Page 164 - University Bulletin

ASDS249 Introduction to Data Science
Credits: 3 (3,0,0) Prerequisite: ASDS225
The Introduction to Data Science course will survey the foundational topics in Data science, Data
Manipulation, Data Analysis with Statistics and Machine Learning. Students will learn Python,
Python data structures including NumPy, Pandas and visualization techniques using Matplotlib.
Students will learn how to apply basic machine-learning concepts for classifications and
regression. Students will work on a group project to apply learned concepts to one of the many
Data Science applications.

ASDS311 Applied Regression Analysis
Credits: 3 (3,0,0) Prerequisite: STAT112
This course is about regression, a powerful and widely used data analysis technique. Students will
learn how to use regression by analyzing a variety of real-world problems. Heavy emphasis will be
placed on the analysis of actual datasets. Topics covered include simple and multiple regression,
prediction, variable selection, causal inference, residual diagnostics, classification (logistic
regression), and time series.

ASDS321 Applied Multivariate Statistics
Credits: 3 (3,0,0) Prerequisite: STAT112 and ASDS311
This course introduces the students to bivariate and multivariate distributions. This includes
multivariate normal distributions, analysis of multivariate linear models, repeated measures,
growth curve, and profile analysis, canonical correlation analysis, principal components, factor
analysis, discrimination, classification, clustering, and Copula models.

ASDS323 Basic Econometrics
Credits: 3 (3,0,0) Prerequisite: ASDS311
This course introduces the linear regression models, functional forms, choice of the functional
forms, linear and log-linear models, regression on standardized variables, measures of goodness
of fit qualitative explanatory variables regression models multicollinearity, heteroscedasticity,
autocorrelation, and model specification. Use of dummy variables.

ASDS329 Operation Research
Credits: 3 (3,0,0) Prerequisite: MATH223
This course covers various topics including; the history and definition of Operational Research.
Introduction to linear programming. Formulation of LP model, Matrix Form, Canonical Form,
Standard Form. Duality theory; Primal and dual form. Graphical solution of two variables. Row
operations, Gaussian elimination. Simplex method. Network programming, Transportation,
assignment, and shortest path problems. Integer programming: Gomoray's cutting plane method,
Branch and Bound method, Introduction to CPM and PERT techniques. Inventory control models.
ABC analysis and selective inventory management. Queuing Models.

152 PSU UNDERGRADUATE BULLETIN