{"id":99345,"date":"2025-08-18T14:37:21","date_gmt":"2025-08-18T18:37:21","guid":{"rendered":"https:\/\/www.bu.edu\/academics\/?page_id=99345"},"modified":"2025-08-19T13:33:22","modified_gmt":"2025-08-19T17:33:22","slug":"ms-in-applied-data-analytics","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/academics\/addendum\/met\/ms-in-applied-data-analytics\/","title":{"rendered":"<strong class=\"addendum\">2025\u20132026 Bulletin Addendum<\/strong> MS in Applied Data Analytics"},"content":{"rendered":"<div class=\"ad-update\">\n<p><em><span>This Addendum entry reflects the following changes to an <a href=\"https:\/\/www.bu.edu\/academics\/met\/programs\/computer-science\/master-of-science-in-applied-data-analytics\/\">existing degree program<\/a>:<\/span><\/em><\/p>\n<ul>\n<li><i>Addition of concentrations<\/i><\/li>\n<li><em>Updated learning outcomes and requirements<\/em><\/li>\n<\/ul>\n<p><em>Effective date: <strong>September 1, 2025<\/strong><\/em><\/p>\n<\/div>\n<div class=\"sidebar\">\n<h3>Contact<\/h3>\n<p>For contact information, please visit the Metropolitan College <a href=\"http:\/\/www.bu.edu\/met\/contact\/\">website<\/a>.<\/p>\n<\/div>\n<p>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.<\/p>\n<p>This program requires an average of 8\u201320 months to complete. Students may begin the program in the fall, spring, or summer term.<\/p>\n<h2>Learning Outcomes<\/h2>\n<ul>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">Knowledge of the foundations of applied probability and statistics and their relevance in day-to-day data analysis.<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">The ability to apply various data visualization techniques using real-world data sets and analyze the graphs and charts.<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">Demonstrate knowledge of web analytics and metrics, procuring and processing unstructured text\/data, and the ability to investigate hidden patterns.<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">Knowledge-discovery skills using data-mining techniques and tools over large amounts of data.<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">The ability to implement machine learning algorithms and their pertinence in real-world applications.<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">Demonstrate comprehensive knowledge of data analytics techniques, skills, and critical thinking, and an understanding of the possibilities and limitations of their applications.<\/li>\n<\/ul>\n<h2>Admissions Information<\/h2>\n<p>For current admissions information, please visit the Metropolitan College <a href=\"https:\/\/www.bu.edu\/met\/admissions\" data-gc-link=\"https:\/\/www.bu.edu\/met\/admissions\">website<\/a>.<\/p>\n<h2>Prerequisites<\/h2>\n<p>Applicants to the program are required to have a bachelor\u2019s degree <span>in any discipline<\/span> from a regionally accredited institution. <span>Students with limited academic background in information technology, computer<\/span> <span>science, and mathematics may be required to enroll in complementary online preparatory labs (PrepLabs).<\/span><\/p>\n<p style=\"font-weight: 400;\"><strong>Preparatory Labs (online, non-unit)<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span> PrepLab 1: Core Mathematical Concepts<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> PrepLab 2: Foundations of Probability and Statistics<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> PrepLab 3: Database Fundamentals<\/span><\/li>\n<\/ul>\n<h2>Degree Requirements<\/h2>\n<p style=\"font-weight: 400;\"><span>Students are required to complete the foundation courses (unless exempted for approved equivalent<\/span> <span>academic background), core courses, and either general electives or concentration courses. Students<\/span> <span>who want to earn one or more concentration(s) must satisfy the stated requirements of each<\/span> <span>concentration they wish to pursue.<\/span><\/p>\n<p style=\"font-weight: 400;\">A total of <span>10 <\/span>courses (<span>40<\/span> units) is required<span>. Students exempted from Foundation Courses will <\/span><span>complete a total of eight courses (32 units).<\/span><\/p>\n<h3>Foundation Courses (two courses\/8 units)<\/h3>\n<p style=\"font-weight: 400;\"><span>Upon admission, qualified students may be exempted from taking one or both foundation course <\/span><span>requirements based on previous academic background in information technology, computer science<\/span><span>, <\/span><span>and mathematics. Foundation courses must be completed within the first term of study.