INDIANA UNIVERSITY DATA SCIENCE SPRING 2016 SEMESTER ADVISING NOTES David Wild - updated January 2016

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1 GENERAL NOTES INDIANA UNIVERSITY DATA SCIENCE SPRING 2016 SEMESTER ADVISING NOTES David Wild - updated January 2016 General information about the program can be found on the website at All general questions, and questions about matters relating to student status and financial matters, should be directed to: IU Data Science Graduate Office, 611 N Park, Bloomington, IN [email protected] phone The office is managed by Kayla Scroggins, Data Science Graduate Services & Records Manager ACADEMIC ADVISING Questions regarding advising should be sent to the [email protected] address. This will get to the two Data Science faculty advisors, Professor David Wild and Professor Ying Ding. Dr David Wild, Director, Data Science Academic Programs and Associate Professor, is the primary advisor for residential students. You can book an advising session in semester time with him on Tuesdays or Wednesday mornings at Please use these sessions sparingly. Residential students should meet in Informatics West 207 (901 E 1oth St); if you would like an online meeting, check the box in the appointment and a link will be provided for the call. Dr Ying Ding, Associate Director, Data Science Online Programs and Associate Professor, is the primary advisor for online students. She is available in her office hours for physical or online meetings Monday and Friday 9-3:30PM open office (drop by anytime). Her physical address is the Main Library LI02. For online meetings she can be contacted on Skype (skype id: ying_ding). Otherwise, send me to schedule an appointment. PATHS AND TRACKS There is understandably some confusion about Paths and Tracks. A path is not a formal part of your degree, but a guide to help you choose courses that are right for you. Courses are classed into Decision Maker or paths. Decision maker courses are focused on the skills needed by data science decision makers such as utilization of data science techniques, social factors, and domain-specific applications. courses are focused on the technical side of data science, often requiring strong programming skills. You do not have to declare a path for your degree, and being on one path doesn t mean you can t take courses from another path. In the course listings below, we tag the classes with paths (note that these tags are currently provisional, and some may change). A track is more formal specialization, and the track determines which courses you can

2 take. The default is the general track, in which you can pick whichever courses you wish. Currently we have just one specialization track, computational & analytic, the requirements of which are listed at the end of this document. We may add more tracks in the future. WHICH COURSE SECTIONS DO I SIGN UP FOR? Many data science courses have multiple sections associated with them. These are generally for different classes of students, and you should take care to sign up for the correct section. Online students. Students registered for the online programs (certificate, MS) should sign up for the section with "Above class for students not in residence on the Bloomington campus". It should also say "Above class taught online". If you see more than one onlinesection, go for the one that says "For data science students only" Residential students taking online class. For most online classes, residential students who are taking an online class should sign up for the section that says "Above class taught online" but not "Above class for students not in residence on the Bloomington campus". You should also be automatically signed up for a residential discussion section. For a few classes, this separate section may not exist, in which case you should sign up for the regular online section (but note that students on visas can normally only take one such "truly online" class per semester) Residential students taking a residential class. Should sign up for normal section without the additional qualifiers. If you see more than one section, go for the one that says "For data science students only".

3 ONLINE COURSES - upcoming semester classes highlighted in green Course Next Class Instructor Path Notes INFO I571 Introduction to Cheminformatics INFO I590 Data Science in Drug Discovery, Health and Translational Medicine Wild Decision Maker Website: Spring 2016 Wild Decision Maker Website: Prerequisites: Ability to perform basic statistical tasks in R; conceptual understanding of machine learning. INFO I590 Management, Access, and Use of Big and Complex Data Fall 2016 Plale Decision Maker & Website INFO I590 Big Data Applications and Analytics INFO I590 Perspectives in Data Science INFO I590 Big Data Open Source Software and Projects Fall 2016 Fox Decision Maker Some programming experience required, Python preferred. Website Spring 2016 Stirling Decision Maker This course will introduce multiple perspectives of the application of data science through recorded interviews with leaders in Silicon Valley companies, and map these to the practical skillsets of the data scientist Spring 2016 Fox / Abdul-Wahid Familiarity in Scripting Languages such as Linux Shell and especially Python. Knowledge of Java helpful but not required. INFO I526 Applied Machine Learning Fall 2016 Natarajan Decision Maker & Rescheduled from Spring Requires programming ability in any one of the following programming languages C / C++/ Java/Python/ Matlab. Basic algebra and probability. CSCI B649 Cloud Computing for Spring 2016 Qiu / Abdul-Wahid This is a programming intensive course. It has

4 Data Intensive Sciences CSCI B649 High Performance Computing similar requirements to the CS graduate level residential version. Students are expected to have weekly (or biweekly) programming homework. General programming experience with Windows or Linux using Java (2-3 years) and scripts is required. A background in parallel and cluster computing is a plus, although not necessary. Fall 2016 Sterling Intermediate C/C++ experience Familiarity with Linux/Unix command-line utilities ILS Z636 Data Semantics Spring 2016 Ding Basic of HTML and XML is necessary. Basic of Java can be helpful. ILS Z637 Information Visualization Spring 2016 Börner / Ginda Decision Maker & ILS Z604 Social and Organizational Informatics of Big Data Rosenbaum & Fichman Decision Maker RESIDENTIAL COURSES - upcoming semester classes highlighted in green. IMPORTANT: Note that the above online classes are only duplicate listed if there is a completely separate residential version of that course. Also note that some classes are cross-listed between departments. You may sign up for either of the crosslisted courses. Please make sure you select the correct section for each class. Course Next Class Instructor Path / Specialization Track Notes CSCI B503: Algorithms Design and Spring 2016 Somogyi CSCI B534: Distributed Systems CSCI B551: Elements of Artificial Spring 2016 Natarajan

