Data Science
STAT 215 - Introduction to Data Science
Explore the foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Introduces statistical programming and inference, with hands-on analysis of real-world datasets, including economic data, document collections, and social networks.
DATA 312 - Data Analysis with Python
An introduction to statistical data analysis using Python. Learn about data preparation and transformation, macros, and descriptive statistics. Topics include diagnostics, t-procedures, ANOVA, nonparametrics, cross-tabulation, chi-squared, correlation, and regression.
STAT 321 - Probability Through Simulation
Learn introductory probability using simulation methods. Topics include the estimation and accuracy of probabilities using repeated sampling and simulating conditional probabilities using conditional programming techniques.
DATA 340 - Data Science Ethics
Understanding ethical implications of one’s personal data, the risks and rewards of data collection and surveillance, and the needs for policy, advocacy, and privacy monitoring. Implications of algorithms and models.
STAT 450 - Introduction to R for Data Science
An introduction to the R environment and data analysis. Topics include use of dataframes and lists, importing and exporting data files, writing user-defined functions, R packages, regression, Principle Components Analysis, and clustering.
STAT 451 - Introduction to Data Visualization
Studies data visualization and interactive data exploration. Topics include importing, exporting and data merging, graphs and charts, interactive maps, and meaningful visual representations of complex statistics.