The Analytics major [formerly Operations Research and Management Science (ORMS)] is designed for students in the College of Letters & Science. In the Analytics major, students will develop solid quantitative and data-driven problem solving skills through core courses in computing, probability/statistics, optimization, and data analytics, and will learn how to apply these skills in solving problems in an area of their choice. It gives students the flexibility to learn more about a particular field of interest in which they can apply these skills.
The major is very math intensive and is appropriate for students who enjoy and are good at mathematics, computers, and solving practical, multidisciplinary problems.
Declaring the Major
Analytics is a high-demand major in L&S.
First-year students applying to Berkeley Letters & Science will be guaranteed admission into the Analytics major if they selected Analytics as their primary major on their UC Berkeley admissions application. Students are guaranteed a spot in the Analytics major, subject to completing the major prerequisites, maintaining good academic standing in L&S, and filing a declaration form.
Newly admitted first year students who did not indicate Analytics as their first choice in their application should refer to the L&S High Demand major policy.
The opportunities for being admitted into the Analytics major after enrollment at UC Berkeley will be extremely limited, and applying to the Analytics major via the comprehensive review process does not guarantee a spot in the major. If you have an interest in the Analytics major, we strongly encourage you to select Analytics as your primary major during the UC application process. If you opt to change to the Analytics major after being admitted to Berkeley, you will be required to have an alternate plan to declare a non-high demand major as a back-up.
All four prerequisite classes (MATH 53, MATH 54, UGBA 10X, and either ECON 1, ECON 2 or ECON C3) must be completed prior to acceptance to the major and all must be taken for a letter grade. Students should declare the major during the semester in which they are enrolled in their final prerequisites but before their 5th semester.
Many factors are considered in determining admission. The main criterion, however, is academic performance as measured by the Berkeley GPA in the prerequisite courses. Since this major is capped, planning for an alternate major is recommended. There is an Operations Research concentration in the Math Department that might be a good choice if students are not admitted to the Analytics major.
Honors Program
Students with a grade point average (GPA) of at least 3.5 overall and 3.7 in the major upper division coursework should consider participating in the Analytics honors program. To graduate with honors, a student must find a faculty sponsor appropriate for an original research project that he or she wishes to do and enroll in two semesters (6 units) of the honors thesis courses IND ENG H196A and IND ENG H196B
Alternatively, a student may take two approved graduate courses in Operations Research or a related field, and achieve at least an A- in each course. Courses used for the honors program cannot be used to fulfill the requirements for the Analytics major or any IEOR graduate program. The student must also maintain a minimum 3.5 overall GPA and 3.7 in the major at the time of graduation.
In addition to the University, campus, and college requirements, listed on the College Requirements tab, students must fulfill the below requirements specific to their major program.
General Guidelines
All courses taken to fulfill the major requirements below must be taken for a letter grade.
No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs, with the exception of minors offered outside of the College of Letters & Science.
A minimum grade point average (GPA) of 2.0 must be maintained in both upper and lower division courses used to fulfill the major requirements.
For information regarding residence requirements and unit requirements, please see the College Requirements tab.
Introduction to Machine Learning and Data Analytics
4
Three clustered electives (see below for sample clusters):
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Analytics majors, with the signed advance approval of their faculty advisers, select a minimum of three upper division elective courses to form a coherent cluster, or concentration, in an area where Analysis is applied. Courses in other departments may count toward this requirement if they have substantial relevant content at an appropriately advanced level. These courses must be upper division and taken for a letter grade.
Undergraduate students must fulfill the following requirements in addition to those required by their major program.
For a detailed lists of L&S requirements, please see Overview tab to the right in this guide or visit the L&S Degree Requirements webpage. For College advising appointments, please visit the L&S Advising Pages.
All students who will enter the University of California as freshmen must demonstrate their command of the English language by fulfilling the Entry Level Writing requirement. Fulfillment of this requirement is also a prerequisite to enrollment in all reading and composition courses at UC Berkeley and must be taken for a letter grade.
The American History and American Institutions requirements are based on the principle that all U.S. residents who have graduated from an American university should have an understanding of the history and governmental institutions of the United States.