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span> MET CS 521 Information Structures with Python<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 526 Data Structures and Algorithms<\/span><\/li>\n<\/ul>\n<h3>Core Courses (four courses\/16 units)<\/h3>\n<ul>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">MET CS 544 Foundations of Analytics and Data Visualization <span style=\"font-weight: 400;\"><span>or MET CS 550 Computational Mathematics for Machine Learning<\/span><\/span><\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">MET CS 555 Foundations of Machine Learning<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">MET CS 677 Data Science with Python<\/li>\n<li data-gc-list-depth=\"1\" data-gc-list-style=\"bullet\">MET CS 688 Web Mining and Graph Analytics or\u00a0MET CS 699 Data Mining<\/li>\n<\/ul>\n<h3>General Electives (four courses\/16 units)<\/h3>\n<p style=\"font-weight: 400;\"><span>Students who choose not to complete a concentration must select four courses (16 units) in addition<\/span> <span>to the MS in Applied Data Analytics foundation courses (if applicable) and core courses:<\/span><\/p>\n<ul style=\"font-weight: 400;\">\n<li><span>MET CS 664 Artificial Intelligence<\/span><\/li>\n<li><span>MET CS 669 Database Design and Implementation for Business<\/span><\/li>\n<li><span>MET CS 688 Web Mining and Graph Analytics<\/span><\/li>\n<li>MET CS 689 Designing and Implementing a Data Warehouse<\/li>\n<li><span>MET CS 699 Data Mining<\/span><\/li>\n<li><span>MET CS 766 Deep Reinforcement Learning<\/span><\/li>\n<li>MET CS 767 Advanced Machine Learning and Neural Networks<\/li>\n<li>MET CS 777 Big Data Analytics<\/li>\n<li>MET CS 779 Advanced Database Management<\/li>\n<li><span>MET CS 787 AI and Cybersecurity<\/span><\/li>\n<li><span>MET CS 788 Generative AI<\/span><\/li>\n<li><span>MET CS 790 Computer Vision in AI<\/span><\/li>\n<\/ul>\n<h2>Concentrations<\/h2>\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">AI &amp; Machine Learning<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\">\n<p style=\"font-weight: 400;\"><span>The Concentration in AI &amp; Machine Learning provides intensive exploration of the theory and practice of <\/span><span>neural nets, generative AI, automated reasoning, AI security, intelligent image processing, and reinforcement <\/span><span>learning. AI ethics, as well as supervised and unsupervised learning are studied. This concentration enables <\/span><span>graduates to design and implement intelligent applications in engineering, business, and industry.<\/span><\/p>\n<h4>Learning Outcomes<\/h4>\n<ul>\n<li style=\"font-weight: 400;\"><span> Advanced Machine Learning and Deep Learning: Students will be able to solve complex problems such<\/span> <span>as computer vision, natural language processing, and speech recognition using machine learning<\/span> <span>algorithms, including supervised and unsupervised learning models, neural network architectures, and<\/span> <span>deep learning techniques.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> Artificial Intelligence Development: Students will be able to design and implement agents and<\/span> <span>algorithms for self-learning systems, leveraging AI models for data representation and prediction,<\/span> <span>implementing evolutionary and genetic algorithms for optimization, and developing software systems<\/span> <span>that incorporate AI models to enhance capabilities.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> Ethical AI and Communication: Students will be able to evaluate the ethical implications of AI systems, <\/span><span>ensuring model fairness, accountability, and transparency, and effectively communicating technical AI <\/span><span>concepts to non-technical stakeholders.<\/span><\/li>\n<\/ul>\n<h4>Concentration Requirements<\/h4>\n<p style=\"font-weight: 400;\"><span>In addition to the MS in Applied Data Analytics foundation courses (if applicable) and core courses (four <\/span><span>courses\/16 units), students pursuing a concentration in AI &amp; Machine Learning must choose four courses (16 <\/span><span>units) from the list below:<\/span><\/p>\n<p style=\"font-weight: 400;\"><span>(four courses\/16 units)<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span> MET CS 664 Artificial Intelligence<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 766 Deep Reinforcement Learning<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 767 Advanced Machine Learning and Neural Networks<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 787 AI and Cybersecurity<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 788 Generative AI<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 790 Computer Vision in AI<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><\/div>\n<\/div>\n<\/span><\/p>\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Data Engineering<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\">\n<p style=\"font-weight: 400;\"><span>The Concentration in Data Engineering equips students with the processes and tools necessary to ingest and<\/span><span>analyze the vast amounts of information provided by large amounts of data. By focusing on data quality and<\/span><span>security, the concentration facilitates the creation of data pipelines for transforming the raw data into various <\/span><span>formats required for data analysis. The concentration enables graduates to effectively utilize and implement <\/span><span>data-driven decisionmaking procedures for various types of applications.