MBAxMS
The joint MS Engineering - MBA will provide students advanced in their professional careers the opportunity to develop further the skills for management and execution of highest level of technical and analytical efficiencies to industry and more. Over the course of a two-year integrated program, students will gain foundational and up to date skills for application in engineering and in management, allowing them to take leadership positions in a multiplicity of fields. Students will begin the program in the first semester taking a combination of courses both in the Engineering School and the Business School, including the core foundation courses required for the program. In the second year the student will take a combination of required courses in both schools, including elective courses in areas of specialization. Students will cross-register in either school.
Offered by Columbia Engineering, the Executive Master of Science in Engineering and Applied Science will provide students with a broad understanding of engineering science and the end-to-end product development process. It is intended for professionals who are preparing for leadership roles that need an understanding of the development process of new products (goods or services). Candidates for the Executive Master of Science in Engineering and Applied Science are required to complete a minimum of 30 graduate-level credits, which includes core courses (15 credits), electives in their concentration (12 credits in one concentration), and a capstone project which integrates their overall learning experience and prepares them to take on a significant leadership role (3 credits). M.S. students must complete the professional development and leadership course, ENGI E4000 PROF DEVELOPMENT＆LEADERSHIP, as a graduation requirement.
A bachelor’s degree or its equivalent from an accredited institution is required for admission to the program. All applicants must also submit a valid GMAT*, Executive Assessment, or GRE score when submitting an application.
*We accept both GMAT Exam and GMAT Focus Edition scores.
Students must complete 67.5 points between Columbia Engineering and Columbia Business School. These must include 31.5 points from the courses listed below that are deemed core to Columbia Business School and Columbia Engineering.
Dual MBA/Executive MS students must complete the professional development and leadership course, ENGI E4000 PROF DEVELOPMENT＆LEADERSHIP, as a graduation requirement.
The overall program must include a minimum of 36 additional credits, which includes electives in their engineering concentration (9 credits), electives in their business concentration (9 credits), analytics course (3 credits), innovation course (3 credits), engineering capstone project (3 credits), and free business school electives (9 credits).
Core Engineering Courses:
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ENGI E4502 Design of UI/UX for Connected Systems
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ENGI E4505 Frontiers of Tough Tech or IEME E4505 Frontiers of Tough Tech
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ENGI E4507 Fundamental Design Tools or MECE E4507 Fundamental Design Tools
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ENGI E4503 Analytics in Python
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ENGI E4501 Human-Centered Design and Innovation or IEME E4201 Human-Centered Design and Innovation for MBAxMS Program
Core Business Courses:
- BUSI B6001: Financial Accounting
- BUSI B6102: Operations Management
- BUSI B6103: Managerial Statistics + Business Analytics
- BUSI B6200: Managerial Economics
- BUSI B6201: Global Economic Environment
- BUSI B6301: Foundation of Valuation
- BUSI B6302: Corporate Finance
- BUSI B6500: Lead: People, Teams, Organizations
