2023-2024 Handbook for the Master of Data Science Degree
Vision and Mission of Data Science Program
Improve the Way the World Uses Data
As a student of the Data Science Program, you’ll prepare to become the data professional our evolving world needs: a holistically trained expert with the vision and skills to use data to solve problems, unite communities, prevent disasters, transform industries, and most importantly, improve lives.
The Master of Data Science program gives our students a deep set of core competencies to see what tomorrow can be, and shape it every day:
Prepare students for courses in statistics, ML, data management and engineering
Empower students to apply computation and inferential thinking to tackle real-world problems
Enable students to start careers as data scientists by providing experience with data tools and techniques
Contact Information
Haixu Tang Director of Data Science Academic Programs Professor of Informatics and Computing
Patrick Shih Director of Graduate Studies for Data Science Associate Professor of Informatics
Data science is the mining, collecting, analyzing, managing, and storing data to help make data driven decisions in e-commerce, finance, government, healthcare, science, social networking, telecommunications, politics, utilities, smart meters, education, aerospace, etc. By collecting, analyzing, managing, and storing data, businesses can run more efficiently and make data-driven business decisions.
To prepare for a career in data science, students need to be proficient in math, statistics, and computer programming such as Python or R. Students need to understand the data to analyze and interpret the data in a meaningful way. To visualize the data, data scientists often use Tableau, Hadoop, or Apache Spark.
According to KDNuggets, “data scientists are highly educated – 88% have at least a master’s degree.” Their undergraduate background is in computer science, statistics, social science, or physical science.
The main difference between a data scientist and a computer scientist is that a computer scientist develops software and data scientists use the software developed by computer scientists to analyze and interpret the data and identify trends.
If you like to mine, collect, analyze, manage, and store data, perhaps you should pursue a master’s degree in data science degree as data scientists mine, collect, analyze, manage, and store data to help make data driven decisions. Data scientists have a good understanding of the data by asking and answering questions as they do their analysis. They are adept in pulling data from multiple sources, cleaning up the data, and analyzing the data to help make sound business decisions. By analyzing the data, the data scientist can make suggestions as to how to improve the process or how to make the process more efficient. When presenting the data to stakeholders, the data scientist designs, creates, and builds data models and data visualizations to make the data easier to understand.
To be competitive in the job market, a large majority of companies are looking for students who have a bachelor’s degree coupled with a master’s degree in data science. The most common data science job titles are data scientist, data architect, data engineer, business analyst, or data analyst.
If you like to build new things, perhaps you should pursue a master’s degree in computer science as computer scientists design, create, test, document, and debug code, software, and mobile applications. Often computer scientists collaborate with other computer scientists and their teams in developing a larger piece of software, application, or computer system.
To be competitive in the computer science job market, you need at least a bachelor’s degree in computer science. Students who have a master’s degree in computer science are paid more, have more responsibility, and more room for advancement in a company. The most common computer science job is software development engineer, software developer, Java developers, systems engineer, or network engineer.
It is expected that students who have degrees in data science and computer science will be in high demand for at least the next five to ten years. By earning a master’s degree in data science or computer science coupled with your undergraduate degree will give you an edge on the job market.
The Master of Data Science degree is interdisciplinary in computer science, information science, informatics, statistics, engineering, and other disciplines. It prepares students to pursue a data science related career as a data scientist, data analyst, data architect, etc. or admission to a Ph.D. program.
To earn the Master of Data Science degree, you must successfully earn 30 graduate-level credit hours. The program takes two years to complete. As a Master of Data Science student, you have the option of focusing on one of the following four distinct tracks: (1) Applied Data Science; (2) Big Data Systems; (3) Computational and Analytical; and (4) Managerial Data Science.
Data Science is in the STEM field (science, technology, engineering, or mathematics). Since the Data Science program is interdisciplinary and an applied program, international students are eligible for a STEM OPT Extension. For more information about the STEM OPT Extension and a list of qualifying STEM majors, go to https://ois.iu.edu/living-working/employment/f1/optional/stem-opt.html.
The Data Science program gives our students a deep set of core competencies in multiple areas—including programming, statistics, data analytics, machine learning, data wrangling, data visualization, communication, business foundations, and ethics that increase their marketability in the industry. The learning outcomes of the MS Data Science Residential degree are the knowledge and skills acquired in the program that are transferable to successfully use data to solve problems, which include:
Data preparation and presentation
Exploratory data analytics & visualization
Model fitting and inference
Efficient and scalable data processing
The Master of Data Science degree requires a student to successfully complete 30 credit hours. Master’s students must be enrolled full-time each semester. Typically, it takes students two years to complete the Master of Data Science program.
During the first three semesters, students take nine (9) credit hours per semester and three (3) to nine (9) credit hours during the fourth semester. The student’s advisor, program director, and the Director of Graduate Studies must approve exceptions. During the summer between Year I and Year II of their studies, students often take an internship.