<\/span><\/p>\n<h4>Learning Outcomes<\/h4>\n<ul>\n<li style=\"font-weight: 400;\"><span> Students will have the know-how of database modeling and design, implementation, distributed<\/span> <span>databases, object-oriented and object-relational databases, databases for web applications, and<\/span> <span>typical data-mining methods.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> Students will gain proficiency in designing, implementing, and performance-tuning different types of <\/span><span>databases, as well as performing data-mining tasks on various data types.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> Students will have the comprehension of computing concepts and applications requirements involving <\/span><span>massive computing needs and data storage.<\/span><\/li>\n<\/ul>\n<h4>Concentration Requirements<\/h4>\n<p><span>In addition to the MS in Applied Data Analytics foundation courses (if applicable) and core courses (four <\/span><span>courses\/16 units), students pursuing a concentration in Data Engineering must choose four courses (16 units) from the list below:<\/span><\/p>\n<p style=\"font-weight: 400;\"><span>(four courses\/16 units)<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span> MET CS 669 Database Design and Implementation for Business<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 674 Database Security<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 688 Web Mining and Graph Analytics<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 699 Data Mining<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 689 Designing and Implementing a Data Warehouse<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 777 Big Data Analytics<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 779 Advanced Database Management<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><\/div>\n<\/div>\n<\/span><\/p>\n<h2>Master\u2019s Thesis Option (two courses\/8 units)<\/h2>\n<p style=\"font-weight: 400;\"><span>Students have the option to complete a master\u2019s thesis by taking two master&#8217;s thesis courses (8 units)<\/span> <span>in addition to the program\u2019s 10-course (40 units) requirement. The thesis must be completed within<\/span> <span>12 months and is available to MS in Applied Data Analytics candidates who have completed at least<\/span> <span>four courses toward their degree (not including foundation courses) and have a grade point average<\/span> <span>(GPA) of 3.7 or higher. Students are responsible for finding a thesis advisor and principal readers within<\/span> <span>the department. The advisor must be a full-time faculty member; the principal readers may be <\/span><span>part-time<\/span> <span>faculty. Department approval is required.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span> MET CS 810 Master\u2019s Thesis <\/span><span>in Computer Science 1<\/span><\/li>\n<li style=\"font-weight: 400;\"><span> MET CS 811 Master\u2019s Thesis <\/span><span>in Computer Science 2<\/span><\/li>\n<\/ul>\n<h2>Declaration of More Than One Concentration<\/h2>\n<p style=\"font-weight: 400;\"><span>Students in the MS in Applied Data Analytics program have the option to concentrate in more than<\/span> <span>one area for their MS program. Each concentration must be finished before the student officially<\/span> <span>graduates from their program. No additional concentration may be added after graduation. In the<\/span> <span>case of some courses overlapping between one or more concentrations, only two courses may count<\/span> <span>toward both concentrations. If more than two courses overlap, the student must take electives in<\/span> <span>their place so that each concentration is completed.<\/span><\/p>\n<ul style=\"font-weight: 400;\"><\/ul>\n<ul style=\"font-weight: 400;\"><\/ul>\n<ul style=\"font-weight: 400;\"><\/ul>\n<ul style=\"font-weight: 400;\"><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This Addendum entry reflects the following changes to an existing degree program: Addition of concentrations Updated learning outcomes and requirements Effective date: September 1, 2025 Contact For contact information, please visit the Metropolitan College website. The Master of Science (MS) in Applied Data Analytics program provides students with solid knowledge of the foundations of data [&hellip;]<\/p>\n","protected":false},"author":4502,"featured_media":0,"parent":99343,"menu_order":6,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/pages\/99345"}],"collection":[{"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/users\/4502"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/comments?post=99345"}],"version-history":[{"count":6,"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/pages\/99345\/revisions"}],"predecessor-version":[{"id":99371,"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/pages\/99345\/revisions\/99371"}],"up":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/pages\/99343"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/academics\/wp-json\/wp\/v2\/media?parent=99345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}