5 Intelligence CSCI B552: Knowledge-Based Artificial Intelligence CSCI B553: Neural and Genetic Approaches to Artificial Intelligence CSCI B555: Machine Learning Spring 2016 Raphael CSCI B561: Advanced Database Concepts CSCI B565: Data Mining Spring 2016 Radivojac CSCI B649 Cloud Computing for Data Intensive Sciences CSCI B649 Malware: Threat and Defense Spring 2016 Qiu / Abdul-Wahid This is a programming intensive course. It has similar requirements to the CS graduate level residential version. Students are expected to have weekly (or biweekly) programming homework. General programming experience with Windows or Linux using Java (2-3 years) and scripts is required. A background in parallel and cluster computing is a plus, although not necessary. Spring 2016 Wang CSCI B649 Data Protection Spring 2016 Hill CSCI B649 Privacy in Wearable & Mobile Computer Systems Spring 2016 Kapadia CSCI B649: Network Systems Spring 2016 Swany CSCI B652: Computer Models of Spring 2016 Leake

6 Symbolic Learning CSCI B656: Web mining CSCI B657: Computer Vision Spring 2016 Crandall CSCI B659: Information Theory and Inference CSCI B659: Stochastic Optimization for Machine Learning CSCI B659: Reinforcement Learning for AI CSCI B659: Machine Learning in Bioinformatics CSCI B659: Computation & Linguistic CSCI B661: Applying Machine Learning Techniques in CL Spring 2016 White Spring 2016 White Spring 2016 Tang Spring 2016 Dickinson Spring 2016 Kuebler CSCI B662: Database Systems & Internal Design CSCI B669: Topics in Database and Information Systems: Scientific Data Management and Preservation Decision Maker CSCI B673: Advanced Scientific Computing Spring 2016 Bramley INFO I519: Introduction to Bioinformatics & Decision Maker

7 INFO I520: Security For Networked Systems INFO I529: Machine Learning in Bioinformatics INFO I533: Systems & Protocol Security & Information Assurance Camp Spring 2016 Tang Spring 2016 Myers INFO I573: Programming for Science Informatics INFO I590: Topics in Informatics: Complex Networks and their Applications INFO I526: Applied Machine Learning INFO I590: Topics in Informatics: Complex Systems INFO I590: Topics in Informatics: Relational Probabilistic Models Fall 2016 INFO I590: Large-Scale Social Phenomena INFO I590: Privacy in Wearable & Mobile Computer Systems Spring 2016 DeDeo Spring 2016 Kapadia INFO I590: Data Protection Spring 2016 Hill INFO I590: Technology Innovation INFO I590: Technology Entrepreneurship Spring 2016 Brown Decision Maker Spring 2016 Brown Decision Maker

8 INFO I590: Visual Analytics ILS P536: Advanced Operating Systems ILS P538: Computer Networks C&A ILS Z511: Database Design Ding ILS Z534: Information Retrieval: Theory and Practice Information Science: Spy tech for non Spies Information Science: Health information sources & services Information Science: Information Architecture in Practice Information Science: Sentiment Information Science: Digital Curation Information Science: Intelligence Analytics Information Science: Scholarly Communication Liu / Guo C&A Spring 2016 Choksy Spring 2016 Robbin Decision Maker Spring 2016 Milojevic Spring 2016 Abdul-Mageed Spring 2016 Donaldson Spring 2016 Chosky Spring 2016 Tsou Decision Maker

9 Information Science: Big Data Analytics ILS Z605: Internship in Library and Information Science Spring 2016 Liu Fichman To be arranged with faculty advisor ILS Z636 Data Semantics Spring 2016 Ding Basic of HTML and XML is necessary. Basic of Java can be helpful. ILS Z639: Social Media Mining Spring 2016 Abdul-Mageed ILS Z652: Digital Libraries Walsh Decision Maker STAT S520: Intro to Statistics Fall 2016 Luen C&A STAT S612: Data Management for Reproducible Statistical Spring 2016 Long Check prerequisites: requires advanced statistical STAT S620: Statistical Theory Spring 2016 Womack Check prerequisites: requires advanced statistical STAT S626: Bayesian Data STAT S632: Applied Linear Models II STAT S640: Multivariate Data STAT S650: Time Series STAT S670: Exploratory Data Spring 2016 Manrique-Vallier Check prerequisites: requires advanced statistical Spring 2016 Valdivia Check prerequisites: requires advanced statistical Spring 2016 Wasserman Check prerequisites: requires advanced statistical Spring 2016 Check prerequisites: requires advanced statistical Spring 2017 King Check prerequisites: requires advanced statistical

10 STAT S675: Statistical Learning & High-Dimensional Data Trosset Check prerequisites: requires advanced statistical STAT S681: Causal Interference Spring 2016 An Check prerequisites: requires advanced statistical STAT S681: Longitudinal Data STAT S681: Statistical Network Spring 2016 King Check prerequisites: requires advanced statistical Check prerequisites: requires advanced statistical