Course Information: Applied Stats For Data Science  (21321)

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.

Required Materials:		Not Yet Available
Technical Requirements:		Not Yet Available
Pre-Requisite:		(( (DATA 2501 with minimum grade: C ) OR ( DATA 2501 with minimum grade: TRC ) AND ( (CILS 1130 with minimum grade: C ) OR ( CILS 1130 with minimum grade: TRC ) OR ( CSCI 1130 with minimum grade: C ) OR ( CSCI 1130 with minimum grade: TRC) ) AND ( (BUSA 2182 with minimum grade: C ) OR ( BUSA 2182 with minimum grade: TRC) ) OR ( SOCI 2101 with minimum grade: C ) OR ( SOCI 2101 with minimum grade: TRC ) OR ( MATH 1401 with minimum grade: C ) OR ( MATH 1401 with minimum grade: TRC) ))
Link to View/Purchase Book:		View Book with CRN   :   View Book with Course Info
Course Attributes:		1. Synchronous

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