This is a student-run computer language workshop made of seven meetings per semester. The course will provide an intermediate/advanced understanding and familiarity with working in R and carrying out data analysis, visualization, and editing of simple and complex datasets.
Focus will be given to both technical details and more general concepts, to allow participants to analyze data and interpret them properly. Therefore, after taking the course, participants should be comfortable with using R to handle data, perform descriptive and basic econometric analyses with R.
This course is designed to help students gain an understanding of fundamental numerical and quantitative skills and their application to everyday life. The focus will be on applying basic mathematical concepts to solve real-world problems, and developing skills in interpreting and working with data in order for students to become able to function effectively as professionals and engaged citizens.
Topics will include problem-solving and back-of-the-envelope calculations, unit conversions, and estimation, percentages and compound interest, linear and other models, data interpretation, analysis and visualization, basic principles of probability, and an introduction to quantitative research and statistics. Another important objective of the course is a clear introduction to and a development of appropriate working knowledge of MS-Excel as well as some of the software’s most common applications in a variety of contexts. This course is offered every semester and does not satisfy the math course requirements for the Interdisciplinary Science major.
Building upon Quantitative Reasoning I numerical and quantitative skills, this course focuses on quantitative research methods and related skills. Students will learn how to use the statistical package R to perform statistical analysis and data visualization, and their applications to business and social sciences. Students will be able to identify, understand, and critique primary and secondary research in industry, scholarly, government, and other specialized applications. They will also gain expertise with the use of large data sets.
Particular emphasis is placed on issues and themes currently considered most central to human development including social progress, economics, efficiency, equity, participation and freedom, sustainability and human security.