We expect students to develop as a scholar, an instructor-mentor, and a professional. As a master’s student and in your career, it is expected that students maintain professionalism and high standards in your interactions with faculty, staff, colleagues, and students as well as in your role as a researcher or associate instructor.
All students must (1) maintain cumulative and semester GPAs of 3.0 or above; (2) complete coursework in a timely manner; (3) maintain academic integrity; (4) maintain a good academic standing; and (5) conduct themselves in accordance with the Indiana University’s Code of Student Rights, Responsibilities, & Conduct. Failure to maintain any of the above requirements will result in the student being placed on academic probation or dismissal from the program. Funding may be in jeopardy as well.
Data Science is shaping the future. According to the U.S. Bureau of Labor Statistics Report, by 2031, the employment rate for data scientists will grow by 36% from 2021 to 2031. According to Dr. Martin Schedlbauer, a Data Science Professor at Northeastern University, “data science careers are in high demand and this trend will not be slowing down any time soon, if ever.”
The demand for data scientists is high. With a Master of Data Science degree from Indiana University’s Luddy School of Informatics, Computing, and Engineering, you could pursue a career as a:
Business Intelligence Developer
Data Architect
Data Scientist
Data Analyst
Data Engineer
Decision Scientists
Enterprise Architect
Software Developers
Statistician
The Luddy School of Informatics, Computing, and Engineering’s Office of Career Services offers a variety of programs and services to help students find and succeed in internships and full-time jobs. The Office of Career Services will review student’s resumes and cover letters, will hold mock interviews, will assist in negotiating a hiring package, etc.
The Indiana University’s Career Development Center is also available to Luddy graduate students.
In the fall and spring, the Luddy Office of Career Services hosts two large career fairs. Many of the employers who attend these career fairs are looking to hire students for full-time employment or internships. For Luddy Career Outcomes, go to our Career Services Website.
All students must abide by the Indiana University Code of Student Rights, Responsibilities, & Conduct. This applies to scholarship, any role the student may have as an Associate Instructor (AI), relations with colleagues, relations with students, and compliance with academic standards with respect to academic ethics.
If students are not familiar with the concept and best practices of avoiding any hint of plagiarism in American universities, they should become familiar with these standards. The Code provides a series of documents describing the behaviors, ideals, and goals for Indiana University.
Our commitment to diversity, equity, and inclusion is grounded in our aspiration to cultivate intellectual rigor and curiosity among our students and to prepare them to thrive in and contribute to a globally diverse, complex, and interconnected world. This includes creating an inclusive and multicultural educational landscape through the retention and recruitment of diverse students in terms of their backgrounds, identities and experiences, who have been traditionally underrepresented in graduate education. The program promotes a climate of diversity, inclusion, engagement, and achievement, which are integral components of graduate education and beyond.
The Data Science Club at Indiana University (DSC@IU) is a student-run organization affiliated with Luddy. All MS Data Science Residential students are encouraged to actively participate in the club. DSC@IU helps students acquire vital skills that will kick-start their journey into the Data Science world, through various means like mentorships, tutorials, seminars and study groups. The Club organizes networking meetups for students to connect with Alumni, Professionals, and Employers for career guidance.
Moreover, it conducts Hackathons and Datathons to get hands-on experience with real-world problems and brings great opportunities to socialize through fun events. For information about the Data Science Club, email dsclub@indiana.edu.
Students who are admitted to the Master of Data Science degree are thought to be ready to start the program with the essential knowledge to be successful in the program. They are not required to take remedial coursework.
However, if a student feels they need remedial work in math and/or programming, they may want to consider enrolling in the Data Science Essentials remedial self-paced package of online coursework that can help you prepare to be successful in the program. The remedial courses available in the Data Science Essentials are: Basic Linear Algebra & Calculus; Basics of Java; Basics of Python Programming; Introduction to C++, Introduction to R Programming; Introduction to SQL; and Introduction to MongoDB. No certificates or badges of understanding will be awarded as these are self-paced modules. This course is offered through the Luddy Office of Online Education (luddyonl@indiana.edu). The cost of this course is $150.
Curriculum
The Master of Data Science degree is a 30-credit degree program offered by the Luddy School of Informatics, Computing, and Engineering. The curriculum consists of 15 credits of core requirements and the remaining 15 credits are satisfied by fulfilling the Master of Data Science track-specific requirements and electives.
Internship Course Credits
Curricular Practical Training (CPT) and DSCI-D591 Graduate Internship Course (0-3 credits)
The CPT opportunity enables students to work for a company or organizations in the U.S. as an integral part of the established Data Science education program. Students apply their academic knowledge of the in-demand technical skills, working well in a group, interpreting data findings effectively to an audience in various formats, as well as the soft skills employers seek, prepare our graduates to use their expertise in the industry. Each internship will vary according to the context, industry, responsibilities, and personal experiences of the student.
