Summer 2017 Term

Note: Unless otherwise specified, all courses listed are worth 3 credit hours.

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Department: Statistics

Class: STAT S520 Introduction to Statistics
Section: 13270
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 datasets. 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. 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.

Department: Informatics

Class: INFO I590 SQL and NOSQL
Section: 13950
Instructor: Ying Ding
Synopsis: A database is the central focus in data science to store and manage data. Relational databases have empowered major industries for decades and are still widely adopted. In our new era of Big Data, the 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 agility to adapt to faster changes. The new challenges of being agile and being able to accommodate data variablity/data integration drove enterprises to turn to NoSQL database technology. It is important for every data scientist to master the skills of current databases 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 and SQL, and moving to several different NoSQL databases, such as XML database and MongoDB.

Class: INFO I590 Network Science
Section: 13951

Instructor: Yong-Yeol Ahn
Synopsis: Networks are everywhere. We can easily find network structure in many complex systems around us: our cells, brains, society, etc. The inherent generality of network approach allowed wide applications of network theory to flourish across diverse fields including biology, sociology, and epidemiology. The questions that we will address in the class are the following: why do networks matter? What are the fundamental theories to understand the structure and dynamics of networks? How has it been applied to other fields? What are the frontiers of the research? We will explore key papers ranging from the fundamental theory to the various applications of network theory. This course will focus more on round-table discussion between students than presentation. Students will work on research projects in groups and finish a paper at the end of the class.

Class: INFO I590 Applied Data Science
Section: 8722

Instructor: Joanne Luciano
Synopsis: This purpose of this course is to provide Data Science graduate students with practical experience applying their data science skill sets to real-world datasets. Data for the first offering of this course in 2017 used a deidentified clinical trials dataset provided by Eli Lilly (agreement already in place with IU), but subsequent offerings could include public data or data provided by other industry partners. Students will be led through the full data analysis process of data preparation, model planning, model building, analysis, and communication of results. Students will meet (virtually or physically) daily to devise a plan.

Class: INFO I590 Data Science On-Ramp
Section: 13947
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.

Class: INFO I590 Python
Section: 13949
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.

Class: INFO I591 Graduate Internship
Instructor: Ying Ding
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.

Class: INFO I699 Independent Study
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.

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