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MS in Applied Data Analytics

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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.

Learning Outcomes

  • Demonstrate knowledge of the foundations of applied probability and statistics and their relevance in day-to-day data analysis.
  • Comprehend computing concepts and application requirements involving massive computing needs and data storage.
  • 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.
  • Exhibit knowledge-discovery skills using data-mining techniques and tools over large amounts of data.
  • Apply 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 from a regionally accredited institution. Applicants are not required to have a degree in computer science for entry to a program within our Department of Computer Science. Upon review of your application, the department will determine if the completion of prerequisite coursework will be required, based on your academic and professional background in information technology, computer science, and mathematics. The course MET CS 300 Introduction to Software Development (offered online only) may be required before admission into the Master of Science in Applied Data Analytics program, in addition to the following prerequisite courses:

  • MET CS 520 Information Structures with Java
    or     MET CS 521 Information Structures with Python*
  • MET CS 526 Data Structures and Algorithms
  • MET CS 546 Introduction to Probability and Statistics
  • MET CS 579 Database Management
    or     MET CS 669 Database Design and Implementation for Business

*Students are strongly encouraged to take MET CS 521, unless they already have a Python background and want to expand their Java skills.

Degree Requirements

A total of eight courses (32 credits) is required:

Core Curriculum (six courses/24 credits)

  • MET CS 544 Foundations of Analytics and Data Visualization
  • MET CS 555 Foundations of Machine Learning
  • MET CS 566 Analysis of Algorithms
  • MET CS 677 Data Science with Python
  • MET CS 688 Web Mining and Graph Analytics
  • MET CS 699 Data Mining

General Electives (two courses/8 credits)

Choose two of the following:

  • MET CS 550 Computational Mathematics for Machine Learning
  • MET CS 689 Designing and Implementing a Data Warehouse
  • MET CS 767 Advanced Machine Learning and Neural Networks
  • MET CS 777 Big Data Analytics
  • MET CS 779 Advanced Database Management
  • MET MA 582 Mathematical Statistics

Related Bulletin Pages

Beyond the Bulletin