No, CPT is highly recommended but not required for the Data Science degree.
A full-time F-1 student in the Data Science Graduate program with permission from the department.
Here is the information page from OIS at Indiana University and SEVIS information from the US Department of Homeland Security. Students can fill out the CPT form and work with the Luddy Graduate Studies offices and OIS to get an approval.
All internships provide opportunities to build skills in communication, time management and team dynamics. The student can reinforce their academic learnings with the understanding of a real-world corporate setting, which will help the student to make important decisions about a future career in Data Science.
Yes, CPT is an experience to be integral to the student’s academic curriculum. The DSCI-D 591 course is created to allow students the opportunity to gain professional work experience in industry and to utilize skills taught in the classroom. The student will enroll in 1 credit for part-time (1-20 hours per week) and 2 credits for full-time (more than 20 hours per week).
At the completion of the internship, MS DS Students are required to submit an exit letter from the employer stating you successfully completed the internship. We do not have a specific template for the exit letter but we require the following information:
Letter is Prepared on Company Letter Head
Student Name
Dates of Employment
Internship Completion Date
Employer Statement Indicating the Internship was Successfully Completed
MS DS students will also need to submit a summary report (1-2 pages) of what you learned for each internship experience. Your summary needs to document what you learned with a strong emphasis on the educational benefits of your internship. The summary report will not need to include any details that breach your employer's confidentiality. You will need to submit the exit letter and summary report to gradvise@indiana.edu for review. A grade will be awarded to the course when you have successfully completed the internship requirements.
If DSCI-D 591 is taken to fulfill the capstone requirement, then the student may enroll in any 1 or 2 credits Luddy course to fulfill the remaining credits.
Applied Data Science Track Overview and Track Requirements
The Applied Data Science track offers the training in both the data science methods and their application in different domains. This track is suitable for students with an interdisciplinary background who want to specialize in application areas of data science.
Students following the Applied Data Science track are required to complete 12 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 3 credit hours of Data Mining and Search, 3 credit hours of Data Management and Engineering, and 3 credit hours of Data Visualization and Storytelling. Students will specialize in 6 credit hours of a Data Science Domain. The remaining 12 credit hours are 3 credit hours of capstone project and 9 credit hours of electives, selected to best suit individual interests, needs, and overall career goals.
Applied Data Science Core Requirements (12 credit hours)
STAT-S 520 Introduction to Statistics (3 cr.) P: MATH M212, M301, M303, or the equivalent. Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares.
Higher level statistics course may be taken with departmental approval
CSCI-B 551 Elements of Artificial Intelligence (3 cr.) CSCI-C 343 recommended. 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.
CSCI-B 555 Machine Learning (3 cr.) Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.
CSCI-B 565 Data Mining (3 cr.) Algorithmic and practical aspects of discovering patterns and relationships in large databases. The course also provides hands-on experience in data analysis, clustering and prediction. Topics include data preprocessing and exploration, data warehousing, association rule mining, classification and regression, clustering, anomaly detection, human factors and social issues in data mining.
CSCI-P 556 Applied Machine Learning (3 cr.) The main aim of the course is to provide skills to apply machine learning algorithms on real applications. We will consider fewer learning algorithms and less time on math and theory and instead spend more time on hands-on skills required for algorithms to work on a variety of data sets.
ENGR-E 511 Machine Learning for Signal Processing (3 cr.) The course discusses advanced signal processing topics as an application of machine learning. Hands-on signal processing tasks are introduced and tackled using a problem-solving manner, so students can grasp important machine learning concepts. The course can help students learn to build an intelligent signal processing system in a systematic way.
ILS-Z 534 Search (3 cr.) The success of commercial search engines shows that Information Retrieval is a key in helping users find the information they seek. This course provides an introduction to information retrieval theories and concepts underlying all search applications. We investigate techniques used in modern search engines and demonstrate their significance by experiment.
INFO-I 606 Network Science (3 cr.) (may be counted only once) Requires strong working knowledge of mathematics and programming, specifically, proficiency in the topics such as probability, statistics, linear algebra, data structures, and algorithms. Python is the main programming language. This course teaches the fundamental theories, algorithms, and key applications of network science across social and biological systems.
Select one course from the following:
CSCI-B 561 Advanced Database Concepts (3 cr.) CSCI-C 241, C 335 and C 343 recommended. Database models and systems: especially relational and object-oriented; relational database design theory; structures for efficient data access; query languages and processing; database applications development; views. Transaction management: concurrency and recovery. Credit not given for both CSCI-B 561 and CSCI-B 461.
