Master of Science (MS) in Data Science and AI

MS Data Science & AI is a 90 ECTS, Malta Qualifications Framework (MQF) / European Qualifications Framework (EQF) full-degree 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).

Docenti Global Business School partners with European Global Institute of Innovation & Technology to offer the MS Data Science & AI for managers and professionals who want to expand interest in the area of data analytics, cyber domain, data engineering, and general management, etc.

The main objective is to prepare students for leadership positions in the local, national, and international economies as well as the digital economy. Through theoretical and practical learning, our MSc program prepares students for future endeavors in both local and worldwide business possibilities. You will learn about having a sustainable impact on the expansion of the global economy.

Our MS Data Science & AI programme is designed and quality assured by doctoral and post-doctoral Professors along with industry experts with huge experience. Learners study 12 modules and a Capstone Consulting Project with an industry mentor.

All modules are assessed using project based assignment, and a Capstone Consulting Project and a Master Thesis towards the end of the programme with an industry mentor.

1. Our Faculty: We bring a programme delivered by experienced and qualified faculty who are passionate about teaching Data Science and AI while guiding professionals through business management principles.
2. Our Technology: We ensure that the programme uses state-of-the-art technology and learning platforms, focusing on trends such Industry 4.0, and AI literacy, providing participants with tools that foster business analytics expertise.
3. Our Student Support: We offer our students strong support services, such as academic advising, career services, learning technological and physical facility ensuring they excel in their pursuit of our Master programme in Lagos and globally.
4. Real-World Impact: Transform Challenges into Opportunities:
   ● Equip yourself to address and solve complex organizational issues, applying skills learned from the master’s in Data Science and AI curriculum.
   ● Drive sustainable change and innovation with a Master of Science tailored for professionals.
5. Global Competence: Lead in a Connected World:
   ●Master cultural intelligence for cross-border collaboration through the renowned European Global Institute of Innovation and Technology MBA, Masters, and DBA programs.
   ● Understand the global trends in digital technology and strategies with exposure to international Data Science.
6. Lifelong Learning: Stay Ahead of the Curve:
   ● Access post-Master learning activities.
   ● Participate in global study tours and business matching programs offered by leading globally recognized schools.
7. Return on Investment: Success You Can Measure:
   ●Immediate application of skills in your role and noticeable growth in your career trajectory, backed by a prestigious Master degree.
   ● Contribute tangibly to organizational success, leveraging skills acquired during your affordable online Master program.
8. Exit Awards/Qualifications: 90 ECTS with WES recognition. Our strategic accreditation allows every learner to earn ECTS credits for every module they study. This allows students to take deferrals, exits and re-join studies and use same ECTS credits for an advanced entry into the programme.

Programme Details

Intakes:
●April and October.

Language of Study:
●English.

Duration:
●One year.

Pricing:
●₦4,000,000.

Key Features:

• ECTS Credits: 90 ECTS credits.
• Duration: 12-36 months.
• Mode: Online (with 2 on-campus sessions in Lagos, Nigeria).
• MQF/EQF Level: Master’s Degree, Level 7.
• Weekly Commitment: 15-40 hours per week.
• Accreditation: Fully accredited (WES recognized).
• Teaching Method: Asynchronous, blended.
• Teaching Body: European Global Institute of Technology and Innovation & Docenti Global Business School.

Awards/Qualifications
Our strategic accreditation allows every learner to earn ECTS credits for every module they study. This allows students to take deferrals, exits and re-join studies and use same ECTS credits for an advanced entry into the programme.

• Award: Master of Science in Data Science and AI from European Global Institute of Technology and Innovation.
• Certificate of Completion for LifeLong Learning in Data Science and AI from Docenti Global Business School.

Awarding Body:

• European Global Institute of Technology and Innovation.
• 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. The 3 Semester can also be compressed in 12 months.

Capstone Consulting Project
The Capstone Consulting Project in Data Science and Artificial Intelligence is the culminating experience for students pursuing a specialisation 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 organisations to address challenging data science and AI problems.

