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A total of 19 courses have been found.
Introduction to epidemiology, biostatistics, and the interdisciplinary nature of public health research and practice; how public policy and population-based interventions are subsequently shaped by public health evidence.

This course is designed to introduce evidence-based public health through the various sciences that inform and shape public health research and practice, public policy, and population-based interventions, with particular emphasis on biostatistics and epidemiology. Prior knowledge of biostatistics, epidemiology, and/or public health are not necessary to complete this course successfully. Topics include but are not limited to population health, sampling, public health measures, descriptive and inferential statistics, probability, hypothesis testing, defining causes of health outcomes, epidemiologic study designs, identifying alternative explanations for observed associations between exposure and disease, and the application of biostatistical and epidemiological principles to understanding public health challenges like heart disease prevention, cancer screening, and vaccination. This course is team taught by instructors from the departments of Biostatistics and Epidemiology. Grades will be based on midterm exams, a cumulative final exam, weekly homework assignments, weekly preparatory assignments, and attendance and participation.

 

At the end of the course the student will be able to:

  • Understand how epidemiology and biostatistics generate and evaluate data to support evidence-based public health research and practice
  • Within a public health context, apply and interpret foundational biostatistical and epidemiological methods and communicate results accurately
  • Locate, use, evaluate, and interpret public health information that is accessible in their day-to-day lives
Quantitative or Formal Reasoning
Introduction to computing; broad overview of discipline; necessary skills and concepts for effective application of computing resources in student's profession.

Introduction to computing for students seeking a broad overview of the discipline. Students acquire necessary concepts and skills to apply computing principles and resources effectively in their chosen profession. Topics include networking and communications, privacy, ethics and security, the limits of computing, and algorithmic problem solving. Lab assignments explore a variety of software tools to support decision making with an emphasis on HTML and CSS for building web pages, a gentle introduction to JavaScript programming language to program web pages, spreadsheet advanced features and database management systems for data processing and querying. This course is not part of the computer science or informatics majors or minors. The course is taught by a faculty member; lab sections are led by TAs.

Quantitative or Formal Reasoning
Introduction to study of algorithms; how computers work, simple algorithms and their efficiency, networking, databases, artificial intelligence, graphics, simulation, modeling, security, and social impact of computing; hands-on introduction to programming concepts with Python; for students in data-intensive disciplines.

Introductory course in computer science and the study of algorithms appropriate for students in data-intensive disciplines. Topics include how computers work, simple algorithms and their efficiency, networking, databases, artificial intelligence, graphics, simulation and modeling, security and the social impact of computing. The course also includes a gentle hands-on introduction to programming concepts with Python. The course is taught by a faculty member, lab sections are led by TAs.

Quantitative or Formal Reasoning
Introduction to programming using Python; programming constructs, data types, problem-solving strategies, data structures, object-oriented programming.

This course introduces students to computational problem solving and algorithmic thinking by providing them with intensive programming experience. This is the introduction to computer science course for CS majors and minors, Data Science majors, CS&E majors, and students from other disciplines (e.g., mathematics, statistics) where a solid foundation in computer science as well as fluency in computer programming is required. Prior programming experience is not required, although some students will have had some previous exposure to programming. The course consists of a lecture, taught three times a week and a 75-minute lab-based discussion period, led by a TA. The labs are used for programming practice under the guidance of a TA in a small classroom atmosphere.  Topics include algorithm correctness and run-time analysis, data representation and manipulation, control strategies, functions, recursion, objects and classes and the course will use Python (more specifically, Python 3) as the programming language. Course evaluation will be based on regular in-class quizzes, weekly programming exercises, weekly labs, 2-3 programming projects, 2 midterms and 1 final. Lectures are taught by a faculty member; discussion sections are led by TAs.

Prerequisites: (MATH:1010 with a minimum grade of C- and MATH:1340 with a minimum grade of C-) or (ALEKS score of 45 or higher and MATH:1010 with a minimum grade of C-) or ALEKS score of 75 or higher or MATH:1020 with a minimum grade of C- or (MATH:1005 with a minimum grade of C- and MATH:1010 with a minimum grade of C-) or MPT Level 3 score of 9 or higher or MATH:1460 with a minimum grade of C- or MATH:1350 with a minimum grade of C- or MATH:1380 with a minimum grade of C- or MATH:1850 with a minimum grade of C-
Quantitative or Formal Reasoning

Gain experience working with geospatial technology, such as geographic information systems (GIS) and remote sensing, using geospatial data and analysis to illuminate and improve sustainability issues that face current and future generations. GE: Sustainability.

