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The first semester of the comprehensive first year course in biology. Covers the biochemistry and inner workings of cells, energy metabolism, genetics, cellular physiology, population genetics and evolutionary pattern and process. Laboratory topics include use of the microscope, biochemistry, cell structure and function, genetics, and evolution. Intended for students who are pursuing, or considering, the major in biology or life sciences teaching.

Full course description for General Biology I

The first semester of the comprehensive first year course in chemistry. Covers measurement, stoichiometry, solution chemistry, atomic structure, bonding, molecular structure, molecular visualization, and problem solving. Lab includes basic laboratory techniques, instrumentation, methodology, chemical analysis, and laboratory notebook procedures. The labs are also designed to engage students in critical thinking and concept building and are directly coordinated with the lecture part of the course. Intended for students who are pursuing, or considering, the biology or life sciences teaching major and/or chemistry minor, and qualified students seeking a general education science course with lab.

Full course description for General Chemistry I

This course covers the basic principles and methods of statistics. It emphasizes techniques and applications in real-world problem solving and decision making. Topics include frequency distributions, measures of location and variation, probability, sampling, design of experiments, sampling distributions, interval estimation, hypothesis testing, correlation and regression.

Full course description for Statistics I

This is a continuation of Math 211 Calculus II and covers calculus as it applies to functions of several variables. Topics include vectors and plane curves, partial differentiation, curves and vectors in space, multiple integrals, vector fields, line integrals, and Stokes Theorem.

Full course description for Calculus III: Multivariable Calculus

The need to solve systems of linear equations frequently arises in mathematics, the physical sciences, engineering and economics. In this course we study these systems from an algebraic and geometric viewpoint. Topics include systems of linear equations, matrix algebra, Euclidean vector spaces, linear transformations, linear independence, dimension, eigenvalues and eigenvectors.

Full course description for Linear Algebra and Applications

This is the first course of a two semester sequence covering the fundamental concepts of physics. This course covers Newton's laws of motion, work, energy, linear momentum, rotational motion, gravity, equilibrium and elasticity, periodic motion, fluid mechanics, temperature, heat, and the laws of thermodynamics. Laboratories emphasize application of physics concepts and quantitative problem solving skills. Intended for science majors and general education students with strong mathematical background.

Full course description for Calculus Based Physics I

This is the second course of a two semester sequence covering the fundamental concepts of physics. This course covers oscillatory motion, waves, superposition and interference of waves, diffraction, electricity and magnetism, electric circuits, light, mirrors and lenses. Laboratories emphasize application of physics concepts and quantitative problem solving skills. Intended for science majors.

Full course description for Calculus Based Physics II

An introduction to methods and techniques commonly used in data science. This course will use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. Students will complete an engineering project. Overlap: DATA 211

Full course description for Data Science and Visualization

This course introduces concepts and economic analysis procedures to assist with decision-making and alternative comparisons in engineering projects. Topics include time value of money calculations and cash flow diagrams (CFDs); present worth (PW), annual worth (AW), future worth (FW), and rate of return analysis as methods of comparing project alternatives; depreciation and taxes; costs analyses such as determining break-even points, impact of inflation, and replacement costs.

Full course description for Economic Analysis for Engineers

In this course, students will learn about probability axioms, intermediate level statistical analysis methods, and applications of probability and statistical methods in engineering fields. It will cover discrete and continuous random variables and their associated probability distributions, sampling distributions, interval estimation and hypothesis testing methods, and linear regression.

Full course description for Probability and Statistics for Engineering

Optimization covers a broad range of problems that share a common goal - determining the values for the decision variables in a problem that will maximize (or minimize) some objective function while satisfying various constraints. Using a mathematical modeling approach, this course introduces mathematical programming techniques and applications for linear and non-linear programming, sensitivity analysis, network modeling, integer linear programming, goal programming, and multiple criteria optimization. Software is used to solve real-world problems with an emphasis on interpretability of results. Overlap: MATH 330

Full course description for Optimization

This course provides a practical introduction to computational mathematics with a strong emphasis on the development of programming skills for mathematical problem solving. The course introduces essential tools for numerical modeling, data handling, and simulation. Selected numerical analysis topics such as root-finding, Gaussian elimination, interpolation, and error analysis are included. The course also delves into stochastic models, including Markov chains, Poisson processes, and queueing theory, and implementation of Monte Carlo simulations to analyze real-world systems. Throughout the course, students gain experience writing code and interpreting analytical and computational results in business and industry contexts.