ENGR-E 516 Engineering Cloud Computing (3 cr.) Experience with Windows or Linux using Java and scripts. This course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage, and high-level language for proficiency with a complex ecosystem of tools that span many disciplines.
INFO-I 535 Management, Access, and Use of Big and Complex Data (3 cr.) Innovation today is emerging from a preponderance of data from sensors, social media, and the Internet. This course covers knowledge representation, data process, and data management for big and complex data. Specific topics include data integration, semantics, and provenance; workflows and pipelines; and distributed noSQL stores. Credit not given for both INFO-I 535 and I 435.
DSCI-D 532 Applied Database Technologies (3 cr.) 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, MongoDB, Neo4j, Cassandra, and HBase.
Select one course from the following:
ENGR-E 583 Information Visualization (3 cr.) (may be counted only once) This course provides students with a working knowledge on how to visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users. Credit not given for both ENGR-E 583 and E 483.
ENGR-E 584 Scientific Visualization (3 cr.) Teaches basic principles of human cognition and perception; techniques and algorithms for designing and critiquing scientific visualizations in different domains (neuro, nano, bio-medicine, IoT, smart cities); hands-on experience using modern tools for designing scientific visualizations that provide novel and/or actionable insights; 3D printing and augmented reality deployment; teamwork/project management expertise.
INFO-I 590 Topics in Informatics
Topic: Data Visualization (3 cr.) (may be counted only once) 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.
STAT-S 670 Exploratory Data Analysis (3 cr.) P: Two statistics courses at the graduate level, or consent of instructor. Numerical and graphical techniques for summarizing and displaying data. Exploration versus confirmation. Connections with conventional statistical analysis and data mining. Applications to large data sets.
Applied Data Science Domain (6 credit hours)
Select one of the following domains and complete two courses within that specific domain:
INFO-I 590 Topics in Informatics
Topic: Artificial Life in Virtual Reality (3 cr.) P: INFO-I 304. This course will explore one powerful application of virtual reality: the study of life, evolution, and artificial intelligence. Students will learn the basic building blocks of biological intelligence, how to build virtual worlds for assessing artificial intelligence, and how to populate virtual worlds with intelligent and autonomous artificial agents.
Topic: Building Virtual Worlds (3 cr.) P: INFO-I 304. This course will explore advanced techniques for designing and building virtual reality worlds. Topics include rigged animation, spatial sound, keyframe and procedural animation, interactivity, intelligent cameras, advanced shaders, and particle systems. Students will develop proficiency with a variety of software tools, development methods, and creation techniques.
Topic: Creating Virtual Assets (3 cr.) P: INFO-I 304. This course will explore advanced techniques for creating virtual assets for virtual reality applications. Topics include 3D modeling, animation, motion capture, sound capture and editing, materials, textures, shaders, and scripting. Students will learn how to export assets to virtual reality, augmented reality, video, still images, and 3D printed objects.
Topic: Introduction to Virtual Reality (3 cr.) Virtual Reality has applications in fields as diverse as medicine, education, military training, trauma recovery, and artificial intelligence. In this course, students will learn the foundational skills needed to build virtual reality applications. We will focus on software programs for building virtual assets and realistic virtual environments.
INFO-I 520 Security for Networked Systems (3 cr.) 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. The course provides the foundation for more advanced security courses and hands-on experiences through course projects. Credit not given for both INFO-I 520 and I 430. INFO-I 525 Organizational Informatics and Economics of Security
INFO-I 525 Organizational Informatics and Economics of Security (3 cr.) 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.
INFO-I 533 Systems and Protocol Security and Information Assurance (3 cr.) This course looks at systems and protocols, how to design threat models for them and how to use a large number of current security technologies and concepts to block specific vulnerabilities. Students will use a large number of systems and programming security tools in the laboratories. Credit not given for both INFO-I 533 and I 433.
INFO-I 538 Introduction to Cryptography (3 cr.) This class considers issues of network security, treating in depth the topics covered in INFO-I 536. In particular, the class involves adversarial modeling, a detailed treatment of security primitives, and methods for analysis of security. It spans the ethics and technology of security, with examples drawn both from deployed and proposed protocols. Topics to be covered include studies of rational and malicious cheating, symmetric and asymmetric cryptography, security reductions and heuristics.
ECON-M 501 Microeconomic Theory I (3 cr.) The course develops the methodology and language of price theory. Partial equilibrium analysis of consumer theory, producer theory, and economics of uncertainty. Emphasis on comparative statistics and the duality theory. Topics include welfare analysis, the theory of price indices, quality of goods, revealed preferences, the theory of derived demand, expected utility theory, attitudes toward risk, and various measures of riskiness.
ECON-M 504: Econometrics I (3 cr.) Emphasis is on the probability and statistical theory underpinning the classical linear regression model used in economic applications. Special topics include finite and asymptotic properties of point and interval estimation, hypothesis testing and model building. Several software packages such as Stata or R are used in computer lab applications.