The Earth is undergoing an era of rapid change. Understanding this change and its impacts on life on Earth depends on systematically analyzing and interpreting evolving data, tools, and theories that are highly interdisciplinary. Spatial technologies, such as geographic information systems (GIS) and remote sensing, are uniquely positioned to tackle sustainability issues to increase resiliency in a changing planet. There is a sizeable amount of spatial data that has become accessible through platforms such as Google Earth Engine. This class will introduce students to introductory geospatial skills using inquiry-based activities to build success in basic geospatial data analysis and critical spatial thinking. Through this class, students will use cutting-edge technology to examine sustainability issues such as urban heat islands, glacier retreats, deforestation, and drought. Ultimately the goal is for students to gain experience working with different types of geospatial data used to illuminate and improve sustainability issues that face our current and future generations.

Learning Objectives:

• Extend knowledge of the underlying physics to execute geospatial data analysis through the implementation of geospatial programming. 
• Connect how geospatial data and analysis can support a systems-approach to sustainability concepts and topics.
• Critically examine geospatial datasets and visualizations regarding social-environmental issues to appraise how these may or may not be misleading to the general public by supporting your ideas with evidence and reason.

Quantitative or Formal Reasoning Sustainability
Semantics and sentence structure of English; word meanings, meaning connected to truth conditions, reasoning based on logical connectives and quantifiers, evaluation of valid and invalid arguments.

This course is an introduction to basic, fundamental principles of logic, covering propositional and quantificational logic. We use these principles to explore ways in which language is used to express rational thought. Topics include logical relations between statements, logical connectives, argument structures, valid inference patterns, and common fallacies.

Quantitative or Formal Reasoning
Functions, relations, coordinate systems; properties and graphs of algebraic, trigonometric, logarithmic, exponential functions; inverse trigonometric functions; properties of lines, conic sections.

This course includes in one semester the essentials of analytic geometry, high school algebra, and trigonometry needed for calculus. It is roughly equivalent to MATH:1005 and MATH:1010 compressed into one semester. Emphasis is on the role of functions and analytic geometry. Topics include functions, coordinate systems; properties and graphs of algebraic, trigonometric, logarithmic, exponential functions; inverse trigonometric functions; and properties of lines, circles, and other conics. This course is not intended for those learning graphing, logarithms, exponentials, or trigonometry for the first time. Such students should take the appropriate lower-level course or courses such as MATH:1005 or MATH:1010. Students are encouraged to use the Math Tutorial Laboratory. The course is taught in individual sections by TAs.

Prerequisites: MATH:1010 with a minimum grade of C- or MATH:1005 with a minimum grade of C- or MPT Level 3 score of 9 or higher or ALEKS score of 60 or higher or MATH:1340 with a minimum grade of C-
Quantitative or Formal Reasoning
Algebraic techniques and modeling; quantitative methods for treating problems that arise in management and economic sciences; topics include algebra techniques, functions and functional models, exponential and logarithmic functions and models, and a thorough introduction to differential calculus; examples and applications from management, economic sciences, and related areas; for students planning to major in business.
Prerequisites: MATH:1005 with a minimum grade of C- or MATH:1340 with a minimum grade of C- or ALEKS score of 55 or higher or MPT Level 3 score of 9 or higher
Quantitative or Formal Reasoning
Relations, functions, coordinate systems, graphing, polynomials, trigonometric functions, logarithmic and exponential functions; discrete mathematics, probability; examples and applications from biological sciences.

This course consists largely of precalculus topics, but also includes a substantial treatment of probability (probability is important in biology and usually not emphasized in high school). It is similar to MATH:1020, except for the biology emphasis and probability topics. The "precalculus" topics include relations, functions, coordinate systems, graphing, polynomials, trigonometric functions, and logarithmic and exponential functions. Probability topics include random experiments and random variables, algebra of sets, methods of enumeration, sampling, conditional probability, and distributions of discrete types. Examples and applications are chosen from the biological sciences. Students are encouraged to use the Math Tutorial Lab. Grades are based on exams, homework, and quizzes. Three hours of lectures are given weekly by a faculty member are complemented by two small-group discussions led by a TA.

