Data Science

Graduate Internship
Class: DSCI D591
Section: 13302, 13004
Credits: 1-3
Instructor: Haixu Tang
Synopsis: 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. Please contact the Data Science Graduate Office before enrolling in this course.

Independent Study
Class: DSCI D699
Section: 13006, 13008
Credits: 1-3
Instructor: Haixu Tang
Synopsis: Independent readings and research for MS students under the direction of a faculty member, culminating in a written report. Please contact the Data Science Graduate Office before enrolling in this course.



Informatics

Applied Machine Learning
Class: INFO I526
Section: 13641 (100% Online)
Syllabus: View Document
Credits: 3
Instructor: James "Jimi" Shanahan
Synopsis: The main aim of the course is to provide skills to apply machine learning algorithms on real applications. We will devote less time to learning algorithms and math/theory, and instead spend more time with hands-on skills required for algorithms to work on a variety of datasets.
Entrance Exam: Each prospective student will need to complete an entrance exam for this course. Your entrance exam performance will form an important component in determining your admittance to this course at this time. You may access the exam here.

Data Science On-Ramp
Class: INFO I590
Section: 8430 (100% Online)
Syllabus: View Document
Credits: 1-3
Instructor: Ying Ding
Synopsis: 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 Visualization
Class: INFO I590
Section: 8433
Syllabus: View Document
Credits: 3
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. ... show more

Python
Class: INFO I590
Section: 8431 (100% Online)
Syllabus: View Document
Credits: 3
Instructor: Vel Malbasa
Synopsis: This course provides a gentle, yet intense, introduction to programming using Python for students with 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

SQL and noSQL
Class: INFO I590
Section: 8432 (100% Online)
Syllabus: View Document
Credits: 3
Instructor: Ying Ding
Synopsis: Database is the central focus in data science to store and manage data. Relational database has empowered the main industries for decades and is still widely adopted. In the new era of big data, database landscape is undergoing significant change. Many non-relational databases become an important part of the enterprise data architecture of companies. Relational databases were developed long before the Internet and the Web to tackle the issues of central-controlled data storage and management. NoSQL databases emerged with the rise of Internet and Web applications to connect companies with customers (i.e., online or mobile) and to develop with agility to adapt to faster changes. The new challenges of being agile and being able to accommodate data variablity/data integration drive enterprises to turn to NoSQL database technology. It is important for every data scientist to master the skills of current database and know about the future of databases in a world of NoSQL. This course aims to provide the basic overview of the current database landscape, starting with relational databases, SQL, and moving to several different NoSQL databases, such as XML database and MongoDB. ... show more



School of Public and Environmental Affairs

Statistical Analysis for Effective Decision-Making
Class: SPEA V506
Section: 13796 (100% Online)
Syllabus: Please request with Instructor
Credits: 3
Instructor: Joanna Woronkowicz
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, t-tests, correlation, regression. The focus is on the practical interpretation and application of statistics.... show more



Statistics

Introduction to Statistics
Class: STAT S520
Section: 8320 (100% online)
Syllabus: View Document
Credits: 3
Instructor: Jianyu Wang
Synopsis: This course introduces the basic concepts of statistical inference through a careful study of several important procedures. Topics include 1- and 2-sample location problems, the one-way analysis of variance, and simple linear regression. Most assignments involve applying probability models and/or statistical methods to practical situations and/or actual data sets. S320 is the basic version of this course, intended for undergraduates. It is the gateway to more advanced courses offered by the Department of Statistics. S520 is an expanded version of S320 that covers additional material (see syllabus below). S520 serves two constituencies: Graduate students in quantitative disciplines who are looking for a solid introduction to statistics and who may want to take additional courses in statistics, and graduate students pursuing an M.S. in Applied Statistics who desire a more gentle introduction to the fundamental principles of statistical inference than is provided in the more theoretical STAT S620.... show more