ARCH Innovation Exchange / Division of Integrative Programs
Dr. Lawrence Udeigwe
Director of Integrative Programs, ARCH Innovation Exchange
The Division of Integrative Programs (DIP), housed within the ARCH Innovation Exchange, serves as the academic home for interdisciplinary programs that operate across traditional departmental and school boundaries. The Division supports fields of study that integrate knowledge, methods, and perspectives from multiple disciplines to address complex contemporary challenges.
DIP programs are collaboratively developed and delivered by faculty from across the University and are jointly administered through the ARCH Innovation Exchange and the Kakos School of Arts and Sciences. By combining technical expertise, analytical thinking, creativity, and human-centered perspectives, these programs prepare students to work effectively across disciplines and adapt to emerging professional fields.
The Division provides academic coordination, advising support, assessment, and program development for its interdisciplinary offerings while maintaining strong partnerships with participating departments and schools throughout the University.
Majors
Minors
B.S in Computational Neuroscience
The B.S. in Computational Neuroscience is an interdisciplinary program focused on understanding brain and cognitive function through experimental and theoretical approaches. Students examine how neural systems give rise to perception, cognition, and behavior, and how biological and behavioral data can be analyzed and explained using computational and mathematical models.
The program builds a strong foundation in the natural sciences, mathematics, statistics, and programming while studying the brain across multiple levels—from neural circuits and systems to cognition and behavior. Coursework in behavioral, systems, and computational neuroscience combines experimental study with modeling, simulation, and data analysis.
Students develop skills in experimental design, quantitative reasoning, and computational modeling, with opportunities to explore areas such as physiology, sensation and perception, neurobiology, machine learning, image analysis, and neurotechnology.
The major prepares students for graduate study in neuroscience, cognitive science, psychology, and biomedical fields, as well as careers in neurotechnology, artificial intelligence, data science, and brain- and behavior-related health fields. Students also engage with the ethical and societal dimensions of neuroscience and neurotechnology, including mental health, human–machine interaction, and the responsible use of data-driven models of the mind.
Curriculum
| Natural Science Foundations (25 Credits) | ||
| BIOL 111 & BIOL 191 | General Biology I and General Biology I Lab | 4 |
| BIOL 112 & BIOL 192 | General Biology II and General Biology II Lab | 4 |
| BIOL 217 & BIOL 297 | Genetics and Genetics Lab | 4 |
| CHEM 101 & CHEM 103 | General Chemistry I and General Chemistry Laboratory I | 4 |
| PHYS 101 & PHYS 191 | Physics I and Physics I Lab | 4 |
| or PHYS 107 & PHYS 193 | Introduction to Physics I and Introduction to Physics I Lab | |
| PHYS 102 & PHYS 192 | Physics II and Physics II Lab | 4 |
| or PHYS 108 & PHYS 194 | Introduction to Physics II and Introduction to Physics II Lab | |
| SCI 100 | Science Orientation Seminar I | 1 |
| Mathematical & Computational Foundations (21 Credits) | ||
| MATH 185 | Calculus I | 4 |
| MATH 186 | Calculus II | 4 |
| MATH 285 | Calculus III | 4 |
| MATH 336 | Applied Statistics | 3 |
| CMPT 101 | Computer Science I | 3 |
| CMPT 102 | Computer Science II | 3 |
| Neuroscience Core (19 Credits) | ||