All undergraduate students at Cal need to take and pass this campus requirement course in order to graduate. The requirement offers an exciting intellectual environment centered on the study of race, ethnicity and culture of the United States. AC courses are plentiful and offer students opportunities to be part of research-led, highly accomplished teaching environments, grappling with the complexity of American Culture.
College of Letters & Science Essential Skills Requirements
The Quantitative Reasoning requirement is designed to ensure that students graduate with basic understanding and competency in math, statistics, or computer/data science. The requirement may be satisfied by exam or by taking an approved course taken for a letter grade.
The Foreign Language requirement may be satisfied by demonstrating proficiency in reading comprehension, writing, and conversation in a foreign language equivalent to the second semester college level, either by passing an exam or by completing approved course work taken for a letter grade.
In order to provide a solid foundation in reading, writing, and critical thinking the College of Letters and Science requires two semesters of lower division work in composition in sequence. Students must complete parts A & B reading and composition courses in sequential order by the end of their fourth semester for a letter grade.
College of Letters & Science 7 Course Breadth Requirements
The undergraduate breadth requirements provide Berkeley students with a rich and varied educational experience outside of their major program. As the foundation of a liberal arts education, breadth courses give students a view into the intellectual life of the University while introducing them to a multitude of perspectives and approaches to research and scholarship. Engaging students in new disciplines and with peers from other majors, the breadth experience strengthens interdisciplinary connections and context that prepares Berkeley graduates to understand and solve the complex issues of their day.
Unit Requirements
120 total units
Of the 120 units, 36 must be upper division units
Of the 36 upper division units, 6 must be taken in courses offered outside your major department
Residence Requirements
For units to be considered in "residence," you must be registered in courses on the Berkeley campus as a student in the College of Letters & Science. Most students automatically fulfill the residence requirement by attending classes at Cal for four years, or two years for transfer students. In general, there is no need to be concerned about this requirement, unless you graduate early, go abroad for a semester or year, or want to take courses at another institution or through UC Extension during your senior year. In these cases, you should make an appointment to meet an L&S College adviser to determine how you can meet the Senior Residence Requirement.
Note: Courses taken through UC Extension do not count toward residence.
Senior Residence Requirement
After you become a senior (with 90 semester units earned toward your B.A. degree), you must complete at least 24 of the remaining 30 units in residence in at least two semesters. To count as residence, a semester must consist of at least 6 passed units. Intercampus Visitor, EAP, and UC Berkeley-Washington Program (UCDC) units are excluded.
You may use a Berkeley Summer Session to satisfy one semester of the Senior Residence requirement, provided that you successfully complete 6 units of course work in the Summer Session and that you have been enrolled previously in the college.
Modified Senior Residence Requirement
Participants in the UC Education Abroad Program (EAP), Berkeley Summer Abroad, or the UC Berkeley Washington Program (UCDC) may meet a Modified Senior Residence requirement by completing 24 (excluding EAP) of their final 60 semester units in residence. At least 12 of these 24 units must be completed after you have completed 90 units.
Upper Division Residence Requirement
You must complete in residence a minimum of 18 units of upper division courses (excluding UCEAP units), 12 of which must satisfy the requirements for your major.
Student Learning Goals
Learning Goals for the Major
All Analytics graduates are expected to acquire the following general skills and knowledge:
Ability to apply mathematics and science to the solution of societal problems.
Ability to design and conduct experiments, analyze, and interpret data.
Ability to design system and operating policies to meet desired needs.
Ability to function on multidisciplinary teams and communicate effectively.
Ability to identify, formulate, and solve societal system problems.
Understanding of professional and ethical responsibility.
Recognize the need for and ability to engage in life-long learning.
Knowledge of contemporary issues.
Ability to use techniques, skills, and modern tools in practice.
Skills
The Analytics major in the IEOR Department has four general objectives for the Bachelor of Arts degree program. The department aims for the BA degree graduates to become skilled in the following:
Quantitative modeling and analysis of a broad array of systems-level decision problems concerned with economic efficiency, productivity, and quality.
