Months
● Part-time – 24 – 36 Months
Weekly Hours: 15-40 Hours, depending upon mode of attendance. The students can complete this qualification in 12-18m by studying for around 30-40 hours per week. However, if you aim to study 15-25 hours per week, please expect a longer time period to complete. The minimum time to complete this program is 12m and the maximum time to complete this program is 36m.
Language of Instruction: English
Dates of Intakes: Batch starts 1st working day of every month

Curriculum

Students pursuing MS Data Science & AI/ 90 ECTS have to complete all thirteen modules including the Capstone Consulting Project. 

Students pursuing only top-up will have to complete Research Methods, Statistics for Data Science and Capstone Consulting Project. Top-up Students may have to study additional modules if not recognised at the time of entry. Please consult your admissions team.

Students pursuing Post-Graduate Diploma in Data Science & Artificial Intelligence/ 60 ECTS will have to complete all modules, except Research Methods and Capstone Consulting Projects.

Module/Unit TitleCompulsory (C) or Elective (E)ECTS (Figures must be whole integers and with a value of at least 1 ECTSMQF Level of each module
Statistics for Data ScienceC67
Mathematics for Data ScienceC67
Programming for analytics using PythonC67
Data Virtualization and storytelling with tableauC67
Artificial Intelligence and Machine LearningC67
Machine Learning Methods using PythonC67
Convolution and Recurrent Neural NetworksC67
Computer Vision and Image RecognitionC67
Natural Language ProcessingC67
Big Data and NoSQLC67
Data Warehousing and managementC67
Research MethodsC67
Capstone Consulting ProjectsC187

Program Structure​

The program is offered in online mode with rolling monthly admissions and project -based individual assessment. 

Each module is expected to be completed in 5 weeks when studied full-time, and 8-10 weeks when studied part-time. The full-time and part-time modes will follow the same structure, the only difference will be related to weekly learning hours spent as stated in the duration in the above section.

MS Data Science & AI/ 90 ECTS: Students have to complete all three semesters.

Post Graduate Diploma in Data Science & AI/ 60 ECTS: Students complete only Semester 1 and Semester 2. Students can decide to complete the full-degree later by completing Semester 3. 

MS Data Science & AI Top-up: Students are provided advanced entry and complete Semester 3 only plus Statistics for Data Science. Additional modules may be recommended by your academic committee at time of reviewing your application for advanced entry. 

Module/Unit TitleCompulsory (C) or Elective (E)ECTS (Figures must be whole integers and with a value of at least 1 ECTSMQF Level of each module
Semester 1
Statistics of Data ScienceC67
Mathematics for Data ScienceC67
Programming for analytics using PythonC67
Data Virtualization and storytelling with tableauC67
Artificial Intelligence and Machine LearningC67
Machine Learning Methods using PythonC67
Semester 2
Convolution and Recurrent Neural NetworksC67
Computer Vision and Image RecognitionC67
Natural Language ProcessingC67
Big Data and NoSQLC67
Data Warehousing and managementC67
Semester 3
Research MethodsC67
Capstone Consulting ProjectsC187

Exit Awards/ Qualifications

Learners will be awarded the highest selected title and not the interim awards. The exit awards are relevant if you are not completing the degree, but existing studies in between OR to choose to study in progressive model, which means enrolling for Certificate, and then Diploma and then Degree. But please remember, choosing progressive mode may incur additional expenses for you.
Sr. No.MS in Data Science & AI - revised Program StructureECTS
1Statistics for Data Science6
2Mathematics for Data Science6
3Programming for analytics using Python6
4Data Virtualization and storytelling with tableau6
5Artificial Intelligence and Machine Learning6
6Machine Learning Methods using Python6
7Convolution and Recurrent Neural Networks6
8Computer Vision and Image Recognition6


Capstone Consulting Project 12 Credits
Duration: 9-24 months
The students can complete this qualification- Post- Graduate Diploma in Data Science & Artificial Intelligence in 9m by studying for around 30-40 hours per week.
However, if you aim to study 15-25 hours per week, please expect a longer time period to complete, which is 24 months. The minimum time to complete this program is 9m and the maximum time to complete this program is 24m.
Sr. No.MS in Data Science & AI - revised Program StructureECTS
1Statistics for Data Science6
2Mathematics for Data Science6
3Programming for analytics using Python6
4Data Virtualization and storytelling with tableau6
5Artificial Intelligence and Machine Learning6


Duration 4 -12 months
The students can complete this qualification- Post- Graduate Certificate in Data Science in 4 m by studying for around 30-40 hours per week. However, if you aim to study 15-25 hours per week, please expect a longer time period to complete, which is 12 months. The minimum time to complete this program is 4m and the maximum time to complete this program is 12m.
a. Award in Statistics for Data Science (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
b. Award in Mathematics for Data Science (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
c. Award in Programming for Analytics using Python (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
d. Award in Data Visualization and Storytelling with Tableau (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
e. Award in Artificial Intelligence and Machine Learning (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
f. Award in Machine Learning Methods using Python (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
g. Award in Convolutional and Recurrent Neural Networks (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
h. 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i. Award in Natural Language Processing (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
j. Award in Big Data and NoSQL (MQF Level 7/6ECTS) (Duration:5-10 weeks)
k. Award in Data Warehousing and management(MQF Level 7/6ECTS)
(Duration:5-10 weeks)
l. Award in Research Methods (MQF Level 7/6ECTS)
(Duration:5-10 weeks)
Duration of microcredentials note – 5-10 weeks