Full course description for Computational Mathematics

This course covers the critical skills required to implement effective product and process control, including acceptance sampling, control charts, the DMAIC (Define, Measure, Analyze, Improve, and Control) process, and process and measurement system capability analysis. Coverage includes the quantitative tools and techniques that drive continuous improvement including ways to summarize data, draw statistical conclusions, analyze distributions, test hypotheses, and design and execute experiments with a focus on process optimization.

Full course description for Quality Engineering

This course introduces simulation to analyze uncertainty over time in a production system or services using a collection of random variables known as stochastic processes. The systems are analyzed using different probability distributions and mathematical models. An analysis of systems is carried out to design and determine different problems that could arise, to understand bottlenecks and wait time, inventory control, capacity planning, and reliability of the system. In this course students will learn how to simulate real-world scenarios using discrete event simulation methods to optimize complex manufacturing and health care systems.

Full course description for Simulation Modeling and Analysis

This course introduces ergonomics (also sometimes referred to as human factors) and the design of human¿machine systems, emphasizing how human sensory, cognitive, and physical capabilities shape safe and effective products, interfaces, and industrial workplaces. Students learn and apply research methods, and iterative design and evaluation processes, to study and improve displays, controls, workstations, and human¿computer interaction. The course integrates anthropometry, biomechanics, work physiology, stress/workload, and safety standards (e.g., OSHA/NIOSH) to reduce risk of harm to workers and improve performance. Through hands-on engineering-related activities, students practice analyzing real systems, making evidence-based design decisions that consider ethical and social implications, and communicating technical recommendations.

Full course description for Ergonomics for Engineers

This course introduces different theories and engineering applications of production planning and inventory control (for both deterministic and stochastic demand) essential for manufacturing shop floors. Topics of this course include forecasting, production scheduling, aggregate planning, material requirement planning (MRP), theory of constraints, assembly line balancing, sequencing algorithms, number of required machines, supply chain management, inventory and production flows, facilities location and layout, and capacity analysis. This course utilizes advanced quantitative methods such as optimization and simulation modeling to illustrate the concepts.

Full course description for Productivity Analysis

This course provides supervised design experiences for advanced industrial engineering students. Students will review and apply industrial engineering principles to design integrated systems that produce or supply products or services in an effective, efficient, sustainable, socially responsible and ethical manner. Students will apply concepts covered in other courses with the goal of enhancing productivity, reducing waste, and ensuring quality. Students will gain experience working effectively in teams, managing resources, and dealing with unforeseen and/or novel factors.

Full course description for Industrial Engineering Design I

This course extends the experience gained in IENG 498 culminating in the completion of a major design project. Students work with limited supervision in teams as they complete their projects. Students will develop the knowledge, skills, and professional rapport necessary to interact with clients, including the skills necessary for communicating results and recommendations with diverse stakeholders.

Full course description for Industrial Engineering Design II

This course focuses on developing the skills needed to become a successful project manager and project team member using quantitative methods and technology tools to enhance critical problem-solving. Topics covered include all aspects of project management from project initiation issues and project planning to scheduling, organization, implementation, monitoring progress and controlling by using technology driven approaches. This includes project management techniques such as PERT, CPM and project evaluation methods using Microsoft Project software.