Development and creative use of analytical and computational methods for solving these problems.
Collection and analysis of data and the use of database and decision-support tools.
Comprehension and analysis of risk and uncertainty.
In addition, graduates will obtain the broader skills, background, and knowledge necessary to be effective life-long professionals who understand the impact of systems in a societal context in a rapidly changing global economy.
Specific outcomes of the BA degree program are as follows:
Develop scientific, quantitative, model building, and problem solving skills through core courses in mathematics, statistics, operations research, and management sciences.
Learn how to apply these skills and tools effectively for operational, tactical, and strategic decisions in an area of choice.
Pursue graduate study in operations research and the management sciences.
Major Map
Major maps are experience maps that help undergraduates plan their Berkeley journey based on intended major or field of interest. Featuring student opportunities and resources from your college and department as well as across campus, each map includes curated suggestions for planning your studies, engaging outside the classroom, and pursuing your career goals in a timeline format.
Use the major map below to explore potential paths and design your own unique undergraduate experience:
Terms offered: Not yet offered
An introduction to computer programming focused on developing foundational skills that can be used for implementing analytics methodologies and software. Programming concepts that will be introduced include: control statements, functions, sequences, dictionaries and sets, vectorization, strings, files and exceptions, object-oriented programming, recursion, search, sort, and databases. Applications of these concepts towards simulation analytics and optimization analytics will also be introduced. There are several homeworks and small-scale programming projects. Computer Programming for Analytics: Read More [+]
Objectives & Outcomes
Course Objectives: 1. Learn how to structure the flow and control of basic software programs
2. Learn how to use different programming data structures
3. Learn how to read, save, and manage files in software programs
4. Learn how to use the object-oriented programming paradigm in structuring software programs
5. Learn how to use basic computer science algorithms for recursion, search, and sort
6. Learn how to create, extract and insert information using relational databases
7. Learn how to apply programming concepts for simulation and optimization analytics
Rules & Requirements
Prerequisites:MATH 51 (co-requisite) and ENGIN 7 (can be taken concurrently)
Terms offered: Fall 2017, Fall 2016, Fall 2015
The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Berkeley Seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Freshman Seminars: Read More [+]
Objectives & Outcomes
Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures.
Student Learning Outcomes: Learn more about Industrial Engineering and Operations Research.
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of seminar per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.
Terms offered: Fall 2016
This Freshman-level Introductory course will provide an intuitive overview of the fundamental problems addressed and methods in the fields of Industrial Engineering and Operations Research including Constrained Optimization, Human Factors, Data Analytics, Queues and Chains, and Linear Programming. The course will focus on two-dimensional, i.e., bivariate, examples where the problems and methods are amenable to visualization and geometric intuition. The course will discuss applications such as dieting, scheduling, and transportation. This course will not require pre-requisites and will present the core concepts in a self-contained manner that is accessible to Freshmen to provide the foundation for future coursework. A Bivariate Introduction to IE and OR: Read More [+]
Objectives & Outcomes
Course Objectives: •
Provide a broad survey of the important topics in IE and OR, and develop intuition about problems, algorithms, and abstractions using bivariate examples (2D).
•
Describe different mathematical abstractions used in IEOR (e.g., graphs, queues, Markov chains), and how to use these abstractions to model real-world problems.
•
Introduce students to the data analysis process including: developing a hypothesis, acquiring data, processing the data, testing the hypothesis, and presenting results.
•
Provide students with concrete examples of how the mathematical tools from the class apply to real problems such as dieting, scheduling, and transportation.