Prerequisites: MATH:1005 with a minimum grade of C- or MATH:1340 with a minimum grade of C- or ALEKS score of 55 or higher or MATH:1010 with a minimum grade of C- or MPT Level 3 score of 9 or higher
Quantitative or Formal Reasoning

One-semester survey of calculus for students in biological or life sciences; nontheoretical treatment of differential and integral calculus; brief introduction to differential equations and probability with calculus, with applications to the life sciences.

This course is a one-semester survey of calculus primarily for students in the biological or life sciences. It includes a non-theoretical treatment of differential and integral calculus and a brief introduction to differential equations and probability with calculus, with applications to the life sciences. Three lectures given weekly by a faculty member are complemented by two small group discussions led by a TA. Students desiring a one-semester terminal calculus course may take this course; life science students wanting a more thorough course or planning to take mathematics beyond first-year calculus should take MATH:1850 and MATH:1860 (traditional calculus sequence) instead of this course. A graphing calculator is usually required. Students are encouraged to use the Math Tutorial Laboratory for additional help.

Prerequisites: MATH:1440 with a minimum grade of C- or MATH:1020 with a minimum grade of C- or (MATH:1005 with a minimum grade of C- and MATH:1010 with a minimum grade of C-) or ALEKS score of 70 or higher or (ALEKS score of 55 or higher and MATH:1010 with a minimum grade of C-) or (MATH:1010 with a minimum grade of C- and MATH:1340 with a minimum grade of C-) or MPT Level 3 score of 9 or higher
Quantitative or Formal Reasoning
Limits, derivatives, max/min, other applications, mean-value theorem, approximating functions, concavity, curve sketching, exponential models; Riemann sums, fundamental theorem; integration techniques, improper integrals, approximations.

This is the first semester of a five-semester mathematics sequence for engineering students, but not restricted to engineering students. This course is a redesigned version of a traditional first-semester calculus course with a little more emphasis on techniques of integration. The course is taught by a faculty member in a lecture of about 120 students meeting three times a week and with two one-hour discussion sections taught by a TA. Students are encouraged to use the Math Tutorial Laboratory for additional help.

 

Prerequisites: (MATH:1010 with a minimum grade of C- and MATH:1005 with a minimum grade of C-) or MPT Level 3 score of 9 or higher or ALEKS score of 75 or higher or (MATH:1380 with a minimum grade of C- and MATH:1010 with a minimum grade of C-) or MATH:1020 with a minimum grade of C- or MATH:1460 with a minimum grade of C- or (MATH:1010 with a minimum grade of C- and ALEKS score of 55 or higher) or (MATH:1340 with a minimum grade of C- and MATH:1010 with a minimum grade of C-)
Quantitative or Formal Reasoning

Calculus I 4 s.h.

Fundamental concepts, limits, methods, and techniques of differential calculus of a single variable; definite and indefinite integrals, substitution rule, fundamental theorem of calculus; applications including graphing, extreme values, areas, and volumes.

Topics include fundamental concepts, methods, and techniques of integral and differential calculus of a single variable. The sequence MATH:1850 and MATH:1860 is one of the basic entry-level mathematics courses for students in the mathematical and physical sciences. The course is designed to be a half-year course; it is not, in general, recommended that students plan to take MATH:1850 and not MATH:1860. Engineering students and others who plan to take MATH:1560, MATH:2550, and MATH:2560 should take MATH:1550. The course is taught in either of two formats. In one format, students meet in sections of 60, three times per week with a faculty member, and two times a week for discussions with a TA in sections of 20 students. The second format has four meetings per week in a class of about 30, with one instructor (faculty or experienced TA). With either format, Math Lab tutoring is offered for additional help.