Full course description for Project Management

The purpose of this course is to introduce students to the fundamental concepts and techniques of production and operations management for both service and manufacturing organizations. It will address the role of operations in relation to other functions and the methods to increase organizational effectiveness and efficiency. Topics covered include: product and service design, capacity planning, design of work systems, location planning and analysis, material requirements planning, supply-chain management, enterprise resource planning, inventory management, total quality management, Six Sigma, lean enterprise and kaizen approaches, aggregate planning, just-in-time systems, scheduling, and project planning. Also included are tools and processes used in operations decisions such as forecasting, breakeven analysis, and critical path method using available software.

Full course description for Introduction to Operations Management

Biomechanics is the study of the physical aspects of life, the materials and structures made and used by living things of all kinds, plants, animals, fungi, protista and bacteria. Biomechanics unites the fields of physics, physiology, ecology and engineering in the investigation of biological materials and structures and the structural and functional roles that they play for the organisms that produce them. This includes chitin, cellulose, spider silk, feathers, tooth enamel, wood, bone, arteries, tree branches, porcupine quills and many more. This course is an upper division elective in the Biology major and meets the 400-level capstone course requirement of the major.

Full course description for Comparative Biomechanics

The second semester of the comprehensive algebra-based first year course in chemistry. Covers acid/base theory, chemical equilibria, nuclear and electrochemistry, redox reactions, terminology, functional groups, reactivity of organic compounds and an introduction to biochemistry. Includes lab. Intended for students pursuing the biology or life sciences teaching major and/or chemistry minor.

Full course description for General Chemistry II

Statistical machine learning (often referred to simply as statistical learning) has arisen as a recent subfield of statistics. It emphasizes the interpretability, precision, and uncertainty of machine learning models. This course assesses the accuracy of several supervised and unsupervised machine learning models for both regression and classification. Topics include the bias-variance trade-off, training and test datasets, resampling methods, shrinkage and dimension reduction methods, non-linear modeling techniques such as regression splines and generalized additive models, and decision tree-based methods. Applications include examples from medicine, biology, marketing, finance, insurance, and sports.

Full course description for Applied Machine Learning

This course provides basic introduction to data structures and algorithms and emphasizes the relationship between algorithms and programming. Students will learn intermediate object-oriented design, programming, testing and debugging. Topics include inheritance, polymorphism, algorithm complexity, generic programming, linked list, stack, queue, recursion, trees, hashing, searching, and sorting.

Full course description for Introduction to Data Structures

Covers concepts and methods in the definition, creation and management of relational databases. Emphasis is placed on usage of appropriate methods and tools to design and implement relational databases to meet identified business needs. Topics include conceptual database design, use of Entity Relationship Diagrams, query tools and SQL; database integrity, security and privacy; query optimization; transaction management, concurrency control, and recovery; and emerging data management trends. Use of database management systems such as MySQL.

Full course description for Database Management Systems

Internships offer students opportunities to gain deeper knowledge and skills in their chosen field. Students are responsible for locating their own internship. Metro faculty members serve as liaisons to the internship sites supervisors and as evaluators to monitor student work and give academic credit for learning. Students are eligible to earn 1 credit for every 40 hours of work completed at their internship site. Students interested in internships within the Mathematics and Statistics Department should work with their advisor and/or faculty internship coordinator to discuss the process for your specific major.

Full course description for Industrial Engineering Internship

This course develops the more advanced mathematical tools necessary for an in-depth analysis of dynamic models. Topics include first order differential equations, first order systems, linear systems, nonlinear systems and numerical methods.

Full course description for Ordinary Differential Equations

This course introduces the concepts of thermodynamics. Topics include the first law of thermodynamics, the second law of thermodynamics, entropy, statistical mechanics, specific heat capacities of gases and solids, efficiency and the Carnot cycle, chemical potential, chemicals and phase equilibriums, etc. Applications explored will include the behavior of gases and the operation of heat engines. Laboratories emphasize real world applications of the concepts and problem solving skills taught in this course.

Full course description for Thermodynamics

This course covers special relativity, elementary quantum theory, atomic structure and spectra. It is intended for students pursuing chemistry and physics major/minor.