Rules & Requirements
Credit Restrictions: Course restricted to Freshman students.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2023, Fall 2022
Design and implementation of databases, with an emphasis on industrial and commercial applications. Relational algebra, SQL, normalization. Students work in teams with local companies on a database design project. WWW design and queries. Industrial and Commercial Data Systems: Read More [+]
Rules & Requirements
Prerequisites: Upper division standing
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2025, Fall 2024, Spring 2024
Economic analysis for engineering decision making: Capital flows, effect of time and interest rate. Different methods of evaluation of alternatives. Minimum-cost life and replacement analysis. Depreciation and taxes. Uncertainty; preference under risk; decision analysis. Capital sources and their effects. Economic studies. Formerly Engineering 120. Principles of Engineering Economics: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Students will not receive credit after taking Engineering 120.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Summer: 8 weeks - 4 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2023, Fall 2022
Analytical techniques for the improvement of manufacturing performance along the dimensions of productivity, quality, customer service, and throughput. Techniques for yield analysis, process control, inspection sampling, equipment efficiency analysis, cycle time reduction, and on-time delivery improvement. Applications on semiconductor manufacturing or other industrial settings. Methods of Manufacturing Improvement: Read More [+]
Terms offered: Spring 2023, Spring 2022, Fall 2021
This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks. Applied Data Science with Venture Applications: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students will be able to design and build data sample application systems that can interpret and use data for a wide range of real life applications across many disciplines and industries;
implement these concepts within applications with modern open source CS tools.
understand relevant mathematical concepts that are used in systems that process data;
Rules & Requirements
Prerequisites: Prerequisites include the ability to write code in Python, and a probability or statistics course. This course is ideal for students who have taken COMPSCI C8 / DATA C8 / INFO C8 / STAT C8
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Fall 2023, Spring 2023, Fall 2022
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Introduction to Machine Learning and Data Analytics: Read More [+]
Objectives & Outcomes
Course Objectives: 1.
To expose students to a variety of statistical learning methods, all of which are relevant in useful in wide range of disciplines and applications. 2.
To carefully present the statistical and computational assumptions, trade-offs, and intuition underlying each method discussed so that students will be trained to determine which techniques are most appropriate for a given problem. 3.
Through a series of real-world examples, students will learn to identify opportunities to leverage the capabilities of data analytics and will see how data analytics can provide a competitive edge for companies. 4.
To train students in how to actually apply each method that is discussed in class, through a series of labs and programming exercises. 5.
For students to gain some project-based practical data science experience, which involves identifying a relevant problem to be solved or question to be answered, gathering and cleaning data, and applying analytical techniques. 6.
To introduce students to advanced topics that are important to the successful application of machine learning methods in practice, include how methods for prediction are integrated with optimization models and modern optimization techniques for large-scale learning problems.
Rules & Requirements
Prerequisites: IEOR 165 or equivalent course in statistics. Prior exposure to optimization is helpful but not strictly necessary. Some programming experience/literacy is expected
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, finance, the Internet, and more. Through these examples, conceptual exercises, data analysis exercises in Python, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, time series modeling, clustering, principal component analysis, regularization, and large-scale learning with neural networks. Introduction to Machine Learning and Data Analytics: Read More [+]
Rules & Requirements
Prerequisites:IND ENG 165 and IND ENG 172 or equivalent courses in probability and statistics. Prior exposure to optimization (either IND ENG 160 or IND ENG 162 or equivalent). Some programming experience/literacy is expected
Terms offered: Spring 2025, Spring 2024
Following IEOR 142A/242A, this course further introduces students to essential
methodologies and recent trends in machine learning and data analytics. The
course will bridge theoretical foundations with applied data analytics by using
examples and real datasets from domains such as e-commerce, social media, finance,
and more. Students will gain experience with various data analytics packages in
Python and will deliver a comprehensive team project. Topics include: deep
learning, time series and survival analysis, end-to-end learning, causal inference,
reinforcement learning, and ethics, fairness and safety in artificial intelligence. Machine Learning and Data Analytics II: Read More [+]
Rules & Requirements
Prerequisites: IndEng 142A or IndEng 242A or equivalent introductory machine learning class. Familiarity with the Python programming language
Credit Restrictions: Students will receive no credit for IND ENG 142B after completing IND ENG 242B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternate method of final assessment during regularly scheduled final exam group (e.g., presentation, final project, etc.).
