Master of Science in Data Science - Gen AI

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Course Overview

Master of Science in Data Science - Gen AI

The Master of Science in Data Science - Gen AI, is a rigorous program that equips students with both foundational knowledge and advanced skills critical for succeeding in today's rapidly evolving technological landscape. This degree is meticulously designed to cover a broad range of core analytical competencies and technical skills, addressing the diverse demands of industries increasingly reliant on data-driven decision-making.

The Master of Science in Data Science - Gen AI, serves as a transformational educational experience for individuals looking to advance their careers in the data-driven world. By combining analytical competencies with technical skills and practical experiences, graduates are well-prepared to take on the challenges of the global industry. Whether aiming for roles in data analysis, machine learning engineering, or data strategy, the knowledge and expertise gained from this program will be instrumental in navigating the complexities of modern data management landscapes.

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The Data Science Landscape

50X
is the minimum growth over the span of the last 10 years, i.e., 2012 to 2022.
1.5 Million
data-savvy managers will be needed in next 5 years to drive decisions.
$108,000
mean salary for positions in the data science field across the globe.
90%
fortune 500 companies have Data Science initiatives underway.

Why Us

Why choose a Master of Science in Data Science - Gen AI ?

At Birchwood University, the Master of Science in Data Science - Gen AI, with a specialization in Generative AI is designed to prepare you for the future of intelligent technology. Our program builds a strong foundation in core analytical and technical skills while equipping you with advanced expertise in Gen AI—one of the most in-demand and rapidly evolving fields today. As industries increasingly depend on AI-driven insights, automation, and innovation, this degree empowers you to tackle real-world challenges with confidence. Whether you’re aiming for a career in machine learning, data engineering, AI product development, or strategic data leadership, Birchwood University provides the knowledge, hands-on experience, and forward-thinking curriculum you need to excel in a competitive, technology-driven global landscape.

Course Offerings

What does this course have to offer?

Explore key learning outcomes, skills, and competencies designed to shape future professionals.

Apply programming skills in Python and R to develop scalable solutions for data analysis and AI-driven applications.
Design and manage structured databases while implementing efficient data retrieval and manipulation techniques.
Perform exploratory data analysis to uncover patterns and inform the development of statistical and machine learning models.
Develop and evaluate machine learning and deep learning models for predictive and analytical tasks.
Design, fine-tune, and deploy generative AI models, including Large Language Models (LLMs), using modern frameworks and tools.
Build and manage autonomous AI systems and agentic workflows for complex, real-world problem-solving.
Integrate end-to-end data science and generative AI solutions to address industry-specific challenges through a capstone project.

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Admission

Admission Requirements

General Admission Requirements

A copy of a valid government-issued photo identity card.
A copy of an updated resume.
Any document if not in English must be accompanied by a certified translated copy.

Eligiblity Criteria For Master of Science in Data Science - Gen AI

Submit a 500-word essay (minimum) summarizing the applicant’s interest in the Master of Science in Data Science - Gen AI program outlining your professional aspirations.
Provide an official undergraduate degree transcript verifying the completion of a bachelor’s degree in computer science, engineering, mathematics, statistics, or a related field with a cumulative GPA of 2.5 or higher.
Provide two (2) professional recommendation letters attesting to your academic abilities and professional potential.
Personal Interviews will be conducted with the Director of Education for applicants with a GPA below 2.5.

Admission Path

Your Path to Admission

We evaluate candidates based on their educational background, professional performance, consideration, and openness to applications. Our goal is to identify motivated individuals with strong leadership potential and a passion for advancing in the field of data science.

Step 1

Online Application

Step 2

Online Assessment

Step 3

Personal Interview

Step 4

Documents Verification

Step 5

Final Committee Decision

Requirements

Graduation Requirements

Application For Admission

All individuals interested in applying for admission to the university must complete an application and submit a non-refundable registration fee of $150.00 (payable by check, money order, or credit card). Checks and money orders should be made payable to Birchwood University.

Applicants must also provide all required application documents to be considered for admission. Once an admissions decision has been made, the candidate will receive an email with further instructions. Admissions agents will maintain regular contact with applicants to ensure all necessary documents are received by the admissions office.

Post Graduation Requirements

To graduate from Birchwood University and to receive a degree, the students must:

Complete all credits as stated in the catalog.
Need to earn a minimum cumulative grade point average of 3.0.
Meet satisfactory academic progress.
Fulfill all financial obligations.

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Course Key Highlights

Master of Science in Data Science - Gen AI Course - Key Highlights

Earn a globally recognized online master's degree equally credible as offline.

100% Online Programs
No campus visit required
24*7 Access
Advanced learning management system for world-class learning experience.
World-class Curriculum
Developed by Industry experts from leading MNCs
The Cohort Experience
Establish a robust network and connections across industries to boost professional advancements.

Objectives

Program Objectives

Upon completion of the program, students will

Apply the necessary skills to communicate effectively, thoughtfully, and compassionately within the global analytics field.
Apply, synthesize, analyze, and integrate the knowledge of Data Science, Python, Machine Learning, Artificial Intelligence to arrive at innovative solutions to organizational problems.
Demonstrate the skills to work in multicultural organizations within a globalized society.
Demonstrate the ability to develop, analyze and communicate empirical scholarly work.
Develop the competencies in Data Science.

Course Structure

Program Curriculum

A summary of the courses you will learn during the program.

MDS500 | Python Programming | 4 Credit Hours

MDS510 | Data Base Management System | 4 Credit Hours

MDS520 | R Programming | 4 Credit Hours

MDS530 | Exploratory Data Analysis | 4 Credit Hours

MDS540 | Machine Learning | 4 Credit Hours

MDS550 | Machine Learning Model Deployment | 4 Credit Hours

MDS560 | Artificial Intelligence | 4 Credit Hours

The Artificial Intelligence course will expand your technical function and make you all an expert in Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Al concepts and techniques, including, Deep Learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Al knowledge. Also, it will help in understanding Convolution Neural Networks Convolution, Pooling and Generative Networks Adversarial Networks. These skills will enable candidates to seek their career in their desired companies.