ECON-M 511: Microeconomic Theory II (3 cr.) General equilibrium theory; welfare economics; microeconomics of capital theory; monopoly, oligopoly and game theory, product differentiation, monopolistic competition. Price discrimination. Economics of Information including adverse selection, moral hazard and principal agent models.
ECON-M 514: Econometrics II (3 cr.) Emphasis is on the matrix formulation and computer estimation methods for single and multiple equation models using economic and business data. Attention is given to the assumptions required for testing sets of coefficients and model structures. Special topics include heteroscedasticity, multicollinearity, errors in variables, simultaneity, time-series analysis, limited dependent variables, sample selection, and alternatives to least squares estimation.
ECON-M 518: Econometrics: Big Data (3 cr.) The course consists of discussion of how to import, clean and visualize data on the computer, an introduction to popular tools from machine learning and an overview on recent advances on combining machine learning methods with economic models to conduct causal inference. Use of software package R to analyze large models and large economic data sets.
ECON-M 524: Financial Econometrics (3 cr.) The course covers the econometrics toolboxes that are useful to analyze financial market data, in particular, time series data. The goal is to understand and implement state-of-the-art econometric methods with the data at hand, providing answers to empirical questions. While the course intends to put more emphasis on implementation, and less on rigorous theory, learning some heuristics behind the theory is important part of the course. Topics include stationary time series analysis, persistency, predictive regression, model selection, factor models, and advanced topics.
INFO-I 507 Introduction to Health Informatics (3 cr.) This is a combined advanced undergraduate and graduate course that provides an introduction to health informatics. By the end of the course, students will be able to describe and apply informatics methods that improve health and well being.
INFO-I 519 Introduction to Bioinformatics (3 cr.) One semester programming course or equivalent recommended. Sequence alignment and assembly; RNA structure, protein and molecular modeling; genomics and proteomics; gene prediction; phylogenetic analysis; information and machine learning; visual and graphical analysis bioinformatics; worldwide biological databases; experimental design and data collection techniques; scientific and statistical data analysis; database and data mining methods; and network and Internet methods.
INFO-I 529 Machine Learning in Bioinformatics (3 cr.) INFO-I 519 or equivalent knowledge recommended. The course covers advanced topics in Bioinformatics with a focus on machine learning. The course will review existing techniques such as hidden Markov models, artificial neural networks, decision trees, stochastic grammars, and kernel methods. Examine application of these techniques to current bioinformatics problems including: genome annotation and comparison, gene finding, RNA secondary structure prediction, protein structure prediction, gene expression analysis, proteomics, and integrative functional genomics.
MGEN-Q 581 Introduction to Quantitative Biomedical Sciences (IU Indianapolis) (3 cr.) (may be counted only once) This course is designed to introduce students to diverse topics in the quantitative biomedical sciences connecting basic biology concepts with advanced quantitative methodologies. It will serve as an introductory course for students with either basic biology or computational backgrounds. The topics cover bioinformatics, medical informatics, imaging informatics, and data sciences.
CSCI-B 657 Computer Vision (3 cr.) P: CSCI-B 551. Concepts and methods of machine vision as a branch of artificial intelligence. Basics of digital image processing. Local and global tools for deriving information from image data. Model-based object recognition and scene understanding.
ENGR-E 599 Topics in Intelligent Systems Engineering
Topic: Autonomous Robotics (3 cr.)
INFO-I 513 Usable Artificial Intelligence (3 cr.) Building foundational skills in machine learning, natural language processing, and artificial intelligence for data collection, data analysis, data visualization, and decision-making.
INFO-I 527 Mobile and Pervasive Design (3 cr.) The aim of this course is to provide students with the ability to design and implement novel interactions with mobile and pervasive technologies. We will discuss interaction paradigms and explore different technologies. Students will design, build, implement and refine mobile and pervasive computing applications for their domain of interest.
INFO-I 540 Human Robot Interaction (3 cr.) This course surveys the field of human-robot interaction (HRI), which involves understanding how people perceive and respond to robots and creating robots that interact naturally with people. We will discuss the design, evaluation and societal significance of interactive robots from a human-centered perspective through readings, discussion and developing HRI prototypes. Credit given for only one of INFO-I 540, I 440 or H 440.
INFO-I 542 Foundations of HCI (3 cr.) "Foundations of HCI" offers a survey overview of the field of Human-Computer Interaction Design. It introduces the main themes of HCI set generally in a historical context. Themes include interaction design, cognitive modeling, distributed cognition, computer-supported cooperative work, data visualization, ubiquitous computing, affective computing, and domestic computing, among others.