Learning Outcomes
At the end of this program the learner will have acquired the responsibility and autonomy to:

• Apply advanced data science and AI techniques to solve complex, real-world problems.
• Apply project management principles to ensure timely and successful project completion.
• Effectively communicate project progress, findings, and challenges to both technical and non-technical stakeholders.

Total Number of ECTS of this Module/Unit

• 18 ECTS.

Asynchronous/Blended Learning: Delivered through the E-Campus of European Institute of Innovation and technology and Docenti LMS (Learning Management System) with live classes conducted via virtual conferencing platforms.

Coursework-Based Curriculum

• Assessment: Assignment, Capstone Project
• Collaborative Learning: Opportunity to connect and work with peers worldwide through webinars.

The content is specially procured by highly experienced professors and industry experts’ team for you to learn 24X7.
This programme can be studied anywhere in the world and earn globally reputed MQF/EQF accredited degree.

Benefits:

• Designed for experienced executives aged 25 and above (minimum 2 years of work experience).
• Earn a prestigious degree with WES recognition.
• Fully accredited by the Council for Higher Education Development (USA) and approved by the Malta Further & Higher Education Authority (MFHEA).
• 90 ECTS, recognized as a Level 7 full-degree programme under the Malta Qualifications Framework (MQF) / European Qualifications Framework (EQF).
• International immersion and global business matching in Portugal, Spain, Thailand, Kigali, China, Malta, and Canada.
• Implementation-based programs with a collaborative learning approach.
• AI literacy for all graduates of the Master of Science (MS) in Data Science and AI.
• Focus on Industry 4.0 trends and business management technologies.
• Storytelling as a key learning methodology.
• Flexible payment options available.
• Lifetime access to videos, materials, and resources via Docenti LMS.
• Full access to the E-Campus, E-Library, and LMS of the European Global Institute of Innovation and Technology.
• Learn from an international faculty team with 20+ years of industry experience.
• Study real-world global case studies for practical insights.
• Consistently receives excellent reviews from graduates.
• Dedicated supervisors for personalized guidance.
• Internship placement and career support services.
• Exclusive access to the European Global Institute of Innovation & Technology alumni network.
• Business mentoring from industry leaders.
• Prepare to become a global business leader.

The learner will be able to:

1.  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.
2.  Implement programming languages commonly used in data science and AI, such as Python and R, and be proficient in using relevant libraries and frameworks.
3. Develop expertise in data preprocessing, cleaning, and feature engineering to prepare data for analysis and modelling.
4.  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.
   
5. Design and implement machine learning models for various applications, such as classification, regression, clustering, and recommendation systems.
6.  Utilise tools like Matplotlib, Seaborn, and Tableau to create compelling visualisations that aid in decision-making processes.
7.  Apply NLP and computer vision techniques to process and analyse human language data, image recognition, object detection, and image generation tasks.
8. Apply theoretical knowledge and work on capstone projects that showcase the ability to solve complex problems using data science and AI methodologies.

Engaging Programme Content:

Video Lectures:
Pre-recorded lectures you can watch at your convenience.

Interactive Modules:
Multimedia content with quizzes, exercises, and simulations to reinforce learning.

Live Webinars:
Real-time sessions with faculty for lectures, Q&A, and discussions (often recorded for later viewing).

Guest Speakers:
Industry experts sharing insights and real-world perspectives.

We Support Learning

Collaborative Learning:
Online discussions, group projects, and peer feedback are common to foster interaction and knowledge sharing.

Active Learning:
You will be actively involved in the learning process through discussions, problem-solving, and applying concepts to real-world cases.

Modern Technology-Enhanced:
Online platforms provide access to resources, communication tools, and learning materials 24/7.

Global Community:
Interact with classmates from diverse backgrounds and locations, broadening your perspectives.

The following scanned copies of the documents are required to be provided to be admitted for the program.