Terms offered: Fall 2024, Fall 2023, Fall 2022
Revenue management (RM) is the decision science of efficiently selling a fixed supply of various goods and services when the demand is heterogeneous and uncertain. This undergraduate course will focus on fundamental models and algorithms for RM. Broad usefulness of concepts will be demonstrated through applications in airline reservation systems, retail, advertising, e-commerce and school-student assignments. Fundamentals of Revenue Management: Read More [+]
Rules & Requirements
Prerequisites: IndEng 162, IndEng 169 and either IndEng 173 Or IndEng 172 (or equivalent introductory courses in mathematical programming and probability). Familiarity with algorithm design and mathematical maturity recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2020, Fall 2019
Quantitative models for operational and tactical decision making in production systems, including production planning, inventory control, forecasting, and scheduling. Production Systems Analysis: Read More [+]
Terms offered: Spring 2025, Fall 2022, Fall 2021
This course is concerned with improving processes and designing facilities for service businesses such as banks, health care organizations, telephone call centers, restaurants, and transportation providers. Major topics in the course include design of service processes, layout and location of service facilities, demand forecasting, demand management, employee scheduling, service quality management, and capacity planning. Service Operations Design and Analysis: Read More [+]
Terms offered: Fall 2024, Spring 2024, Spring 2022
We will focus primarily on both quantitative and qualitative issues which arise in the integrated design and management of the entire logistics network. Models and solution techniques for facility location and logistics network design will be considered. In addition, qualitative issues in distribution network structuring, centralized versus decentralized network control, variability in the supply chain, strategic partnerships, and product design for logistics will be considered through discussions and cases. Logistics Network Design and Supply Chain Management: Read More [+]
Terms offered: Spring 2025, Spring 2024
With the growing complexity of providing healthcare, it is increasingly important to design and manage health systems using engineering and analytics perspectives. This course will cover topics related to healthcare analytics, including: optimizing chronic disease management, designing matching markets for health systems, developing predictive analytics models, and managing resource utilization. Healthcare Analytics: Read More [+]
Rules & Requirements
Prerequisites: Courses in mathematical modeling (such as IND ENG 160 and IND ENG 172) and computer programming (such as CS C8 or CS 61A) are recommended
Credit Restrictions: Students will receive no credit for IND ENG 156 after completing IND ENG 256.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course introduces unconstrained and constrained optimization with continuous and discrete domains. Convex sets and convex functions; local optimality; KKT conditions; Lagrangian duality; steepest descent and Newton's method. Modeling with integer variables; branch-and-bound method; cutting planes. Models on production/inventory planning, logistics, portfolio optimization, factor modeling, classification with support vector machines. Nonlinear and Discrete Optimization: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course addresses modeling and algorithms for optimization of linear constrained optimization problems. The simplex method; theorems of duality; complementary slackness. Applications in production planning and resource allocation. Graph and network problems as linear programs with integer solutions. Algorithms for selected network flow problems. Transportation and logistics problems. Dynamic programming and its role in applications to shortest paths, project management and equipment replacement. Linear Programming and Network Flows: Read More [+]
Terms offered: Spring 2025, Spring 2024
Designed for students from any science/engineering major, this upper-division course will introduce students to optimization models, and train them to use software tools to model and solve optimization problems. The main goal is to develop proficiency in common optimization modeling languages, and learn how to integrate them with underlying optimization solvers. Students will work primarily on modeling exercises, which will develop confidence in modeling and solve optimization methods using software packages, and will require some programming.
Review of linear and nonlinear optimization models, including optimization problems with discrete decision variables. Applications to practical problems from engineering and data science. Introduction to Optimization Modeling: Read More [+]
Objectives & Outcomes
Course Objectives: •
To introduce students to the core concepts of optimization
•
To train them in the art and science of using software tools to model and solve optimization problems.
Rules & Requirements
Prerequisites: No prerequisites except some Python programming skills, which can be met by COMPSCI C8 (or any other Python-based course)
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2025, Spring 2024, Spring 2023
This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Applications in forecasting and quality control.