Prerequisites: (MATH:1010 with a minimum grade of C- and MATH:1380 with a minimum grade of C-) or MATH:1460 with a minimum grade of C- or ALEKS score of 75 or higher or MPT Level 3 score of 9 or higher or (ALEKS score of 55 or higher and MATH:1010 with a minimum grade of C-) or MATH:1020 with a minimum grade of C- or (MATH:1340 with a minimum grade of C- and MATH:1010 with a minimum grade of C-) or (MATH:1005 with a minimum grade of C- and MATH:1010 with a minimum grade of C-)
Quantitative or Formal Reasoning

Critical thinking and its application to arguments and debates.

Socrates held that the only way to arrive at the truth is by an honest and objective use of critical reasoning. This course covers some of the main methods and principles that can be used to objectively evaluate whether an argument is good or bad. These techniques will be illustrated by considering controversial topics in ethics, science, and religion, among others. We look at how to recognize an argument before evaluating it, and Socrates' distinction between sophistry (persuasive techniques) and philosophy (rational argument). Methods for objectively evaluating deductive and inductive arguments will both be discussed. We also consider the difference between deductive proof and providing evidence for (or against) a given theory such as evolution or creationism.


Students who have acquired reasoning skills by learning and practicing these distinctly philosophical methods obtain better scores on professional school entrance exams such as the LSAT, GMAT, MCAT, and GRE. These skills are extremely valuable in almost any course or job that involves creative thinking, writing and analytical work.


The course satisfies the General Education requirement in Quantitative and Formal Reasoning. It is also a good way of preparing for other courses in philosophy such as Introduction to Philosophy, Twentieth-Century Philosophy, Analytic Ethics, and of course Introduction to Symbolic logic (which is a requirement for a major in Philosophy).


Coursework involves doing a relatively small amount of scheduled reading from the textbook, and discussing the homework exercises at the end of each section in the textbook. Online homework exercises provide immediate and customized feedback. Exams are closely modeled on the exercises, most of which are answered in class.


This course has no prerequisites.


Our textbook will be the fifth edition of Baronett's Logic (OUP), in the form of an eBook. The ICON Direct program will be used to provide required course materials via your ICON course site. Your U-Bill will be charged automatically after your course has started, unless you opt out prior to the last day for tuition and fee reduction course deadline. Specific opt out information will be provided in the course syllabus and in the opt out tool.

Quantitative or Formal Reasoning
What is information? What does it teach us about societies and cultures? How is information used to shape societies and even personal preferences? What types of information are there and how can we understand and use them? Students work with faculty from multiple disciplines to investigate these questions using inquiry-based activities to build success in critical thinking and teamwork.

Does data matter? How do societies create information and use data? What can we learn about the past and ourselves from the data that we generate individually and as societies?  Students work with faculty from multiple disciplines to investigate these questions through contemporary and historical examples, using inquiry-based activities to build success in basic data skills, critical thinking, and teamwork.  They gain experience working with different types of data used to illuminate and improve politics, literature, entertainment, public health, and other areas of society and culture.    

 

Quantitative or Formal Reasoning
Graphing techniques for presenting data, descriptive statistics, correlation, regression, prediction, logic of statistical inference, elementary probability models, estimation and tests of significance.
Requirements:

one year of high school algebra or MATH:0100

Quantitative or Formal Reasoning
Foundational knowledge in psychological research methods and corresponding statistical concepts; basic concepts of statistics, statistical inference, and research design as applied in psychological research; study of descriptive statistics, measurement, survey design, correlational analyses, and regression analysis; first in a sequence of two courses.

Understanding research and the ability to interpret statistics are two key characteristics of a trained scientist.  This course is designed to introduce students to the science of psychology by building a foundation of knowledge in psychological research methods and the associated statistical procedures.  Students will develop an understanding of univariate and bivariate data, measurement, survey design, descriptive statistics, correlation, and regression analysis.  Upon completion of this course, students should be able to design, analyze, and interpret research using a correlational design.

Prerequisites: PSY:1001 or PSY:2701
Quantitative or Formal Reasoning

Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical sciences; focus on critical approach to statistical evidence.

Requirements:

one year of high school algebra or MATH:0100 

Quantitative or Formal Reasoning
Descriptive statistics, graphical presentation, elementary probability, estimation and testing, regression, correlation; statistical computer packages. Quantitative or Formal Reasoning
Methods of data description and analysis using SAS; descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data. Quantitative or Formal Reasoning