2016-2017 Data Science Courses

DATA 150 Data and Society (4)
Quantitative literacy is increasingly important in our world of information. The primary goal of this course is to learn about data and how it’s used. Along the way, we will learn how to develop basic tools to analyze and visualize data, read and evaluate research claims, and report research findings in honest and ethical ways. (This course may not be taken for credit if a student already has credit for DATA 220.)
DATA 220 Introduction to Statistics (4)
Statistics is the study of data. This course will develop tools for analyzing data from a variety of fields. We follow the process from data gathering (sampling methods and experimental design) to exploratory data analysis (graphs, tables, charts, and summary statistics) to inferential statistics (hypothesis tests and confidence intervals) using simulation and sampling distributions. A key component of the course is the introduction of the statistical language R for analysis and R Markdown for the presentation of statistical analysis. Prerequisites: MATH 105.
DATA  250  Statistical Modeling (4)
The general linear model is a powerful framework for modeling relationships in data analysis. This course establishes the theory and application of regression models from simple and multiple regression through ANOVA and logit/probit models. In addition to building models, we will also learn to diagnose model fit and handle a wide range of possible complications. We will use the statistical language R for analysis and R Markdown for the presentation of statistical analysis. Prerequisites: DATA 220.
DATA  370  Statistical Learning (4)
Statistical learning is a broad term that refers to any statistical technique that seeks to estimate the relationships among data. Modern advances in computational power allow us to use technology to build a wide array of models to analyze increasingly complex data sets. This course will explore the theory and application of statistical learning techniques such as clustering, regression, discriminant analysis, resampling, regularization, splines, generalized additive models, and Bayesian inference. We will use the statistical language R for analysis and R Markdown for the presentation of statistical analysis. Prerequisites: DATA 250 and either MATH 211 or WCSAM 203.
DATA 470  Capstone Project (1)
The capstone project is an opportunity for students to apply the knowledge gained throughout the Data Science minor to an interesting data problem, preferably in conjunction with a research project in their major. The students in the course will work with a mentor in their field of interest as well as the faculty member running the Data Science capstone project to develop a research plan to analyze one or more data sets addressing a topic of interest. All capstone students will meet together one hour a week to share ideas and take advantage of interdisciplinary collaboration. The capstone experience will culminate in a paper and a presentation. Prerequisites: All required core courses in the Data Science minor (WCSAM 110, WCSAM 203, DATA 220, and CMPT 201 or BIOL/PHYS/CHEM 370).

 

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