Terms offered: Spring 2025, Fall 2023, Spring 2022
Introductory course on the theory and applications of decision analysis. Elective course that provides a systematic evaluation of decision-making problems under uncertainty. Emphasis on the formulation, analysis, and use of decision-making techniques in engineering, operations research and systems analysis. Includes formulation of risk problems and probabilistic risk assessments. Graphical methods and computer software using event trees, decision trees, and influence diagrams that focus on model design. Decision Analytics: Read More [+]
Terms offered: Spring 2022, Spring 2021, Fall 2020
This course addresses modeling and algorithms for integer programming problems, which are constrained optimization problems with integer-valued variables. Flexibility of integer optimization formulations; if-then constraints, fixed-costs, etc. Branch and Bound; Cutting plane methods; polyhedral theory. Applications in production planning, resource allocation, power generation, network design. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Algorithms for integer optimization problems. Specialized strategies by integer programming solvers. Integer Optimization: Read More [+]
Objectives & Outcomes
Course Objectives: •
Enable the students to recognize when problems can be modeled as integer optimization problems. •
Familiarize students in leading methodologies for solving integer optimization problems, and techniques in these methodologies. •
To acquire skills in the best modeling approach that is suitable to the practical problem at hand. •
To train students in modeling of integer optimization problems; •
To train the students in the selection of appropriate techniques to be used for integer optimization problems.
Rules & Requirements
Prerequisites:MATH 53, MATH 54, and background in Python and programming
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2025, Spring 2024, Spring 2023
This course surveys topics related to the design of products and interfaces ranging from alarm clocks, cell phones, and dashboards to logos, presentations, and web sites. Design of such systems requires familiarity with human factors and ergonomics, including the physics and perception of color, sound, and touch, as well as familiarity with case studies and contemporary practices in interface design and usability testing. Students will solve a series of design problems individually and in teams. Industrial Design and Human Factors: Read More [+]
Rules & Requirements
Prerequisites: Upper division standing
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2023, Spring 2023
This course emphasizes the three Berkeley Changemaker pillars of critical thinking, effective communication, and productive collaboration. It combines critical examination of entrepreneurial challenges with strategic, ethical, and leadership theories. It develops verbal and collaborative leadership skills, through flipped classroom and intense case discussions, a team project, and a formal final presentation of the project. The case discussions in particular will develop effective listening, real-time analysis, and verbal leadership skills. The project will challenge you to analyze a current or historical ethical challenge in a high technology industry,
or analyze the ethical implications of your own entrepreneurial plans. Berkeley Changemaker: Ethical and Effective Entrepreneurship in High Tech: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students who fully engage with this class will strengthen their in-the-moment abilities to listen, learn, analyze, and convince. They will size up high tech business and entrepreneurial
opportunities with new perspectives, both strategic and ethical. They will gain practice in applying strategic and ethical frameworks to entrepreneurship and business decisions in high
technology. They will learn how to understand and build upon criticism in real-time, and lead discussions on contentious issues towards productive, inclusive, and mutually beneficial
outcomes. They will become an entrepreneur who not only sees how innovation can solve society’s problems, but can furthermore convince and lead others in accomplishing and
implementing a solution.
Rules & Requirements
Prerequisites: Upper division standing
Credit Restrictions: Students will receive no credit for IND ENG 171 after completing UGBA 105.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Summer: 8 weeks - 6 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2025, Fall 2024, Spring 2024
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
Course Objectives: Students will learn how to model random phenomena and learn about a variety of areas where it is important to estimate the likelihood of uncertain events. Students will also learn how to use computer simulation to replicate and analyze these events.
Terms offered: Spring 2025, Spring 2024, Spring 2023
This is an introductory course in stochastic models. It builds upon a basic course in probability theory and extends the concept of a single random variable into collections of random variables known as stochastic processes. The course focuses on discrete-time Markov chains, Poisson process, continuous-time Markov chains, and renewal theory. It also discusses applications to queueing theory, risk analysis and reliability theory. Along with the theory, the course covers stochastic simulation techniques that will allow students to go beyond the models and applications discussed in the course. Introduction to Stochastic Processes: Read More [+]
Objectives & Outcomes
Course Objectives: Students will learn how to model random phenomena that evolves over time, as well as the simulation techniques that enable the replication of such problems using a computer. By discussing various applications in science and engineering, students will be able to model many real world problems where uncertainty plays an important role.