| PSYC 150 | Roots: Psychology | 3 |
| PSYC 435 | Behavioral Neuroscience | 3 |
| PSYC 340 | Cognition and Learning | 3 |
| BIOL 405 & BIOL 485 | Neurobiology and Neurobiology Laboratory | 4 |
| NEUR 360 | (Systems Nueroscience) | |
| NEUR 490 | (Senie Seminar & Capstone Project) | |
| Computational & Modelling Methods (6 Credits) | ||
| MATH 457 | Machine Learning | 3 |
| NEUR 460 | (Computational Methods in Neuroscience) | |
| Cross-disciplinary Electives (3-4 Credits) | ||
| PSYC 332 | Artificial Psychology | 3-4 |
| or PSYC 467 | Sensation and Perception | |
| or BIOL 320 & BIOL 390 | Animal Physiology and Animal Physiology Laboratory | |
| or BIOL 321 & BIOL 391 | Molecular Cell Biology and Molecular Cell Biology Lab | |
| or BIOL 326 & BIOL 396 | Animal Behavior and Animal Behavior Laboratory | |
| or CMPT 335 | Discrete Structures | |
| or CMPT 465 | Neural Networks and Learning Systems | |
| or CMPT 485 | Deep Learning & Generative AI | |
| or MATH 286 | Differential Equations | |
| or MATH 331 | Probability | |
| or MATH 372 | Linear Algebra I | |
| or MATH 456 | Mathematical Modeling | |
| or EECE 404 | Bioinstrumentation | |
| or EECE 457 | Bioinspired Robotic Vision Systems | |
| Liberal Arts (39 Credits) | ||
| PHIL 201 | Ethics | 3 |
| KSAS Core Curriculum | 36 | |
| Free Electives (9-10 Credits) | 9-10 | |
Plan of Study
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| ENGL 110 | 3 | ENGL 150 | 3 |
| MATH 185 | 4 | MATH 186 | 4 |
| BIOL 111 & BIOL 191 | 4 | BIOL 112 & BIOL 192 | 4 |
| PSYC 150 | 3 | RELS 110 | 3 |
| SCI 100 | 1 | CMPT 101 | 3 |
| 15 | 17 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| PSYC 435 | 3 | NEUR 360 (Systems Neuroscience) | 3 |
| PHYS 101 & PHYS 191 | 4 | PHYS 102 & PHYS 192 | 4 |
| CHEM 101 & CHEM 103 | 4 | BIOL 217 & BIOL 297 | 4 |
| MATH 285 | 4 | LLRN 105 | 3 |
| CMPT 102 | 3 | PHIL 150 | 3 |
| 18 | 17 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| PSYC 340 | 3 | MATH 336 | 3 |
| PHIL 201 | 3 | BIOL 405 & BIOL 485 | 4 |
| MATH 457 | 3 | MUSC 150 or ART 150 | 3 |
| HIST 150 | 3 | RELS 2XX | 3 |
| FREE ELECTIVE | 3 | NEUR 460 (Computational Methods in Neuroscience) | 3 |
| 15 | 16 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| MODERN LANGUAGES | 3 | NEUR 490 (Senior Seminar & Capstone Project) | 3 |
| RELS 3XX | 3 | MODERN LANGUAGES | 3 |
| SOCIAL SCIENCES | 3 | FREE ELECTIVES | 6 |
| NEUR ELECTIVES | 4 | ||
| 13 | 12 | ||
| Total Credits: 123 | |||
B.S. in Data Science
The Bachelor of Science in Data Science is an interdisciplinary major that brings together the expertise of the Kakos School of Arts and Sciences, the School of Engineering, and the O'Malley School of Business. By integrating mathematics, computing, business, and engineering, the program prepares students to thrive in a world increasingly driven by data and artificial intelligence.
Students develop a rigorous foundation in mathematics, statistics, programming, databases, and machine learning while learning to analyze complex data, build predictive models, design intelligent systems, and solve real-world problems. Through elective clusters, students can apply data science across diverse fields, including healthcare, finance, environmental science, engineering, business, and public policy.
The program culminates in a senior capstone project that integrates technical knowledge with domain expertise. Consistent with Manhattan University's mission, students also examine the ethical and societal implications of data and AI, graduating with the technical skills, critical thinking, and professional judgment needed to lead in a data-driven world.