ENGR-E 583 Information Visualization (3 cr.) (may be counted only once) This course provides students with a working knowledge on how to visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users. Credit not given for both ENGR-E 583 and E 483.
ILS-Z 604 Topics in Library and Information Science
Topic: Music Data Mining (3 cr.) This course gives an introductory overview of the field of music data mining, which is the process of extracting invaluable information from digital audio signals and various music-related text data, such as lyrics, music reviews, and social tags. Lab tasks and the final project will provide hands-on experiences in applying machine learning and deep learning techniques to those various kinds of music data. Topics covered in this course include audio and textual data analysis, classification, clustering, and recommendation.
ILS-Z 639 Social Media Mining (3 cr.) Basic Unix skills recommended. This course provides a graduate-level introduction to social media mining and methods. The course provides hands-on experience mining social data for social meaning extraction (focus 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.
INFO-I 513 Usable Artificial Intelligence (3 cr.) Building foundational skills in machine learning, natural language processing, and artificial intelligence for data collection, data analysis, data visualization, and decision-making.
INFO-I 590 Topics in Informatics
Topic: Data Visualization (3 cr.) (may be counted only once) 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.
INFO-I 606 Network Science (3 cr.) (may be counted only once) Requires strong working knowledge of mathematics and programming, specifically, proficiency in the topics such as probability, statistics, linear algebra, data structures, and algorithms. Python is the main programming language. This course teaches the fundamental theories, algorithms, and key applications of network science across social and biological systems.
Applied Data Science Capstone Project (3 credit hours)
Students will be required to work on a project that applies the knowledge and skills learned to solve real-world problems for a company, organization, or individual. This may be fulfilled through a capstone course, an internship, or an independent study project. The aim of this requirement is to demonstrate students' capabilities to prospective employers and inspire innovation.
DSCI-D 590 Topics in Data Science
Topic: Faculty Assistance in Data Science (1-3 cr.) The Faculty Assistance in Data Science program provides faculty with access to expertise and assistance in advanced data analytics, visualization, and development for the purpose of catalyzing their research. This program pairs faculty from across the campus in any discipline with graduate students pursuing a M.S. in Data Science through the Luddy School of Informatics, Computing, and Engineering for summer research projects.
DSCI-D 591 Graduate Internship (0-3 cr.) Department Approval. Students gain professional work experience in an industry or research organization setting using skills and knowledge acquired in Data Science coursework. A written report will be required upon completion of the experience. May be repeated for a maximum of 6 credit hours.
DSCI-D 592 Data Science in Practice (3 cr.) Students gain critical, practical skills in applying data science to real world problems. Students will work in teams of 3-5 to tackle a real-world problem defined by a project sponsor. Project sponsors can be academics or industry practitioners. Students work with the project sponsor to understand the problem domain, identify where their data science skills can be applied, and to design, implement and test a solution.
DSCI-D 699 Graduate Independent Study in Data Science (1-6 cr.) Must be a student in the Data Science graduate program. Independent Study under the direction of a faculty member, culminating in a written report and/or database development and/or documented laboratory experience. May be repeated 2 times for a maximum of 9 credit hours.
INFO-I 590 Topics in Informatics
Topic: Luddy Artificial Intelligence Development and Experience Laboratory (1-3 cr.) This is an on-campus research practicum for Luddy graduate students that allows participants to develop new AI applications or apply existing AI technology to new problems. As part of the LAIDEL practicum, students will have a chance to apply their existing and learn new skills while working on a class project with external organizations and industry partners. The external partners provide a real-world problem specification, which is then assigned to a team of students, along with a mentor from the external partner’s organization. The student teams will explore the problem, create and execute a proposed solution, and upon completion will receive academic credit for their experiences. Throughout the course, students will get an opportunity to network with external partners and learn more about their organization and potential careers within it.
ILS-Z 690 Capstone in Information Architecture (3 cr.) The capstone course integrates within a single project the theoretical and practical components of the Information Architecture Certificate program. Working with one of the program co-directors, who serves as the student's project advisor, the student will determine both the scope and extent of the project. The student will publicly present and defend the capstone project upon completion.
MGEN-Q 581 Introduction to Quantitative Biomedical Sciences (IU Indianapolis) (3 cr.) (may be counted only once) This course is designed to introduce students to diverse topics in the quantitative biomedical sciences connecting basic biology concepts with advanced quantitative methodologies. It will serve as an introductory course for students with either basic biology or computational backgrounds. The topics cover bioinformatics, medical informatics, imaging informatics, and data sciences.
Applied Data Science Electives (9 credit hours)
The remaining 9 credit hours are selected from unselected courses above or additional data science-related course offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn credit for courses taken to fulfill core, domain, or capstone requirements.
