Computer Science

Applied Algorithms
Class: CSCI B505
Section: 33026 (online), 30571
Syllabus: View document
Instructor: Funda Ergun
Synopsis: The course studies the design, implementation, and analysis of algorithms and data structures as applied to real world problems. Topics include divide-and-conquer, optimization, and randomized algorithms applied to problems such as sorting, searching, and graph analysis. Students will learn about trees, hash tables, heaps, and graphs.

Elements of Artificial Intelligence
Class: CSCI B551
Section: 33013 (online), 12190, 1664
Instructor: David Crandall
Synopsis: Introduction to major issues and approaches in artificial intelligence. Principles of reactive, goal-based, and utility-based agents. Problem-solving and search. Knowledge representation and design of representational vocabularies. Inference and theorem proving, reasoning under uncertainty, and planning. Overview of machine learning.



Information and Library Science

Social Media Mining
Class: ILS Z639
Section: 32775 (online), 12144
Instructor: Vincent Malic (online), A. Riddell
Synopsis: This course provides a graduate-level introduction to social media mining and methods. It offers hands-on experience mining social data for social meaning extraction (focusing on sentiment analysis) using automated methods and machine learning technologies. We will read, discuss, and critique claims and findings from contemporary research related to SMM.



Statistics

Introduction to Statistics
Class: STAT S520
Section: 36267 (online), 9845, 14505, 31275
Instructor: Jianyu Wang (online), Arturo Valdivia, Brad Luen, Jaime Ramos
Synopsis:  TBA



Informatics

Security for Networked Systems
Class: INFO I520
Section: 14127 (online), 11366
Instructor: Raquel Hill
Synopsis: This course is an extensive survey of system and network security. Course materials cover the threats to information confidentiality, integrity and availability, and the defense mechanisms that control such threats. It provides the foundation for more advanced security courses and hands-on experiences through course projects.

Big Data Applications and Analytics
Class: INFO I523
Section: 13310 (online), 13307
Instructor: Gregor Von Laszewski
Synopsis: The Big Data Applications & Analytics course is an overview course in Data Science and covers the applications and technologies (data analytics and clouds) needed to process the application data. It is organized around this rallying cry: Use Clouds running Data Analytics Collaboratively processing Big Data to solve problems in XInformatics.

Organizational Informatics & Economics of Security
Class: INFO I525
Section: 33034 (online), 32915
Instructor: Jean Camp
Synopsis: Security technologies make explicit organizational choices that allocate power. Security implementations allocate risk, determine authority, reify or alter relationships, and determine trust extended to organizational participants. The course begins with an introduction to relevant definitions (security, privacy, trust) and then moves to a series of timely case studies of security technologies.... show more

Management, Access, and Use of Big and Complex Data
Class: INFO I535
Section: 33630 (online), 33628
Instructor: Inna Kouper
Synopsis: Data is abundant, offering potential for new discovery along with economic and social gain. But data has its difficulties. It can be noisy and inadequately contextualized. There can be too big a gap from data to knowledge, or due to limits in technology or policy not easily combined with other data. This course will examine the underlying principles and technologies needed to capture data, as well as clean, contextualize, store, access, and trust it for a repurposed use. Specifically we will cover 1) distributed systems and database concepts underlying noSQL and graph databases, 2) best practices in data pipelines, 3) foundational concepts in metadata and provenance plus examples, and 4) developing theory in data trust and its role in reuse.... show more

Applied Data Mining
Class: INFO I590
Section: 33631
Instructor: Mehmet Dalkilic
Synopsis: The learning objective for this course is to broadly familiarize students with the elements of data mining. Students are expected to be proficient in algebra, as well as have familiarity with probability and calculus. While proficiency in R is necessary, the first week will be a refresher. Additionally, since there will be a fair amount of writing, knowledge of LATEX, a type-setting language, is required. Although there are many freely available, MikTeX is strongly suggested for its ease of use.
There are five major learning outcomes of equal importance:

· Knowledge Area
· Overall Data Mining Process
· Elements of the Process
· Machine Learning Algorithms
· Interpretation of Data Mining

More plainly, the student should be able to assess a potential data mining problem, employ the process (which includes the appropriate algorithm), interpret the results, and suggest an outcome clearly and succinctly.
... show more

