Master of Science in Data Science
Master of Science in Data Science
In an era where data science and artificial intelligence are reshaping industries, there is a critical demand for professionals who are not only well-versed in theoretical knowledge but also adept in practical skills. These professionals are essential for addressing the dynamic challenges and evolving needs of the modern world. As technology progresses at a rapid pace, the ability to adapt and innovate becomes indispensable in various sectors, including healthcare, business, technology, and academia.
Recognizing this need, our Master’s program in Data Science is meticulously crafted to provide a rigorous and comprehensive education that prepares students to thrive in these demanding environments. The program is structured to build a robust foundation in both statistics and computer science, enhancing students’ abilities to handle complex data analyses, develop innovative AI solutions, and make informed decisions that can drive organizational success.
The curriculum is designed with flexibility in mind, offered in a format that accommodates both full-time students and working professionals. It combines core coursework with elective options that allow students to explore advanced AI technologies and engage in meaningful ethical discussions about their use. This approach ensures that graduates are not only equipped with the essential skills needed to excel in their chosen fields but are also prepared to contribute responsibly and ethically to the advancement of society through technology.
Educational Objectives
Our educational objectives are designed to develop well-rounded data science professionals with knowledge and practical skills.
Solid Foundational Courses: Our program offers a comprehensive suite of courses that strengthen foundational knowledge in mathematical and computational areas vital for data science. This robust education supports students’ career development by equipping them with the necessary skills to excel in complex data analyses and problem-solving environments.
Enhanced Practical Skills: The curriculum is tailored to enhance skills in statistical analysis, data mining, and machine learning through engagement with the latest industrial tools and technologies. This prepares students to handle practical data science tasks effectively and stay abreast of industry advancements.
Exposure to Cutting-Edge AI Technologies: Students will also have the opportunity to explore advanced topics such as generative AI, learning how to practically implement these technologies in real-world applications. This exposure prepares them to contribute to the forefront of AI development and innovation.
Capstone Project: The program culminates with a capstone project, which can be undertaken individually or in groups. This guided project is designed to consolidate students’ learning by giving them the opportunity to apply their skills in a comprehensive, real-world scenario. It builds self-study skills, enhances practical problem-solving capabilities, and boosts confidence in their abilities.
Ethical and Responsible Application: Emphasizing the importance of ethics in AI, students will learn to navigate the complex moral landscapes of modern data science, ensuring their work advances society responsibly.
Program Learning Outcomes
Upon completion of the program, students will be able to:
Apply Mathematical and Computational Techniques: Utilize essential mathematical concepts and computational techniques to solve data analysis problems effectively.
Utilize Data Science Tools and Technologies: Effectively use a range of programming tools, data science software, and technologies in machine learning and AI to analyze data, build predictive models, and create impactful data visualizations.
Lead and Implement Data Projects: Demonstrate proficiency in leading and managing data-driven projects, efficiently handling data lifecycles from collection and cleaning through analysis to presentation. Apply learned tools in real-world working environments to deliver practical solutions.
Engage with Ethical Considerations: Critically engage with ethical issues in data science, making informed decisions that consider legal and societal impacts.
Develop Self-Study and Adaptation Skills: Develop the ability to self-study and continuously adapt to industrial advancements, ensuring ongoing relevance and expertise in the field of data science post-graduation.
Career Opportunities
Graduates of our Master’s program are prepared for high-impact roles in various sectors. Possible career paths include but are not limited to:
Data Scientist or Data Analyst: Pursue opportunities in technology firms, finance, healthcare, or government sectors, where you can leverage your skills to interpret and extract meaningful insights from large data sets.
Machine Learning Engineer or AI Specialist: Develop new technologies and innovative solutions, working on the cutting edge of machine learning and artificial intelligence.
Business Intelligence Analyst or Data Strategist: Play a crucial role in startup environments, focusing on the strategic use of data to drive business decisions and foster company growth.