The students can complete this qualification of micro- credentials of MQF Level 7/ 6 ECTS) in 5 weeks by studying for around 30-40 hours per week. However, if you aim to study 15-25 hours per week, please expect a longer time period to complete, which is 10 weeks. The minimum time to complete this program is 5 weeks and the maximum time to complete this program is 10 weeks
Sr. No.MS in Data Science & AI – revised Program StructureECTS
1Statistics for Data Science6
2Research Methods6
3Capstone Consulting Projects18


Entry-criteria:
The learners who have completed Level 7 Post- Graduate Diploma in Data Science & Artificial Intelligence can opt to enter into Top-up Master of Science (MS) in Data Science & Artificial Intelligence and complete further 30 ECTS to be awarded the Master's degree – Master of Science (MSc) in Data Science & Artificial Intelligence.
Duration: 6-12 months

The students can complete this qualification of MQF Level 7/ 6 ECTS) in 6 months by studying for around 30-40 hours per week. However, if you aim to study 15-25 hours per week, please expect a longer time period to complete, which is 12 months. The minimum time to complete this program is 6 months and the maximum time to complete this program is 12 months.

Target Group

All learners who meet the eligibility requirement and are passionate to learn about evolving technologies are welcome to participate in their program.

The target group for studying a Master of Science (M.S) in Data Science (DS) and Artificial Intelligence (AI) typically include the following:

Working Professionals

Professionals with relevant work experience in data science, software engineering, or related fields who want to earn formal qualification in DS and ML.

Data Scientists and Analysts

Professionals working in data-related roles who want to deepen their expertise in machine learning and AI techniques can benefit from an MS program to enhance their career prospects.

Entrepreneurs and Innovators

Those looking to start AI and DS-related businesses or develop innovative AI applications can benefit from an MS program to acquire the necessary skills and knowledge.

Researchers

Those interested in conducting research in DS, AI and ML may pursue these masters to gain the necessary skills and knowledge for their research endeavours.

Top-level executives

Top-level executives who wish to understand the landscape of DS & AI to guide its implementation in the organisation.

Relationship to Occupation/s

The learner who have successfully achieved the outcomes for this program can be employed for the following positions with following job descriptions:
Career PathJob Description
Data AnalystForecasting future trends and identifying significant patterns in data. Also, Analysing massive datasets for anomalies, patterns, etc., to make predictions
Natural Language Processing EngineerInvestigating the relationship between spoken language and computer systems, working on chatbot and virtual assistant projects
Research ScholarPursuing Ph.D. in the areas of Data Science
ResearcherEngaging in AI and computer science research, advancing Data Science technologies
Research ScientistExpert in computational statistics, machine learning, deep learning, and applied mathematics, typically requiring a doctorate
Software EngineerDeveloping applications using AI tools, also known as a programmer or AI developer
AI EngineerCreating AI models from scratch, assisting stakeholders in understanding outcomes
Machine Learning EngineerDesigning, developing, and maintaining ML software systems using data
Data ScientistAssembling, scrutinising, and understanding data sets
Computer Vision EngineerCreating and working on systems and projects using visual data

Andragogical Methodology

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:
1. 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.
2. 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. d. Books: Books are an essential source of study to learn concepts in a systematic manner and to practice exercises.
e. 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.
f. 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.
g. 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.

3. 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.

Eligibility Requirement

Following scanned copies of the documents are required to be provided to be admitted for the program
  • Biopage of your valid passport.
  • 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 at least MQF level 5 (Undergraduate Diploma/Certificate) or equivalent knowledge of mathematics (for instance, linear algebra, calculus).
  • Language proficiency certificate: All programmes are taught in English language, and therefore English proficiency is required. For students who completed the bachelor’s degree from the US, UK or any English-speaking countries, OR have studied in English Language for at least 2 years; OR have worked in an English speaking environment for at least 2 years prior to applying for this program, language proficiency certificate is not required. For learners who cannot provide any evidence of their English proficiency, they must provide an English language certificate equivalent to IELTS 6.
  • 200-300 words Statement of Purpose/Motivational Letter
  • Scan of passport size photograph

    • Documents Required

  • Scanned copy of the following documents
  • Passport
  • Last highest qualification degree and transcripts as mentioned in eligibility criteria
  • Scanned passport size professional photograph
  • Proof of English Language – MoI or English Test minimum equivalent to IELTS 6
  • 200-300 words Statement of Purpose/Motivational Letter

    • Selection Process

  • Interview:
    This is an interaction to confirm your interest, spoken English skills, and eligibility and interest in studies

  • Offer Letter & Payment Details:
    You will receive more information from our program team

  • Post payment, fee receipt & enrollment letter – please submit evidence of payment at invoice@euglobal.edu.eu

  • Studies Begin – Mostly all Online Programmes begin on 1st working day of the month, but please check programme related variation. On-Campus programmes, if offered, begin on stipulated months.

    • Scholarships And Fee Financing

  • Interview:
    This is an interaction to confirm your interest, spoken English skills, and eligibility and interest in studies

  • Offer Letter & Payment Details:
    You will receive more information from our program team

  • Post payment, fee receipt & enrollment letter – please submit evidence of payment at invoice@euglobal.edu.eu

  • Studies Begin – Mostly all Online Programmes begin on 1st working day of the month, but please check programme related variation. On-Campus programmes, if offered, begin on stipulated months.

    • Enrolment Letter

    The student gets copy of invoice and and enrolment letter.

    Studies begins first working day of the month, when the access of e-campus and induction documents will be emailed to the student.

    Followed the induction and e-campus access, the students will be introduced to the students success manager in live induction session within first 9 days of the study, the schedule of the live online induction session will be pre-announced.

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