2025–2026 Bulletin Addendum MS in Applied Data Analytics

The Master of Science (MS) in Applied Data Analytics program provides students with solid knowledge of the foundations of data analytics and emphasizes the presentation and discussion of the latest industry tools and approaches within an academically rigorous framework. The curriculum provides a thorough immersion in concepts and techniques for organizing, cleaning, analyzing, and representing/visualizing large amounts of data. Students will be exposed to various database systems, data-mining tools, data visualization tools and packages, Python packages, R packages, and cloud services. The knowledge of analytics tools combined with an understanding of data-mining and machine-learning approaches will enable students to critically analyze real-world problems and understand the possibilities and limitations of analytics applications.

This program requires an average of 8–20 months to complete. Students may begin the program in the fall, spring, or summer term.

Learning Outcomes

• Knowledge of the foundations of applied probability and statistics and their relevance in day-to-day data analysis.
• The ability to apply various data visualization techniques using real-world data sets and analyze the graphs and charts.
• Demonstrate knowledge of web analytics and metrics, procuring and processing unstructured text/data, and the ability to investigate hidden patterns.
• Knowledge-discovery skills using data-mining techniques and tools over large amounts of data.
• The ability to implement machine learning algorithms and their pertinence in real-world applications.
• Demonstrate comprehensive knowledge of data analytics techniques, skills, and critical thinking, and an understanding of the possibilities and limitations of their applications.

Admissions Information

For current admissions information, please visit the Metropolitan College website.

Prerequisites

Applicants to the program are required to have a bachelor’s degree in any discipline from a regionally accredited institution. Students with limited academic background in information technology, computer science, and mathematics may be required to enroll in complementary online preparatory labs (PrepLabs).

Preparatory Labs (online, non-unit)

• PrepLab 1: Core Mathematical Concepts
• PrepLab 2: Foundations of Probability and Statistics
• PrepLab 3: Database Fundamentals

Degree Requirements

Students are required to complete the foundation courses (unless exempted for approved equivalent academic background), core courses, and either general electives or concentration courses. Students who want to earn one or more concentration(s) must satisfy the stated requirements of each concentration they wish to pursue.

A total of 10 courses (40 units) is required. Students exempted from Foundation Courses will complete a total of eight courses (32 units).

Foundation Courses (two courses/8 units)

Upon admission, qualified students may be exempted from taking one or both foundation course requirements based on previous academic background in information technology, computer science, and mathematics. Foundation courses must be completed within the first term of study.

• MET CS 521 Information Structures with Python
• MET CS 526 Data Structures and Algorithms

Core Courses (four courses/16 units)

• MET CS 544 Foundations of Analytics and Data Visualization or MET CS 550 Computational Mathematics for Machine Learning
• MET CS 555 Foundations of Machine Learning
• MET CS 677 Data Science with Python
• MET CS 688 Web Mining and Graph Analytics or MET CS 699 Data Mining

General Electives (four courses/16 units)

Students who choose not to complete a concentration must select four courses (16 units) in addition to the MS in Applied Data Analytics foundation courses (if applicable) and core courses:

• MET CS 664 Artificial Intelligence
• MET CS 669 Database Design and Implementation for Business
• MET CS 688 Web Mining and Graph Analytics
• MET CS 689 Designing and Implementing a Data Warehouse
• MET CS 699 Data Mining
• MET CS 766 Deep Reinforcement Learning
• MET CS 767 Advanced Machine Learning and Neural Networks
• MET CS 777 Big Data Analytics
• MET CS 779 Advanced Database Management
• MET CS 787 AI and Cybersecurity
• MET CS 788 Generative AI
• MET CS 790 Computer Vision in AI

Concentrations

Master’s Thesis Option (two courses/8 units)

Students have the option to complete a master’s thesis by taking two master’s thesis courses (8 units) in addition to the program’s 10-course (40 units) requirement. The thesis must be completed within 12 months and is available to MS in Applied Data Analytics candidates who have completed at least four courses toward their degree (not including foundation courses) and have a grade point average (GPA) of 3.7 or higher. Students are responsible for finding a thesis advisor and principal readers within the department. The advisor must be a full-time faculty member; the principal readers may be part-time faculty. Department approval is required.

• MET CS 810 Master’s Thesis in Computer Science 1
• MET CS 811 Master’s Thesis in Computer Science 2

Declaration of More Than One Concentration

Students in the MS in Applied Data Analytics program have the option to concentrate in more than one area for their MS program. Each concentration must be finished before the student officially graduates from their program. No additional concentration may be added after graduation. In the case of some courses overlapping between one or more concentrations, only two courses may count toward both concentrations. If more than two courses overlap, the student must take electives in their place so that each concentration is completed.