No more than three (3) credit hours of DSCI-D 591 may be earned
No more than three (3) credit hours total may be earned in DSCI-D 590 Basic Data Science On-Ramp and DSCI-D 590 Advanced Data Science On-Ramp
Applied Data Science Track Sample Schedule of Courses
The following is a sample schedule of courses for the Applied Data Science Track. Students should consult with their advisor and the Director of Graduate Studies in order to select courses that will best support their plans and career goals.
Fall Year 1 (9 cr.)
Spring Year 1 (9 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Data Science Domain Course (3 cr.)
Data Science Domain Course (3 cr.)
Fall Year 2 (9 cr.)
Spring Year 2 (2 cr.)
Elective (3 cr.)
Capstone Project (3 cr.)
Elective (3 cr.)
Elective (3 cr.)
Elective (3 cr.)
Elective (3 cr.)
Big Data Systems Track Overview and Requirements
The Big Data Systems track focuses on the development and engineering of software systems for collecting, managing, and mining massive data. This is most suitable for students with a background in computer science or engineering who prefer hands-on and project-based learning.
Students following the Big Data Systems track are required to complete 21 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 6 credit hours of AI and Machine Learning, 9 credit hours of Big Data, Cloud Computing, and Visualization, and 3 credit hours of Core Engineering. The remaining 9 credit hours are electives, selected to best suit individual interests, needs, and overall career goals.
Pre-requisites: Students in this program need to have a solid foundation in STEM course work, specifically the following:
Proficient level of programming experience in C, Java or Python
Familiarity with R and MATLAB is useful
Calculus I and II and basic understanding of probability and elements of discrete math
Big Data Systems Track Core Requirements (21 credit hours)
Select one course from the following:
SPEA-V 506: Statistical Analysis for Effective Decision-Making (3 cr.) Non-calculus survey of concepts in probability, estimation, and hypothesis testing. Applications of contingency table analysis; analysis of variance, regression, and other statistical techniques. Computer processing of data emphasized.
STAT-S 520: Introduction to Statistics (3 cr.) Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares
Students who have completed equivalent prior coursework in statistics can opt to take an additional elective in lieu of one of the Statistical Methods courses.
Select two courses from the following:
CSCI-B 555 Machine Learning (3 cr.) Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.
CSCI-B 565 Data Mining (3 cr.) Algorithmic and practical aspects of discovering patterns and relationships in large databases. The course also provides hands-on experience in data analysis, clustering and prediction. Topics include data preprocessing and exploration, data warehousing, association rule mining, classification and regression, clustering, anomaly detection, human factors and social issues in data mining.
CSCI-P 556 Applied Machine Learning (3 cr.) The main aim of the course is to provide skills to apply machine learning algorithms on real applications. We will consider fewer learning algorithms and less time on math and theory and instead spend more time on hands-on skills required for algorithms to work on a variety of data sets.
ENGR-E 511 Machine Learning for Signal Processing (3 cr.) The course discusses advanced signal processing topics as an application of machine learning. Hands-on signal processing tasks are introduced and tackled using a problem-solving manner, so students can grasp important machine learning concepts. The course can help students learn to build an intelligent signal processing system in a systematic way.
ENGR-E 533 Deep Learning Systems (3 cr.) This course teaches the pipeline for building state-of-the-art deep learning-based intelligent systems. It covers general training mechanisms and acceleration options that use GPU computing libraries and parallelization techniques running on high performance computing systems. The course also aims at deploying the networks to the low-powered hardware systems.
ENGR-E 536 High Performance Graph Analytics (3 cr.) This course covers theoretical and practical concepts in large-scale graph analytics with applications to social networks, computational biology, machine learning and scientific computing. It will demonstrate graph algorithms by analyzing large-scale social and biological networks using high-performance graph analytics frameworks. Design principles for parallel graph algorithms will be discussed.
Select three courses from the following:
CSCI-B 561 Advanced Database Concepts (3 cr.) CSCI-C 241, C 335 and C 343 recommended. Database models and systems: especially relational and object-oriented; relational database design theory; structures for efficient data access; query languages and processing; database applications development; views. Transaction management: concurrency and recovery. Credit not given for both CSCI-B 561 and CSCI-B 461.
ENGR-E 516 Engineering Cloud Computing (3 cr.) Experience with Windows or Linux using Java and scripts. This course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage, and high-level language for proficiency with a complex ecosystem of tools that span many disciplines.
ENGR-E 522 HPC and Cloud Computing for Large Scale Image Applications (3 cr.) Java and Python will be used as programming languages. Understanding of machine learning and/or image processing is helpful. This course describes big data techniques for sensors and remote sensing explaining how one architects analysis systems for sensors and remote imagery. Algorithms, software systems, and storage issues are addressed. The impact of user interfaces is covered. Streaming and batch examples from satellite, internet of things and physics data.