Curriculum
| Foundations for Data Science (21 Credits) | ||
| MATH 185 | Calculus I | 4 |
| MATH 186 | Calculus II | 4 |
| MATH 285 | Calculus III | 4 |
| MATH 243 | Foundations for Higher Mathematics | 3 |
| CMPT 101 | Computer Science I | 3 |
| CMPT 102 | Computer Science II | 3 |
| Data Science Core (24 Credits) | ||
| MATH 331 | Probability | 3 |
| MATH 336 | Applied Statistics | 3 |
| MATH 372 | Linear Algebra I | 3 |
| MATH 457 | Machine Learning | 3 |
| CMPT 238 | Data Structures and Algorithms - I | 3 |
| CMPT 258 | Database Systems I | 3 |
| DTSC 311 | (Computational Methods for Data Sci) | 3 |
| DTSC 490 | (Data Science Capstone Project) | 3 |
| Data Science Electives (12 Credits) | ||
| CMPT 363 | Data Mining | 3 |
| or BUAN 410 | Data Mining for Business Applications | |
| or EECE 478 | Applied Data Mining for Engineers | |
| or MATH 456 | Mathematical Modeling | |
| CMPT 465 | Neural Networks and Learning Systems | 3 |
| or CMPT 485 | Deep Learning & Generative AI | |
| BUAN 440 | Big Data Analytics for Business Analytics | 3 |
| BUAN 405 | Data Privacy | 3 |
| or EECE 403 | Trustworthy AI Applications in Electrical & Computer Engineering | |
| Natural Sciences (9 Credits) | ||
| SCI 100 | Science Orientation Seminar I | 1 |
| BIOL 111 & BIOL 191 | General Biology I and General Biology I Lab | 4 |
| or PHYS 101 & PHYS 191 | Physics I and Physics I Lab | |
| or CHEM 101 & CHEM 103 | General Chemistry I and General Chemistry Laboratory I | |
| BIOL 112 & BIOL 192 | General Biology II and General Biology II Lab | 4 |
| or PHYS 102 & PHYS 192 | Physics II and Physics II Lab | |
| or CHEM 102 & CHEM 103 | General Chemistry II and General Chemistry Laboratory I | |
| Ethics (3 Credits) | ||
| PHIL 201 | Ethics | 3 |
| KSAS Core Curriculum (39 Credits) | 39 | |
| Free Electives (15 Credits) | 15 | |
Plan of Study
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 185 | 4 | MATH 186 | 4 |
| CMPT 101 | 3 | CMPT 102 | 3 |
| ENGL 110 | 3 | ENGL 150 | 3 |
| LLRN 105 | 3 | RELS 110 | 3 |
| SCI 100 | 1 | Modern Languages II | 3 |
| Modern Languages I | 3 | ||
| 17 | 16 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| MATH 243 | 3 | MATH 336 | 3 |
| MATH 285 | 4 | MATH 372 | 3 |
| CMPT 238 | 3 | PHIL 201 | 3 |
| PHIL 150 | 3 | DTSC 311 | 3 |
| Natural Sciences I | 4 | Natural Sciences II | 4 |
| 17 | 16 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| MATH 331 | 3 | CMPT 258 | 3 |
| MATH 457 | 3 | Data Science Elective | 3 |
| Data Science Elective | 3 | Data Science Elective | 3 |
| Free Elective | 3 | MUSC 150 or ART 150 | 3 |
| HIST 150 | 3 | RELS 2XX | 3 |
| 15 | 15 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| Data Science Elective | 3 | Free Elective | 3 |
| RELS 3XX | 3 | Free Elective | 3 |
| Free Elective | 3 | Social Science | 3 |
| Free Elective | 3 | DTSC 490 | 3 |
| Social Science | 3 | ||
| 15 | 12 | ||
| Total Credits: 123 | |||
B.S in Integrated Marketing Communication
The Integrated Marketing Communication (IMC) major brings together the strengths of the O’Malley School of Business and the Kakos School of Arts and Sciences in one interdisciplinary degree. Designed for students interested in the intersection of business, communication, creativity, and technology, the program prepares students to understand audiences, build brands, create compelling content, and develop communication strategies across digital, social, and traditional media.
Students study both the strategic side of marketing and the creative side of communication, developing skills in consumer behavior, marketing strategy, persuasive communication, digital media, content creation, business analytics, and emerging technologies. The program combines the AACSB-accredited business core with coursework in communication and media, giving students the ability to connect creative ideas to broader organizational and business goals.
Students can further shape the major through one of two specialized tracks. The Content Creation track emphasizes multimedia storytelling, visual and audio production, social media content, and brand communication. The Artificial Intelligence in Marketing and Communication track focuses on analytics, AI-assisted strategy, data-driven marketing, predictive analysis, and emerging digital technologies.
