Page 165 - University Bulletin

ASDS339 INTRODUCTION TO DATABASE SYSTEMS
Credits: 3 (3,1,0) Prerequisite: ASDS213
This course offers an in-depth introduction to database systems and modeling, aiming to equip
students with the essential skills to engage with databases in real-world scenarios. The course
begins with a comprehensive overview of database systems, including their definitions, historical
evolution, architecture, and diverse applications. As the course advances, students will explore
different data models, giving particular attention to entity-relationship, relational, and other
pertinent models. A significant emphasis will be placed on database query languages and
standards, providing a practical understanding of how to interact with databases effectively. The
course also covers database design, incorporating theoretical foundations and methodological
approaches. Throughout the course, the goal is to introduce the fundamentals of database
systems and prepare students for practical engagement with database technologies in
professional contexts.

ASDS345 Data Warehousing
Credits: 3 (3,0,0) Prerequisite: ASDS213
This course provides an overview of fundamental data warehousing and data mining concepts. It
introduces the concepts and strategies necessary to build and deploy a data warehouse as a
decision support tool for an enterprise. Different data mining techniques e.g. classification,
clustering would also be covered in this course. The course objective is that its successful
completion should enable students to engineer database warehouses and to apply mining on real-
world data repositories.

ASDS370 Machine Learning for Data Science
Credits: 3 (3,1,0) Prerequisite: ASDS213

This course covers the theory and practice of machine learning from a variety of perspectives. It
explores topics such as learning decision trees, neural network learning, statistical learning
methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and
reinforcement learning. Typical assignments include neural network learning for face recognition
and decision tree learning from databases of credit records.

ASDS377 Mathematical Statistics
Credits: 3 (3,1,0) Prerequisite: MATH223 and STAT111
This course describes the most important ideas, theoretical results, and examples of bivariate
probability distributions, sampling distributions and the CLT, functions of random variables,
parameter estimations and hypothesis testing. The course includes the essential fundamentals of
these topics. The emphasis is on calculations, and some applications are mentioned.

ASDS381 Methods for Survey Sampling
Credits: 3 (3,1,0) Prerequisite: STAT111
The main objective of this course is to teach the students the main idea of the sampling methods
from a theoretical and applied perspective. The course covers the main methods used for
samplings, such as simple random sampling, systematic, stratification, cluster sampling,
multistage sampling, unequal selection probability, sampling error estimation methods, and non-
sampling errors.

153 PSU UNDERGRADUATE BULLETIN