- BUSI B6502: Strategy Formulation
- BUSI B6601: Marketing
- BUSI B8518: Foundations of Entrepreneurship
For more information on Columbia Business School courses please visit courses.business.columbia.edu.
Engineering Concentrations
Medical Device Design
| Code | Title | Points |
|---|---|---|
| BMEN E6005 | Biomedical Innovation I | |
| BMEN E6006 | BIOMEDICAL DESIGN II | |
| BMEN E6007 | LAB-TO-MARKET | |
| BMEE E4740 | BIOINSTRUMENTATION | |
| BMEN E4530 | DRUG AND GENE DELIVERY | |
| BMEN E4590 | BIOMEMS:CELL/MOLECULAR APPLIC | |
| BMEN E4000 | SPECIAL TOPICS IN BIOMEDICAL ENGINEERING | |
| BMEN E6000 | Graduate Special Topic | |
| CHEN E4660 | BIOCHEMICAL ENGINEERING | |
| CHEN E4700 | PRINCIPLES OF GENOMIC TECHNOL | |
| CHEN E4870 | Synthetic Organogenesis |
AI and Machine Learning
| Code | Title | Points |
|---|---|---|
| COMS W4705 | NATURAL LANGUAGE PROCESSING | |
| COMS W4701 | ARTIFICIAL INTELLIGENCE | |
| MECE E6615 | ROBOTIC MANIPULATION | |
| MECE E6616 | ROBOT LEARNING | |
| EECS E6694 | TOPICS DATA-DRIVEN ANAL ＆ COMP | |
| EECS E6699 | TOPICS DATA-DRIVEN ANAL ＆ COMP | |
| EECS E6720 | BAYESIAN MOD MACHINE LEARNING | |
| ELEN E6820 | SPEECH＆AUDIO PROC＆REC | |
| ELEN E6876 | Sparse and Low-Dimensional Models for High-Dimensional Data | |
| ELEN E6885 | Topics in signal processing | |
| EECS E6892 | TOPICS-INFORMATION PROCESSING | |
| EECS E6893 | TOPICS-INFORMATION PROCESSING | |
| EECS E6895 | TOPICS-INFORMATION PROCESSING | |
| CSEE W4121 | COMPUTER SYSTEMS FOR DATA SCIENCE | |
| EECS E4750 | Heterogeneous Computing for Signal and Data Processing | |
| EECS E6891 | TOPICS-INFORMATION PROCESSING | |
| CIEN E4253 | COMP SOLID MECHANICS WITH AI | |
| CIEN E4256 | Applied Machine Learning in Civil Engineering | |
| CHEN E4580 | ARTIFICIAL INTELLIGENCE IN CHEMICAL ENGINEERING | |
| CHEN E4880 | ATOMISTIC SIMULATIONS FOR SCIENCE AND EN | |
| CHEN E4180 | Machine Learning for Biomolecular and Cellular Applications | |
| COMS W4731 | Computer Vision I: First Principles | |
| COMS W4732 | Computer Vision II: Learning | |
| COMS W4773 | Machine Learning Theory | |
| COMS W4774 | Unsupervised Learning | |
| COMS W4775 | Causal Inference | |
| COMS W4776 | Machine Learning for Data Science | |
| COMS W4995 | TOPICS IN COMPUTER SCIENCE | |
| COMS W6706 | Advanced Spoken Language Processing | |
| COMS E6998 | TOPICS IN COMPUTER SCIENCE | |
| EAEE E4000 | Machine learning for environmental engineering and science | |
| EAEE E4009 | GIS-RES,ENVIR,INFRASTRUCTR MGT | |
| ECBM E4040 | NEURAL NETWRKS ＆ DEEP LEARNING | |
| ELEN E6908 | TOPICS IN ELECTRICAL AND COMPUTER ENGINE | |
| EEEL E4220 | Energy System Economics and Optimization | |
| IEOR E4523 | Data Analytics and Machine Learning | |
| MECE E4602 | INTRODUCTION TO ROBOTICS | |
| MECE E4611 | ROBOTICS STUDIO | |
| MECE E6612 | Robotics Studio (Advanced) |
Supply Chain, Retail and Service Systems
| Code | Title | Points |
|---|---|---|
| IEOR E4108 | SUPPLY CHAIN ANALYTICS | |
| IEOR E4418 | TRANSPORTATION ANALYTICS ＆ LOGISTICS | |
| IEOR E4601 | DYNAMIC PRICING/REVENUE MGMT | |
| IEOR E4507 | HEALTHCARE OPERATIONS MGT | |
| EAEE E4200 | Introduction to Sustainable Production of Earth Mineral ＆ Metal Resources | |
| EAEE E4220 | Energy System Economics and Optimization | |
| EAEE E4361 | ECON-EARTH RESOURCE INDUSTRIES | |
| IEOR E4505 | OPERATION RES IN PUBLIC POLICY |
Robotics and Smart Machines
| Code | Title | Points |
|---|---|---|
| COMS W4771 | MACHINE LEARNING | |
| COMS W4733 | COMPUTATIONAL ASPECTS OF ROBOTICS | |
| COMS W4731 | Computer Vision I: First Principles | |
| MECE E4602 | INTRODUCTION TO ROBOTICS | |
| MECE E4611 | ROBOTICS STUDIO | |
| MECE E4613 | Industrial Automation | |
| MECE E6616 | ROBOT LEARNING | |
| MECE E6617 | Advanced Kinematics, Dynamics, and Control in Robotics | |