Full course description for Modern Physics

This course covers the fundamental to intermediate ideas of the statistical analysis of categorical data. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include analysis of 2x2 tables, stratified categorical analyses, estimation of odds ratios, analysis of general two-way and three-way tables, probit analysis, and analysis of loglinear models. Completion of STAT201 (Statistics I) is a prerequisite.

Full course description for Analysis of Categorical Data

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods of time. This course provides an introduction to both standard and advanced time series analysis and forecasting methods. Graphical techniques and numerical summaries are used to identify data patterns such as seasonal and cyclical trends. Forecasting methods covered include: Moving averages, weighted moving averages, exponential smoothing, state-space models, simple linear regression, multiple regression, classification and regression trees, and neural networks. Measures of forecast accuracy are used to determine which method to use for obtaining forecasts for future time periods.

Full course description for Time Series Analysis and Forecasting

This course addresses tools, techniques, and strategies used in service and manufacturing organizations for management and controlling internal and enterprise supply chains. Topics include demand management, forecasting, sales and operations planning, production scheduling, material requirements planning, capacity planning, just-in-time, distribution requirements planning, order-point inventory control methods, and strategic design of planning and control systems.

Full course description for Supply Chain Planning and Control

This course examines those activities involved in planning, implementing and controlling the flows of raw materials, in-process inventories, and finished goods from the points of origin to the points of consumption at the lowest total cost. Topics covered include enterprise resource planning; forecasting; inventory management; transportation modes, services and rates; warehousing; information systems; performance measurement; quality; materials handling; customer services; and the overall management of logistical functions. The computerized information programs intending to support the management functions are also treated. Special emphasis is placed on building business analysis skills to assess the feasibility and cost benefit of its functions to support logistics operations.

Full course description for Logistics in Supply Chain

This course focuses on the interactions between the consumer and the producer. It begins with the theory of markets, supply and demand, and the price system. Then it covers demand elasticity, the costs of production including the various factor inputs, the four major market structures (pure competition, monopolistic competition, oligopoly and monopoly), and ways to increase the competition in markets.

Full course description for Microeconomics

Interdisciplinary Business Knowledge and Skills for Non-Business Majors is designed to provide broad coverage of major business concepts in finance, marketing, accounting, and management and deep coverage of specific skills and knowledge needed as a foundation for applying that knowledge to opportunities in existing or new businesses. Students will learn how to research data within the Metro State library databases to augment their knowledge and skills to evaluate opportunities and existing organizations. The students will be asked to enhance their analytical thinking by asking pertinent questions, determining relevant information, and systematically developing and applying the business processes to make decisions.

Full course description for Building Your Dream: An Introduction to Small Business

This entrepreneurship course will introduce students to the concepts of creativity, and innovation to solve business problems. Innovation techniques will be applied to identify market trends, customer needs, generate and evaluate new business opportunities. Students will also gain knowledge in identifying a business model to launch a new venture.

Full course description for Creativity, Innovation and Business Opportunity

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

This course focuses on policies and practices for effectively managing a diverse workforce in private, public and nonprofit organizations. The current context, legal environment and historical development of equal employment opportunity, affirmative action, and diversity are addressed. Students gain theoretical and practical knowledge to understand beliefs, attitudes, biases, and prejudices to more effectively manage differences in order to enhance organization productivity. A significant amount of time will be focused on racism, origin of racism, and individual responsibility of racism.

Full course description for Managing a Diverse Workforce

Do business firms have obligations besides making as much money as possible for their stockholders? What are their responsibilities, if any, to their employees, their customers, and the wider community? Is it enough to obey the law, or does the law sometimes allow people to do things that are wrong? Do employees have any right to privacy on the job? To 'living wages'? To 'decent' working conditions? Does a seller have any obligation to look out for the interests of the buyer? Isn't it necessary to put the best possible 'spin' on your product and let the buyer look out for him or herself? This course will examine questions like these in light of various theories of ethics and current theories of justice. In addition to considering how we might ideally like people to act, it will also consider the challenges to personal integrity and 'doing the right thing' posed by the real world of business and by the kind of large bureaucratic organizations that dominate it.

Full course description for Business Ethics