Data Engineer: Build and maintain the architecture (such as databases and large-scale processing systems) that allows for the efficient and insightful analysis of big data.
Researcher or Academic: Continue into PhD programs or contribute to scholarly work in data science, expanding the field’s knowledge base and exploring new frontiers.
- Solid Foundational Courses: Our program offers a comprehensive suite of courses that strengthen foundational knowledge in mathematical and computational areas vital for data science. This robust education supports students’ career development by equipping them with the necessary skills to excel in complex data analyses and problem-solving environments.
- Enhanced Practical Skills: The curriculum is tailored to enhance skills in statistical analysis, data mining, and machine learning through engagement with the latest industrial tools and technologies. This prepares students to handle practical data science tasks effectively and stay abreast of industry advancements.
- Exposure to Cutting-Edge AI Technologies: Students will also have the opportunity to explore advanced topics such as generative AI, learning how to practically implement these technologies in real-world applications. This exposure prepares them to contribute to the forefront of AI development and innovation.
- Capstone Project: The program culminates with a capstone project, which can be undertaken individually or in groups. This guided project is designed to consolidate students’ learning by giving them the opportunity to apply their skills in a comprehensive, real-world scenario. It builds self-study skills, enhances practical problem-solving capabilities, and boosts confidence in their abilities.
- Ethical and Responsible Application: Emphasizing the importance of ethics in AI, students will learn to navigate the complex moral landscapes of modern data science, ensuring their work advances society responsibly.
Upon completion of the program, students will be able to:
Apply Mathematical and Computational Techniques: Utilize essential mathematical concepts and computational techniques to solve data analysis problems effectively.
Utilize Data Science Tools and Technologies: Effectively use a range of programming tools, data science software, and technologies in machine learning and AI to analyze data, build predictive models, and create impactful data visualizations.
Lead and Implement Data Projects: Demonstrate proficiency in leading and managing data-driven projects, efficiently handling data lifecycles from collection and cleaning through analysis to presentation. Apply learned tools in real-world working environments to deliver practical solutions.
Engage with Ethical Considerations: Critically engage with ethical issues in data science, making informed decisions that consider legal and societal impacts.
Develop Self-Study and Adaptation Skills: Develop the ability to self-study and continuously adapt to industrial advancements, ensuring ongoing relevance and expertise in the field of data science post-graduation.
Graduates of our Master’s program are prepared for high-impact roles in various sectors. Possible career paths include but are not limited to:
Data Scientist or Data Analyst: Pursue opportunities in technology firms, finance, healthcare, or government sectors, where you can leverage your skills to interpret and extract meaningful insights from large data sets.
Machine Learning Engineer or AI Specialist: Develop new technologies and innovative solutions, working on the cutting edge of machine learning and artificial intelligence.
Business Intelligence Analyst or Data Strategist: Play a crucial role in startup environments, focusing on the strategic use of data to drive business decisions and foster company growth.
Data Engineer: Build and maintain the architecture (such as databases and large-scale processing systems) that allows for the efficient and insightful analysis of big data.
Researcher or Academic: Continue into PhD programs or contribute to scholarly work in data science, expanding the field’s knowledge base and exploring new frontiers.
Admission Requirements
To qualify for admission, applicants must satisfy the following criteria:
- Hold a Bachelor's degree before the start of the intended enrollment term.
- Demonstrate English proficiency through one of these methods:
- Submit official scores from recognized English language tests like TOEFL, IELTS, Duolingo, or equivalent.
- Earn a degree from an institution where English is the primary language of instruction.
Applicants must also submit a complete application package in accordance with the college’s standard admission procedures. Apply now
2025-26 Curriculum Overview
The MS in Data Science is a 36-semester credit curriculum with three major components: core requirements, electives, and a capstone project.
Graduation Requirements
- Successful completion of the curriculum with a grade point average of no less than B minus.