The program culminates in an applied capstone experience in which students bring together marketing strategy, audience research, communication, media production, and analytics to develop and evaluate an integrated marketing communication campaign. Graduates are prepared for careers in areas such as marketing, advertising, public relations, brand strategy, social media, digital marketing, content creation, marketing analytics, and media strategy across industries ranging from business and technology to healthcare, entertainment, nonprofit organizations, and consumer brands.
| Modified Liberal Arts Core (51 Credits) | ||
| ENGL 110 | First Year Composition | 3 |
| RELS 110 | Lasallian Seminar | 3 |
| RELS 200 | Special Topic: in Religion | 3 |
| RELS 300 | Special Topic | 3 |
| ECON 203 | Microeconomics | 3 |
| ECON 204 | Macroeconomics | 3 |
| Select Two: | 6 | |
| Roots: Psychology | ||
| Roots: Sociology | ||
| Roots: Government | ||
| MATH 153 | Finite Mathematics for Business Decisions | 3 |
| MATH 154 | Calculus for Business Decisions | 3 |
| SCI XXX | 3 | |
| LLRN 105 | Interdisciplinary Liberal Arts Seminar | 3 |
| CIS 110 | Introduction to Information Systems | 3 |
| ENGL 211 | Business Communication | 3 |
| COMM 150 | Roots: Communication | 3 |
| ART 145 | Fundamentals of Art & Design | 3 |
| PHIL 150 | Roots: Philosophy | 3 |
| Business Core (AASCB) (27 Credits) | ||
| ACCT 201 | Principles of Accounting I | 3 |
| ACCT 202 | Principles of Accounting II | 3 |
| BUAN 227 | Business Statistics | 3 |
| FIN 301 | Principles of Business Finance | 3 |
| LAW 203 | Business Law I | 3 |
| MKTG 201 | Essentials of Marketing | 3 |
| MGMT 201 | Introduction to Management | 3 |
| MGMT 307 | Operations and Quality Management | 3 |
| MGMT 406 | Strategic Management | 3 |
| Major Courses (45 Credits) | ||
| MKTG 303 | Marketing Research | 3 |
| MKTG 307 | Consumer Behavior | 3 |
| MKTG 412 | International Marketing | 3 |
| MGKT 403 | 3 | |
| or COMM 320 | Strategic Planning in Public Relations | |
| COMM 101 | Introduction to Communication and Media | 3 |
| COMM 201 | Ethics in Communication & Media | 3 |
| COMM 218 | Introduction to Integrated Marketing Communications | 3 |
| COMM 304 | Digital Storytelling | 3 |
| or COMM 305 | Digital Print Design | |
| COMM 309 | Digital Media Writing for Marketing Communications | 3 |
| MBA 645 | Special Topics in Business | 3 |
| or COMM 412 | Digital Marketing Communications | |
| Capstone (select one): | ||
| Senior Seminar | ||
MKTG 460 | ||
INTERSHIP | ||
| BUAN 167 | AI Essentials for Business Applications | 3 |
| Three electives (examples): | ||
| MKTG 421 | Contemporary Marketing Issues | 3 |
| COMM 420 | Advanced Public Relations | 3 |
| COMM 413 | News Production for Social Media | 3 |
| COMM 414 | Advertising Campaigns | 3 |
| MKTG 308 | Sales Management | 3 |
| MKTG 305 | Direct Response Marketing | 3 |
| MKTG 404 | Advertising and Communication Management | 3 |
| MKTG 315 | Retail Management | 3 |
| Tracks (Select three courses from one track) (9 Credits) | ||
| Track 1: Content Creation | ||
| ART 212 | Art of Digital Photography | 3 |
| ART 213 | Digital Drawing | 3 |
| ART 309 | Animation | 3 |
| ART 380 | Digital Video Art: Editing and Production | 3 |
| COMM 214 | Magazine Writing | 3 |
| COMM 308 | Studio Television Production | 3 |
| COMM 317 | Audio Production | 3 |
| MUSC 390 | Digital Audio Recording and Editing | 3 |
| MUSC 393 | Audio Mixing & Music Prod | 3 |
| MUSC 395 | Acoustics & Sound Control | 3 |