Concentration in Gen AI

Generative AI and LLM

This course introduces students to Generative AI concepts, including Autoencoders, GANs, Transformers, and attention mechanisms. Learners will explore Small Language Models, prompt engineering essentials, and advanced prompting strategies, gaining practical insight into how AI generates, interprets, and manipulates data. The course also covers Large Language Models (LLMs), open-source variants, vector databases, and retrieval-augmented generation (RAG) techniques.

Students will gain hands-on experience with LLM frameworks, application development, fine-tuning, and model adaptation. The module also includes practical aspects of deploying generative AI applications, including cloud-based solutions, enabling learners to develop, implement, and manage AI-powered systems efficiently.

Agentic AI and Autonomous AI Systems

This module focuses on Agentic AI, covering the foundational principles, architectures, and design patterns behind autonomous AI systems. Students will explore the creation and management of AI agents using tools such as LangChain, LCEL, LangGraph, and Phidata, and understand the implementation of agentic retrieval-augmented generation (RAG).

The course emphasizes multi-agent systems, advanced agent development with Autogen, and monitoring AI agent performance in real-world scenarios. By the end of the module, students will be equipped to design, develop, and deploy autonomous AI agents that can operate effectively and responsibly in complex, data-driven environments.

AI-driven Software Engineering and LLM Ops

This course bridges AI and software engineering, introducing students to best practices in AI-enhanced development. Topics include continuous integration/continuous deployment (CI/CD) for AI systems, AI-driven code generation and maintenance, software testing automation, and integration of AI into enterprise-grade software applications.

In addition, learners will gain an understanding of LLM operations (LLMOps), covering model monitoring, evaluation, deployment pipelines, continuous training, scaling of LLM systems, governance, and compliance. Practical case studies highlight real-world implementation of AI-driven software engineering and LLM operations, preparing students for professional roles in AI system design and management.

MDS570 | Data Visualization Using Tableau/Power BI | 4 Credit Hours

MDS600 | Capstone Project | 4 Credit Hours

Our Faculty

Faculty Members

Explore insights, research, and expert perspectives shared by our faculty at Birchwood University.

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Prof.Tilokie Depoo, Ph.D

Dr. Tilokie Depoo has dedicated his career in the higher education sector for over 20 years as a highly regarded senior executive and thought leader, successfully bridging the gaps between academic innovation and strategic business thinking. He is an expert at developing and managing multi-site educational programs and institutions, and he brings to the table a distinctive realm of academia, finance, and visionary leadership to embrace the changes accosting the spheres of education and enterprise. Dr. Depoo has shown an unwavering ability to coordinate institutional aspirations with the needs of emerging markets that led to their student equity and access to educational programs.

Dr. Andrew Salisbury

Dr. Andrew Salisbury is a veteran educator and academic from England, UK, who has an eclectic background in the fields of engineering, computer science, business, and digital change. He possesses a BEng (Hons) and a PhD from Lancaster University and has complemented his qualification with postgraduate awards in the teaching and learning course at Sheffield University, achieving Fellowships in Higher Education (FHEA and SFHEA). His teaching portfolio consists of data analytics, object-oriented programming, AI, and machine learning online courses at various universities like UCL, Open University, University of Leeds, University of Edinburgh, University of Aston, and University of Bolton. Dr. Salisbury's research interests are management information systems, database design, and digital transformation with emphasis on the embedding of technology into business education.

Dr. Vinícius Dezem

Dr. Vinícius Dezem is a Brazilian banking executive and data-driven financial solutions expert, strategic management, and financial technologies. He has a Ph.D. in Engineering Knowledge Management from the Federal University of Santa Catarina (UFSC), with an area of focus on Open Banking APIs and decision support systems. His Ph.D. thesis, entitled "Strategies for Future Data-Driven Banking by Open Banking APIs," explores best practices for incorporating in-house and outsourced technology innovation in open banking.

The research highlights the flexibility and cost-saving nature of hybrid models in addressing tight deadlines and budget limitations. Professionally, Dezem has more than a decade of experience in banking. He has been a Banking Manager at Caixa Econômica Federal since 2012, where he has managed projects in data-driven branch optimization, credit structuring, project financing, and strategic partnership development. His job includes mentoring teams and developing a culture of ongoing improvement to drive improved performance.

Prof.Aida Mehrad, Ph.D

Dr. Aida Mehrad is an esteemed psychologist, educator, researcher, and published author with a truly exceptional academic history reflecting two Doctorate degrees (PhDs); Health/Industrial and Organizational Psychology received from the Universitat Autònoma de Barcelona and a second in Social Psychology received from University Putra Malaysia, accumulating over 14 years of teaching experience both nationally and internationally, educating thousands of students. Her work has touched students across continents and cultures.

Prof.Egla Mansi, MS

Egla Mansi is an enthusiastic and empathetic scholar in Behavioural and Development Economics with a sharp academic background and significant experience in teaching and the financial sector. She makes the connections between economic theory and the tangible world by emphasizing how individual behaviour and institutional characteristics shape development outcomes.

Prof.Millet T.De Guzman, Ph.D

Dr. Millet is an experienced and results-oriented professional with more than ten years of focused experience in Supply Chain Management. Dr. Millet has a comprehensive knowledge of logistics, procurement, inventory management, and operations strategy. Her expertise lies with designing optimal complex supply chain processes, improving operations, and managing continuous improvement initiatives in various operational environments.

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