| ELEN E4830 | DIGITAL IMAGE PROCESSING | |
| ELEN E6908 | TOPICS IN ELECTRICAL AND COMPUTER ENGINE | |
| EECS E4764 | Artificial Intelligence of Things (AIoT) | |
| MEEC E6600 | Mathematics of Machine Learning, Signals, and Control | |
| EEME E6911 | Topics in Control | |
| MEEE E4600 | Continuous Control Systems | |
| EEME E4601 | DISCRETE CONTROL SYSTEMS | |
| ELEN E4720 | Machine Learning for Signals, Information and Data | |
| COMS W4731 | Computer Vision I: First Principles | |
| EECS E6792 | Deep Learning on the Edge | |
| COMS W4732 | Computer Vision II: Learning | |
| COMS E6998 | TOPICS IN COMPUTER SCIENCE |
Climate, Energy, and Sustainability
| Code | Title | Points |
|---|---|---|
| MECE E4211 | ENERGY SOURCES AND CONVERSION | |
| EAEE E4002 | ALTERNATIVE ENERGY RESOURCES | |
| EESC GU4008 | Introduction to Atmospheric Science | |
| EAEE E4180 | ELECTROCHEMICAL ENERGY STORAGE SYSTEMS | |
| CHEN E4231 | SOLAR FUELS | |
| EAEE E4000 | Machine learning for environmental engineering and science | |
| EAEE E4100 | A Better Planet by Design (MS) | |
| EAEE E4300 | INTRO TO CARBON MANAGEMENT | |
| MECE E4312 | SOLAR THERMAL ENGINEERING | |
| EESC W4008 | ||
| MECE E4211 | ENERGY SOURCES AND CONVERSION | |
| MECE E4312 | SOLAR THERMAL ENGINEERING | |
| ELEN E4361 | POWER ELECTRONICS | |
| ELEN E4511 | POWER SYSTEMS ANALYSIS | |
| ELEN E4510 | Grid Modernization ＆ Clean Tech | |
| ELEN E6570 | Future Energy: Economics, Systems, Policies | |
| ELEN E6905 | TPCS-ELEC ＆ COMPUT ENGINEERING | |
| COMS E6998 | TOPICS IN COMPUTER SCIENCE | |
| CIEN E4012 | Sustainable Urban Systems Engineering | |
| CHEN E4201 | ENGIN APPL OF ELECTROCHEMISTRY | |
| CHEN E4600 | AEROSOLS | |
| CHEN E4860 | NMR BIOSOFTENG | |
| CHEN E4380 | Green Chemical Engineering ＆ Innovation | |
| CHEN E4331 | Catalysis and Kinetics of Sustainable CO2 Conversion | |
| CHEN E4665 | Polymer Chemistry for Sustainable Solutions | |
| CHEN E4880 | ATOMISTIC SIMULATIONS FOR SCIENCE AND EN | |
| EAEE E4200 | Introduction to Sustainable Production of Earth Mineral ＆ Metal Resources | |
| EAEE E4220 | Energy System Economics and Optimization | |
| EAEE E4300 | INTRO TO CARBON MANAGEMENT | |
| EAEE E4361 | ECON-EARTH RESOURCE INDUSTRIES |
Advanced Materials and Nanotechnology
| Code | Title | Points |
|---|---|---|
| ELEN E4411 | FUNDAMENTALS OF PHOTONICS | |
| ELEN E4944 | PRNCPLS OF DEVICE MICROFABRCTN | |
| ENME E4114 | MECHANCS OF FRACTURE ＆ FATIGUE | |
| ENME E4115 | MICROMECH OF COMPOSITE MAT | |
| MECE E4212 | MICROELECTROMECHANICAL SYSTEMS | |
| MSAE E4090 | NANOTECHNOLOGY | |
| MSAE E4102 | SYNTHESIS ＆ PROCESSING OF MATERIALS | |
| MSAE E4206 | ELEC ＆ MAGNETIC PROP OF SOLIDS | |
| MSAE E4215 | MECH BEHAVIOR OF MATERIALS | |
| MSAE E4250 | CERAMICS ＆ COMPOSITES | |
| MSAE E4260 | ELECTROCHEM MATLS ＆ DEVS | |
| MSAE E4301 | MATERIALS SCIENCE LABORATORY | |
| ELEN E4106 | Advanced Solid State Devices and Materials | |
| ENME E4117 | Mechanics of Fiber-Reinforced Composites | |
| CHEN E4620 | INTRO-POLYMERS/SOFT MATERIALS | |
| CHEN E4630 | TOPICS IN SOFT MATERIALS | |
| CHEN E4665 | Polymer Chemistry for Sustainable Solutions | |
| CHEN E4150 | COMPUTATIONAL FLUID DYNAMICS I | |
| CHEN E4650 | POLYMER PHYSICS |
Fintech and Analytics
| Code | Title | Points |
|---|---|---|
| IEOR E4523 | Data Analytics and Machine Learning | |
| IEOR E4525 | MACHINE LEARNING FE ＆ OPR | |
| IEOR E4532 | Visualization and Storytelling with Data | |
| IEOR E4533 | Performance, Objectives, ＆ Results Using Data Analytics | |
| IEOR E4534 | Applied Analytics: from Data to Decisions | |
| IEOR E4576 | TOPICS IN OPERATIONS RESEARCH | |
| IEOR E4577 | TOPICS IN OPERATIONS RESEARCH | |
| IEOR E4737 | AI Applications in Finance | |
| IEOR E4742 | Deep Learning for OR and FE | |
| ELEN E6883 | TOPICS IN SIGNAL PROCESSING |