• Biopage of your valid passport, National ID card or Driving License.
• Bachelor’s academic transcript and degree certificate in any discipline OR equivalent completion of Level 6 qualification with at least 180 ECTS, HND or a four-year undergraduate degree.
• The applicant must have studied Mathematics at least MQF level 5 (Undergraduate Diploma/Certificate or OND in statistics or done our Skill Development Program in Data Science Diploma) or equivalent knowledge of mathematics (for instance, linear algebra, calculus).
• 200-300 words Statement of Purpose/Motivational Letter.
• Scan of passport size photograph.

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 Path – Job Description

• Data Analyst – Forecasting future trends and identifying significant patterns in data. Also, Analysing massive datasets for anomalies, patterns, etc., to make predictions.
• Natural Language Processing Engineer – Investigating the relationship between spoken language and computer systems, working on chatbot and virtual assistant projects.
• Research Scholar – Pursuing Ph.D. in the areas of Data Science.
• Researcher – Engaging in AI and computer science research, advancing Data Science technologies.
• Research Scientist – Expert in computational statistics, machine learning, deep learning, and applied mathematics, typically requiring a doctorate.
• Software Engineer – Developing applications using AI tools, also known as a programmer or AI developer.
• AI Engineer -Creating AI models from scratch, assisting stakeholders in understanding outcomes.
• Machine Learning Engineer – Designing, developing, and maintaining ML software systems using data.
• Data Scientist – Assembling, scrutinising, and understanding data sets.
• Computer Vision Engineer – Creating and working on systems and projects using visual data.

Module 1: Statistics for Data Science

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.

Learning Outcomes:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• Develop a strong foundation in statistical concepts and methods relevant to AI and ML applications.
• Implement linear regression, logistic regression, and other regression/classification algorithms for specific AI tasks.
• Evaluate and recommend the use of descriptive statistics, probability, confidence intervals, hypothesis testing, analysis of variance, regression and correlation analysis, t-tests, and technological applications for statistical analysis, as well as the interpretation of the relevance of statistical findings for solving real-life problems and making decisions.

Total Number of ECTS of this Module/Unit

• 6 ECTS.

Module 2: Mathematics for Data Science

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.

Learning Outcomes:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• Demonstrate a strong foundational understanding of key mathematical concepts relevant to data science.
• Apply linear algebra concepts to perform operations on data, transform feature spaces, and understand linear transformations in machine learning.

Total Number of ECTS of this Module/Unit

• 6 ECTS.

Module 3: Programming for Analytics using Python

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 visualisation problems.

Learning Outcomes:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• Understand the basics of Python programming language
• Utilise fundamental programming constructs such as variables, data types, loops, and conditionals.
• Acquire skills in using Python libraries for data manipulation and analysis, such as NumPy and pandas.

Total Number of ECTS of this Module/Unit

• 6 ECTS.
  

Module 4: Data Visualization and Storytelling with Tableau

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.

Learning Outcomes:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• Experiment with various datasets and create amazing graphs with Tableau, creating fascinating stories with the data’s hidden information.
• Use Tableau’s array of resources to show best practices for data narrative and visualisation.
• Discover the benefits and drawbacks of each graph and see how simple it is to create geographical maps using Tableau.

Total Number of ECTS of this Module/Unit

• 6 ECTS.
  

Module 5: Artificial Intelligence and Machine Learning

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.

Learning Outcomes:
At the end of the module/unit the learner  will  have acquired the responsibility and autonomy  to:

• Define and explain the fundamental concepts, principles, and applications of ML.
• Implement and evaluate various supervised learning algorithms, such as naive bayes, linear regression, decision trees, and support vector machines.
• Explore unsupervised learning techniques, including clustering and dimensionality reduction.
• Apply algorithms like k-means clustering and principal component analysis (PCA).

Total Number of ECTS of this Module/Unit

• 6 ECTS.
  

Module 6: Machine Learning Methods using Python

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.

Learning Outcomes
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• Understand Python libraries for machine learning and divide dataset into training and test datasets.
• Implement linear and polynomial regression, understand Ridge and lasso Regression, acquire programming skills in core Python.
• Identify appropriate techniques to solve the formulated AI & ML.
• Develop the ability to write database applications in Python.

Total Number of ECTS of this Module/Unit

• 6 ECTS.