- Take at least 50% of required credits from Northern College.
Curriculum and Course Description
2025-26 Curriculum Overview
The MS in Data Science is a 36-semester credit curriculum with three major components: core requirements, electives, and a capstone project.
Graduation Requirements
- Successful completion of the curriculum with a grade point average of no less than B minus.
- Take at least 50% of required credits from Northern College.
Core Requirements (15-21 credits)
Core Requirements (15-21 credits)
Credits: 3
Exploring key mathematical concepts, this course equips students with the essential mathematical foundations for data science, including linear algebra, basic statistics, and optimization techniques. Emphasis is placed on practical computational and visualization programming exercises to solidify understanding and relevance in real-world applications.
Credits: 3
Throughout this course, students delve into data structures, database fundamentals, and essential data management and querying tools like SQL. By solidifying Python programming skills and introducing data structure analysis and computational complexity, the course prepares students for complex computational challenges in data science.
Credits: 3
Covering the essentials of probability theory, key statistical distributions, and statistical methods/tests, this course extensively utilizes Python programming to visualize concepts and apply statistical methods in practical scenarios, thereby boosting students’ predictive abilities.
Credits: 3
In this course, students engage with R programming to master data cleaning, transformation, and advanced visualization techniques. Practical projects using Tableau for dashboarding teach how to transform complex datasets into actionable insights.
Credits: 3
This course prepares students to understand and leverage data mining and predictive modeling in business contexts. By using Python and PyTorch, and tackling real-world datasets, students gain practical skills that culminate in comprehensive projects designed to solve actual business problems.
Credits: 3
A cornerstone of the Data Science Master’s Program, this course introduces advanced machine learning algorithms’ theoretical foundations and practical applications. Students engage with logistic regression, decision trees, support vector machines, and deep learning, applying these techniques to real-world data tasks.
Credits: 3
Addressing ethical considerations, this course explores the legal, societal, and ethical challenges emerging from AI and data science technologies. Through discussions and case studies, students learn about the responsibilities and ethical practices essential in technology use.
Electives (9-15 credits)
Electives (9-15 credits)
Credits: 3
Offering practical experience in statistical learning methods, this course focuses on applying these techniques using industry-standard software like R, covering hypothesis testing, generalized linear models, and modern machine learning methods through real-world data and class projects.
Credits: 3
Exploring cloud computing with infrastructure from Amazon Web Services (AWS) and Microsoft Azure, this course includes practical applications using Docker and managing data workflows with technologies like Airflow and Kafka, preparing students for cloud-based solutions deployment.
Credits: 3
Introducing key big data principles and engineering practices, this course covers data ingestion, storage, processing, and analysis with technologies like Hadoop and Spark, enabling students to design and manage effective data pipelines.
Credits: 3
Enhancing knowledge in statistical theories and methods, this course advances students’ understanding of statistical analysis covering probability, maximum likelihood estimation, ANOVA, and regression, and prepares them for precise data analysis roles.
Credits: 3
By applying deep neural networks to computer vision and NLP, this course exposes students to sophisticated machine learning techniques, preparing them to address complex problems such as image classification and text generation.
Credits: 3
Students in this course learn about the forefront of Generative AI, focusing on innovative models like Transformers and Diffusion Models, and how these technologies create novel content, assessing their societal impacts and ethical considerations.
Capstone (6 credits)
Capstone (6 credits)
Credits: 6
In this culminating capstone course of the Data Science Master’s program, students apply their skills in Python, R, and other specialized tools to tackle industry-relevant data science projects. Under the guidance of dedicated mentors, students navigate from problem definition to solution delivery, engaging in real-world challenges that synthesize data analytics and visualization concepts. Through regular mentorship meetings and collaborative or individual project work, students develop robust problem-solving skills and deliver practical solutions, preparing them for professional success in the field of data science.
Faculty Highlights
Meet our distinguished faculty members who are leading experts in their respective fields