| Track 2: AI | ||
| CIS 205 | Introduction to Programming for Business Applications | 3 |
| or BUAN 205 | Introduction to Programming for Business Applications | |
| CIS 211 | System Administration and Cloud Computing for Business Applications | 3 |
| or BUAN 211 | ||
| BUAN 410 | Data Mining for Business Applications | 3 |
| BUAN 427 | Artificial lntelligence and Machine Learning | 3 |
| CIS 431 | Analysis, Design, and Implementation of Information Systems | 3 |
| Total 132 credits | ||
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| ENGL 110 or RELS 110 | 3 | ENGL 110 or RELS 110 | 3 |
| MATH 153 | 3 | MATH 154 | 3 |
| PSYC 150, SOC 150, or POSC 150 | 3 | PSYC 150, SOC 150, or POSC 150 | 3 |
| ART 145 | 3 | COMM 150 | 3 |
| COMM 101 | 3 | MKTG 201 | 3 |
| 15 | 15 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| CIS 110 | 3 | BUAN 227 | 3 |
| ACCT 201 | 3 | ACCT 202 | 3 |
| ECON 203 | 3 | ECON 204 | 3 |
| COMM 201 | 3 | COMM 218 | 3 |
| RELS 200 or ENGL 150 | 3 | MKTG 307 | 3 |
| RELS 200 or ENGL 150 | 3 | ||
| 15 | 18 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| MGMT 201 | 3 | MGMT 307 | 3 |
| LAW 203 | 3 | FIN 301 | 3 |
| BUAN 167 | 3 | RELS 300 | 3 |
| COMM 304 | 3 | COMM 309 | 3 |
| MKTG 303 | 3 | MKTG 412 | 3 |
| LLRN 105 | 3 | LLRN 105 or PHIL 150 | 3 |
| 18 | 18 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| COMM 320 or MKTG 403 | 3 | MBA 645 or COMM 412 | 3 |
| SCI XXX | 3 | COMM 409, MKTG 460, or INTERNSHIP | 3 |
| MGMT 406 | 3 | MAJOR ELECTIVE/TRACK ELECTIVE | 3 |
| MAJOR ELECTIVE/TRACK ELECTIVE | 3 | MAJOR ELECTIVE/TRACK ELECTIVE | 3 |
| MAJOR ELECTIVE/TRACK ELECTIVE | 3 | MAJOR ELECTIVE/TRACK ELECTIVE | 3 |
| MAJOR ELECTIVE/TRACK ELECTIVE | 3 | ||
| 18 | 15 | ||
| Total Credits: 132 | |||
Artificial Intelligence Minor
AI has become a foundational technology affecting nearly all academic disciplines and professional fields. While all students routinely use AI‑enabled tools, it should also be important to learn and understand AI fundamentals, including how such systems function, their limitations, and the ethical considerations surrounding their use. The Computer Science Department participates in this interdisciplinary minor.
The Minor in AI provides structured, interdisciplinary instruction in AI fundamentals to students from all majors. Its academic objective is to ensure that graduates possess a baseline understanding of AI concepts, design principles, and responsible application relevant to their fields of study.
The curriculum is designed to accommodate students with varied academic backgrounds. Thus, the curriculum explicitly recognizes that students enter the minor with varying levels of preparation. Multiple pathways are included to distinguish between students with prior coursework in programming and quantitative methods and those without such backgrounds.
Transfer Credit: At most one course transferred from another institution may be credited toward the credits required for a minor.
Application: You need to consult with the Chair of the respective Department in your school (the Computer Science Department for all KSAS students) and ask your academic adviser to assist you with a respective paperwork and adding minor to your Degree Works.
The minor consists of an AI core drawn from courses offered across the three participating schools, supplemented by prerequisite coursework where necessary. Students will complete either five or six courses, with a minimum of three drawn from the AI core.
