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M.S. in Data Science – Curriculum

M.S. in Data Science Curriculum

Over the course of 30-42 credits (depending on prior academic preparation), you will study theory, algorithms, and programming languages and apply the skills you learn in the classroom to solve real-world problems for external clients. Through small classes and a personalized learning experience, you will have the opportunity to work one-on-one with faculty as well as participate in various team projects.

Monmouth’s M.S. in Data Science program is divided into four areas: foundation courses, core courses, elective courses, and a practicum or thesis.

Foundation Courses (12 credits)

Your foundation courses will establish a background in programming, database design and management, and probability and statistics. You will study crucial data manipulation libraries available within R and Python, and will develop a working expertise in exploratory data analysis, as well as the theoretical and practical knowledge necessary for handling complex data sets.

Students may fulfill the foundation course requirements by taking the following courses as an undergraduate:

M.S. in Data Science
Foundation Course
Monmouth University Undergraduate Equivalent
DS 501 – Probability and Statistics for Data Science MA 220 – Probability and Statistics
DS 502 – Introduction to Computer Programming for Data Science I CS 175 – Introduction to Computer Science I
DS 503 – Introduction to Computer Programming for Data Science II CS 176 – Introduction to Computer Science II
DS 504 – Database Management CS 432 – Database Systems

Core Courses (15 Credits)

Your core courses will focus on the theory and practice of data science while providing instruction on data analysis methodologies and techniques, data handling, and the interpretation of analyses. Topics of study will include the methods and challenges of presenting data findings, ethical issues associated with data science, best practices for working with clients, and more.

Elective Courses (9 credits)

Apply your theoretical and practical knowledge by working with a team of students to solve a problem for an external client using real data. Students may repeat this course up to three times, with a new topic and external partner being featured each semester. This course will provide students with hands-on experience in a variety of areas where data science can be applied, such as business, social networks, journalism, sports analytics, and health care.

You may choose to substitute your third iteration of this course with elective offerings in advanced data science techniques or data analysis in business and finance.

Practicum or Thesis (6 credits)

Gain in-depth knowledge of the field of data science by participating in a practicum or completing a thesis. The practicum course offers an additional opportunity to engage in a team-based data science project, while the thesis allows for a deep dive into a specialized data science topic that culminates in a dissertation.

Both options provide you with the ability to create, analyze, and critically evaluate different solutions to data science problems while contributing to research in the field.

Professor Jay Wang's class Real-time Software Design and Implementation