Applied Data Science
Class: INFO I590
Section: 33632 (online), 33627
Syllabus: View document
Instructor: Joanne Luciano
Synopsis: The aim of the Applied Data Science course is to provide the skills needed to apply data science principles on real world applications at every stage in the data science workflow. The course is organized around each stage covering the algorithms, best practices, and evaluation criteria. Both good and bad application examples will be discussed to help the student develop an intuition and deeper understanding of the choice of algorithm for the data, and the development of the best practices and methods for evaluating results of different approaches. Students will learn Tableau and use it to to visually analyze and report data.... show more

Data Science for Drug Discovery
Class: INFO I590
Section: 33633
Syllabus View document
Instructor: Joanne Luciano
Synopsis: With exploding healthcare costs, greater longevity and the widespread health challenges of diabetes, obesity, cancer and cardiovascular disease, today's medicine and healthcare will be a primary scientific and economic focus for the remainder of this century. Informatics and big data promise an understanding of health, disease and treatment on a scale never before imagined. This course will address the big data techniques that are being used in the drug discovery, healthcare and translational medicine domains. Some specific topics covered will include large-scale, integrated molecular datasets; cheminformatics and bioinformatics in a big data domain; storing and data mining of electronic medical records; visualization and mapping of diseases; bridging the clinical and molecular; smart devices for smart health; and data mining for healthcare economics.... show more

Data Science On-Ramp
Class: INFO I590
Section: 33634
Syllabus:View Document
Instructor: Ying Ding
Credit Hours: 1 - 3
Synopsis: A course dealing with self-paced modules to build and strengthen core competencies necessary for Data Science curriculum. Individual lessons vary from beginner to intermediate and will cover C++, MongoDB, R, Java, Python, Tableau, SQL, Hadoop/MapReduce, Spark, Scala, Github, Web Scraping, and Text Mining (NLP). If you would like descriptions of each lesson and how these will be mapped to credit, please consult Professor Ying Ding for more information.... show more

Data Semantics
Class: INFO I590
Section: 13972
Instructor: Ying Ding
Synopsis: The class explores the technologies of the Semantic Web by examining the application of technologies to WWW information delivery and the principles of formal logic and computation guiding their development.

Data Visualization
Class: INFO I590
Section: 14120 (online), 33510, 9363
Instructor: Yong-Yeol Ahn
Synopsis: From dashboards in a car to cutting-edge scientific papers, we extensively use visual representation of data. As our world becomes increasingly connected and digitized and as more decisions are being driven by data, data visualization is becoming a critical skill for every knowledge worker. In this course we will learn fundamentals of data visualization and create visualizations that can provide insights into complex datasets.

Python
Class: INFO I590
Section: 33636
Instructor: Vel Melbasa
Synopsis: This course provides a gentle yet intense introduction to programming with Python for students who have little or no prior experience in programming. Python, an open-source language that allows rapid application development of both large and small software ystems, is object-oriented by design and provides an excellent platform for learning the basics of language programming. The course will focus on planning and organizing programs, and developing high quality working software that solves real problems.... show more

Graduate Internship
Class: INFO I591
Section: 14072, 14073
Instructor: David Wild
Section: 7839, 7840
Instructor: Steven Myers
Credit Hours: 0 - 6
Synopsis: Students gain professional work experience in an industry or research organization setting, using skills and knowledge acquired in Informatics course work. May be repeated for a maximum of 6 credit hours.

Independent Study
Class: INFO I699
Section: 14074, 14075
Instructor: David Wild
Section: 6736
Instructor: Martin Siegel
Credit Hours: 1 - 3
Synopsis: Independent readings and research for MS students under the direction of a faculty member, culminating in a written report.



School of Public and Environmental Affairs

Statistical Analysis for Effective Decision-Making
Class: SPEA V506
Section:9717
Syllabus:View document
Instructor:Kand McQueen
Synopsis: An introduction to statistics. Nature of statistical data. Ordering and manipulation of data. Measures of central tendency and dispersion. Elementary probability. Concepts of statistical inference decision: estimation and hypothesis testing. Special topics discussed may include regression and correlation, analysis of variance, nonparametric methods.This course will provide an introduction to the analysis of quantitative data via statistical analyses. Topics covered include, but are not limited to, descriptive statistics, z-scores, probability, z-tests, ttests, correlation, regression. The focus is on the practical interpretation and application of statistics.... show more