Rules & Requirements
Prerequisites: Students should have taken a probability course, such as STAT 134 or IND ENG 172, and should have programming experience in Matlab or Python
Credit Restrictions: Students will receive no credit for Ind Eng 173 after taking Ind Eng 161.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2023, Fall 2022
Introductory course on design, programming, and statistical analysis of simulation methods and tools for enterprise-scale systems such as traffic and computer networks, health-care and financial systems, and factories. Topics include the types of problems that can be solved by such methods. Programming material includes the theory behind random variable generation for a variety of common variables. Advanced techniques such as variance reduction, simulation optimization, or meta-modeling are considered. Student teams implement an enterprise-scale simulation in a semester-length design project. Simulation for Enterprise-Scale Systems: Read More [+]
Objectives & Outcomes
Course Objectives: •
Exposure students to state-of-art advanced simulation techniques. •
Note: the course is a mixture of modeling art, analytical science, and computational technology. •
Have students communicate their ideas and solutions effectively in written reports. •
Insure students become familiar with the fundamental similarities and differences among simulation software packages. •
Introduce students to modern techniques for developing computer simulations of stochastic discrete-event models and experimenting with such models to better design and operate dynamic systems. •
Introduce the different technologies used to develop simulation models and simulator products in order to become critical consumers of simulation study results. •
Teach strengths and weaknesses of different approaches for a foundation for selecting methodologies. •
Teach students how to model random processes and experiment with simulated systems.
Credit Restrictions: Students will receive no credit for IND ENG 174 after completing IND ENG 131. A deficient grade in IND ENG 174 may be removed by taking IND ENG 131.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2025, Spring 2024, Spring 2023
Application of systems analysis and industrial engineering to the analysis, planning, and/or design of industrial, service, and government systems. Consideration of technical and economic aspects of equipment and process design. Students work in teams under faculty supervision. Topics vary yearly. Senior Project: Read More [+]
Rules & Requirements
Prerequisites: 160, 162, 165, 173, Engineering 120, and three other Industrial Engineering and Operations Research electives
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 6 hours of fieldwork per week
Summer: 10 weeks - 3 hours of lecture and 9 hours of fieldwork per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Fall 2021, Spring 2011
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company, product, or service. Over the duration of this course, students will examines case studies of early, mid-stage, and large-scale enterprises as they seek to start a new venture, introduce a new product or service, or capitalize on global economic trends to enhance their existing business. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion. Cases will include both U.S. companies seeking to enter emerging markets and emerging market companies looking to expand within their own nations or into markets in developed nations. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Cases in Global Innovation: Read More [+]
Rules & Requirements
Prerequisites: Junior or Senior standing
Hours & Format
Fall and/or spring: 8 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Prior to 2007
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company product or service, with a focus on China. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the China market, or domestic Chinese companies seeking to adapt a U.S. or western business model to the China market. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion and the particularities of the China market and their contrast with the U.S. market. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Cases in Global Innovation: China: Read More [+]
Rules & Requirements
Prerequisites: Junior or senior standing. Recommended, but not required to be taken after or along with Engineering 198
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Prior to 2007
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in conducting business, globalizing a company product or service, or investing in South Asia. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the South Asian market, or South Asian companies seeking to adapt a U.S or western business model. The course will put this into the larger context of the political, economic, and social climate in several South Asian countries and explore the constraints to doing business, as well as the policy changes that have allowed for a more conducive business environment. Cases in Global Innovation: South Asia: Read More [+]
Rules & Requirements
Prerequisites: Junior or senior standing. Recommended but not required to be taken after or along with Engineering 198
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Prerequisites: Consent of instructor and major adviser
Credit Restrictions: Course may be repeated for a maximum of four units per semester.
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Summer: 6 weeks - 2.5-10 hours of independent study per week 8 weeks - 2-7.5 hours of independent study per week 10 weeks - 1.5-6 hours of independent study per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
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