Core Courses
| Data Mining | ||
| CMPT 363 | Data Mining | 3 |
| or BUAN 410 | Data Mining for Business Applications | |
| or EECE 478 | Applied Data Mining for Engineers | |
| Artificial Intelligence & Machine Learning | ||
| CMPT 420 | Artificial Intelligence | 3 |
| or MATH 457 | Machine Learning | |
| or BUAN 427 | Artificial lntelligence and Machine Learning | |
| or EECE 471 | Artificial Intelligence Applications in Electrical & Computer Engineering | |
| or MECH 442 | Artificial Intelligence Applications in Mechanical Engineering | |
| Artificial Neural Networks | ||
| CMPT 465 | Neural Networks and Learning Systems | 3 |
| or EECE 448 | Applied Machine Learning for Electrical & Computer Engineering | |
| Others | ||
| CMPT 471 | Parallel Computing | 3 |
| CMPT 485 | Deep Learning & Generative AI | 3 |
| MATH 455 | Operations Research | 3 |
| MATH 457 | Machine Learning | 3 |
| EECE 403 | Trustworthy AI Applications in Electrical & Computer Engineering | 3 |
| EECE 447 | Image Processing & Pattern Recognition | 3 |
| EECE 483 | Cognitive-AI Based Eng Design | 3 |
| EECE 494 | Special Topics in Artificial Intelligence (AI) in Electrical and Computer Engineering | 3 |
| MECH 438 | Operation Research | 3 |
| MECH 475 | Data Driven Problem Solving in Mechanical Engineering | 3 |
| CHML 241 | Data Analytics for Chemical Engineers | 3 |
Tracks
| 1. Track for KSAS students (except CMPT, MATH, PHYS, and GAME ) - 18 credits | ||
| CMPT 101 | Computer Science I | 3 |
| CMPT 102 | Computer Science II | 3 |
| CMPT 335 | Discrete Structures (Three courses from the Core ) | 9 |
| Three core courses (at least two from CMPT) | 9 | |
| 2. Track for Physics Majors - 15 credits | ||
| CMPT 102 | Computer Science II | 3 |
| CMPT 335 | Discrete Structures | 3 |
| Three core courses (at least two from CMPT) | 9 | |
| 3. Track MATH majors- 15 credits | ||
| CMPT 102 | Computer Science II | 3 |
| Four core courses (at least 2 from CMPT) | 12 | |
| 4. Track for CMPT majors GAME concentrators - 15 credits | ||
| Five core courses (3 must be CMPT) | 15 | |
| Remark: To avoid double dipping, CMPT majors cannot count more than one CMPT course taken for the Minor in AI towards their Major. | ||
| 5. Track for OMSB majors (except CIS and BUAN) - 18 credits | ||
| CIS 205 | Introduction to Programming for Business Applications | 3 |
| CIS 310 | Business Data and Information Management | 3 |
| CMPT 342 | Python Programming | 3 |
| Three core courses (one must be CMPT) | 9 | |
| 6. Track for BUAN majors -15 credits | ||
| BUAN 167 | AI Essentials for Business Applications | 3 |
| CMPT 342 | Python Programming | 3 |
| Three core courses (two must be CMPT) | 9 | |
| 7. Track for CIS majors -15 credits | ||
| BUAN 167 | AI Essentials for Business Applications | 3 |
| CMPT 342 | Python Programming | 3 |
| Three core courses (one must be CMPT) | 9 | |
| 8. Track for students in the School of Engineering (except Electrical/Computer) -16 credits | ||
| EECE 210 | Applied Software Engineering I | 3 |
| EECE 300 | Fundamentals of Electrical & Computer Engineering for Non-Engineering Programs | 4 |
| Three core core courses (one must be EECE) | 9 | |
Quality Science Minor
The Quality Science minor will expand opportunities for students who are interested in careers in the pharmaceutical and drug device industries. There is a great need in these industries to ensure that the medicines, dietary supplements, devices and other products meet quality and safety standards to build patient trust and improve global health. This minor will expose students to the practices and processes used by the industry to assure quality and safety.
The Quality Science minor is offered through a cooperative education model between industry partners and Manhattan University The industry partners are organized into the Pathway for Patient Health non-profit consortium (https://www.pathway4ph.org/). Students who successfully complete this minor with a grade of at least 70% in each Pathway course and receive a passing grade on the courses taken through Manhattan University will also receive a Certified Quality Science Professional (CQSP) micro-credential Students who earn grades of 95% or higher in all 3 Pathway courses, will receive their micro-credential with honors.
Pathway for Patient Health provides students enrolled in the minor with opportunities for mentoring and access to their hiring platform for internships and job opportunities. Pathway partners include Johnson and Johnson, Astra Zeneca, Baxter, Boston Scientific, Cook Medical, Illumina, Johnson and Johnson, Merck, Proctor and Gamble, Sanofi, Steris, Thermo-Fisher, the Wistar Institute and, many others pharmaceutical companies.
Job opportunities for students with the minor and CQSP micro-credential include:
-
Quality Assurance Auditor
-
Product Validation Manager
-
FDA Inspection Manager
-
Quality Control Laboratory Specialist
The 15-16 credit minor requires five courses: two in-person Manhattan University courses and three online, asynchronous courses developed by the Pathway Chief Quality Officer Team but offered as Manhattan University courses through Moodle.
