About This Course
This course focuses on data analysis in settings where the data is so large, dispersed or messy that machine-processing is required to gather, clean and transform it into forms suitable for analysis. We also study computer-based techniques for the analysis of such data, including machine data visualization and machinelearning. Finally we consider how the practice of reproducible research and the development of interactive web-based applications can enhance communication of the results of data analysis.
Course At A Glance
An introductory study of statistics, including such topics as numerical and graphical descriptive statistics, sampling methods and design of studies and experiments, basic probability and the distribution of sampling statistics; and inferential procedures such as confidence intervals and tests of hypothesis. This course does not count toward a major or a minor in Mathematics.
Developing algorithms to solve problems and using the computer as a means to implement algorithms and to automate tasks. The course includes the study of a modern computer language along with the programming paradigms that it represents. Topics include variables, control structures, data structures, objects and reuse of code.
First course in an introductory, algebra-based, physics sequence for college students. Topics include mechanics, heat, thermodynamics, sound, and waves. Laboratory.