CILS 3111 - Applied Statistics for Data Science

Credits: 3

Introduction to intermediate level applied statistics and techniques of statistical modeling. The course will utilize available primary and secondary data sets in improving the conceptual understanding. The course will involve use of programming through scripting language (Python) and statistical package R and STATA. The focus of the course will be on using understanding the following concepts by analyzing data in Python, R and STATA: inferential statistics, data mining, visualization, linear regression, decision trees, logistics regression, k-means clustering, hierarchical clustering, collaborative filtering, random forests, resampling methods, classification, singular value decomposition, regularization, choosing models and fitting parameters, generalized linear models etc.

Prerequisite(s): MATH 1113 and CILS 1130/CSCI 1130 and BUSA 2182 or SOCI 2101 or MATH 1401
Equivalent
DATA 3111