Curriculum (15 or 16 credits):
| Quality Science Core: asynchronous courses developed by the Pathway Chief Quality Officer Team but offered as Manhattan University courses through Moodle. | ||
| SCI 206 | Global Regulatory & Legal Requirements of Quality | 3 |
| SCI 306 | Risk & Failure Analysis | 3 |
| SCI 307 | Product Development & Validation | 3 |
| Business Requirement - Select one of the following: | ||
| ACCT 201 | Principles of Accounting I | 3 |
| or ECON 150 | Roots: Economics | |
| or ECON 203 | Microeconomics | |
| or ECON 204 | Macroeconomics | |
| or CHML 461 | Industrial Practice in Pharmaceutical Industry | |
| Laboratory Science Requirement - Select one of the following: | ||
| BIOL 111 & BIOL 191 | General Biology I and General Biology I Lab | 4 or 3 |
| or BIOL 103 & BIOL 183 | Introduction to Biology and Introduction to Biology Lab | |
| or BIOL 225 & BIOL 295 | Microbiology and Microbiology Lab | |
| or CHEM 101 & CHEM 103 | General Chemistry I and General Chemistry Laboratory I | |
| or PHYS 101 & PHYS 191 | Physics I and Physics I Lab | |
| or SCI 203 | Topics in Science I | |
| or SCI 204 | Topics in Science II | |
Sustainability Studies Minor
The Minor in Sustainability Studies is an interdisciplinary program offered across all three of Manhattan University’s schools. It is designed to equip students with the knowledge and applied skills necessary to address complex environmental, economic, and social challenges. Drawing upon courses in environmental science, engineering, business, public health, economics, and public policy, the minor emphasizes systems thinking, ethical decision-making, and real-world problem solving.
Students complete a foundational course in environmental science and sustainability, disciplinary coursework within their home school, and interdisciplinary electives across schools. The program culminates in a capstone experience in which students work collaboratively to develop practical, evidence-based solutions to contemporary sustainability challenges, with particular attention to urban contexts and environmental justice.
This minor prepares students for careers and graduate study in fields including sustainable engineering, environmental policy, corporate sustainability, public health, environmental law, and urban planning.
Curriculum
| ENSC 101 | People,Planet & Sustainability | 3 |
| Two courses from the student's home school: | ||
| OMSB: | ||
| ECON 332 & ECON 432 | Introduction to Environmental Economics and Applied Environmental Economics | 6 |
| SOE: | ||
| ENGS 204 & MECH 481 | Environmental Engineering Principles I and Energy Management | 6 |
| KSAS: | ||
| PHP 206 & POSC 223 | Introduction to Public Health and Environmental Politics | 6 |
| One course that is not offered by the student's home school. All courses are 3 credits | ||
| OMSB Options: | ||
| ECON 332 | Introduction to Environmental Economics | 3 |
| or ECON 432 | Applied Environmental Economics | |
| SOE Options: | ||
| CEEN 307 | Hydraulic Design | 3 |
| or CEEN 308 | Reliability Analysis in Civil and Environmental Engineering | |
| or CEEN 309 | Environmental Law | |
| or CEEN 314 | Water & Wastewater Treatment Processes | |
| or CEEN 401 | Sustainable Water Resource Engineering | |
| or CEEN 402 | Introduction to Geoenvironmental Engineering | |
| or CEEN 450 | Energy & the Environment | |
| or ENGS 204 | Environmental Engineering Principles I | |
| or ENGS 478 | Sustainability Engineering | |
| or ENVL 408 | Environmental Engineering Design | |
| or MECH 481 | Energy Management | |
| KAKOS Options: | ||
| HIST 358 | The Industrial Revolution | 3 |
| or PHP 206 | Introduction to Public Health | |
| or PHP 418 | Introduction to Environmental Health | |
| or POSC 223 | Environmental Politics | |
| or POSC 251 | Global Issues | |
| or INTL 201 | Global Issues | |
| or POSC 367 | Model United Nations | |
| or POSC 368 | Model United Nations II | |
| or SOC 205 | Urban Environments | |
| or SOC 225 | Telling Stories with Maps | |
| or SOC 250 | Introduction to GIS | |
| or SOC 334 | Sustainable Development | |
| or SOC 353 | Political Ecology | |
| Capstone | ||
| SUST 450 | (Urban Sustainability Consulting Capstone) | 3 |