ENGR-E 534 Big Data Applications (3 cr.) This is an overview course of Big Data Applications covering a broad range of problems and solutions. It covers cloud computing technologies and includes a project. Algorithms are introduced and illustrated.
ENGR-E 583 Information Visualization (3 cr.) This course provides students with a working knowledge on how to visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users. Credit not given for both ENGR-E 583 and E 483.
ENGR-E 584 Scientific Visualization (3 cr.) Teaches basic principles of human cognition and perception; techniques and algorithms for designing and critiquing scientific visualizations in different domains (neuro, nano, bio-medicine, IoT, smart cities); hands-on experience using modern tools for designing scientific visualizations that provide novel and/or actionable insights; 3D printing and augmented reality deployment; teamwork/project management expertise.
ENGR-E 616 Advanced Cloud Computing (3 cr.) This course describes Cloud 3.0 in which DevOps, Microservices, and Function as a Service is added to basic cloud computing. The discussion is centered around the Apache Big Data Stack and a major student project aimed at demonstrating integration of cloud capabilities.
ENGR-E 623 Applied Streaming Data Systems Java, C, and Python will be used as programming languages. This course covers the software and algorithm engineering of streaming data systems in the cloud with an emphasis on use in industry and the internet of things.
Select one course from the following:
ENGR-E 503 Introduction to Intelligent Systems (3 cr.) This course covers fundamental principles and five use cases with special attention to challenges and opportunities coming from modern computing infrastructure, the internet of things and artificial intelligence.
ENGR-E 517 High Performance Computing (3 cr.) Students will learn about the development, operation, and application of HPC systems prepared to address future challenges demanding capability and expertise. The course combines critical elements from hardware technology and architecture, system software and tools, and programming models and application algorithms with the cross-cutting theme of performance management and measurement.
ENGR-E 535 Image Processing for Medical Applications (3 cr.) Learn how to build intelligent algorithms and software for medical imaging that can help medical doctors to treat their patients and researchers to understand how the body works. Students will be familiarized with algorithmic techniques such as tracking, denoising, warping, segmentation, model fitting, optimization and interactive visualization of medical datasets.
ENGR-E 551 Simulating Nanoscale Systems (3 cr.) Students will learn how to model and simulate material behavior at the nanoscale. Analysis and control of shape, assembly, and flow behavior in soft nanomaterials will be discussed. Applications to engineering problems at the nanoscale will be emphasized. Optimization methods, nonequilibrium systems, and parallel computing will be covered.
Big Data Systems Electives (9 credit hours)
The remaining 9 credit hours can be selected from unselected courses above or additional data science-related course offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn credit for courses taken to fulfill core requirements.
No more than three (3) credit hours of DSCI-D 591 may be earned
No more than three (3) credit hours total may be earned in DSCI-D 590 Basic Data Science On-Ramp and DSCI-D 590 Advanced Data Science On-Ramp
Big Data Systems Track Sample Schedule of Courses
The following is a sample schedule of courses for the Big Data Systems Track. Students should consult with their advisor and the Director of Graduate Studies in order to select courses that will best support their plans and career goals.
Fall Year 1 (9 cr.)
Spring Year 1 (9 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Elective (3 cr.)
Fall Year 2 (9 cr.)
Fall Year 2 (3 cr.)
Core Course (3 cr.)
Elective (3 cr.)
Core Course (3 cr.)
Elective (3 cr.) (optional)
Elective (3 cr.)
Elective (3 cr.) (optional)
Computational and Analytical Track Overview and Requirements
The Computational and Analytical track focuses on the foundational data science methods. This track is most suitable for students with a background in computer science, statistics, or mathematics who wish to dive deeper into the mechanics of data science methodologies.
Students following the Computational and Analytical track are required to complete 15 credit hours of core coursework that covers 3 credit hours of Data Systems Foundation, 3 credit hours of Algorithmic Foundation, 6 credit hours of Data Analytics Foundation, and 3 credit hours of Big Data Infrastructures. The remaining 15 credit hours are electives, selected to best suit individual interests, needs, and overall career goals.
Computational and Analytical Core Requirements (15 credit hours)
CSCI-B 561 Advanced Database Concepts (3 cr.) CSCI-C 241, C 335 and C 343 recommended. Database models and systems: especially relational and object-oriented; relational database design theory; structures for efficient data access; query languages and processing; database applications development; views. Transaction management: concurrency and recovery. Credit not given for both CSCI-B 561 and B 461.
CSCI-B 505 Applied Algorithms (3 cr.) The course studies the design, implementation, and analysis of algorithms and data structures as applied to real world problems. The topics include divide-and-conquer, optimization, and randomized algorithms applied to problems such as sorting, searching, and graph analysis. The course teaches trees, hash tables, heaps, and graphs.
STAT-S 520 Introduction to Statistics (3 cr.) P: MATH M212, M301, M303, or the equivalent. Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares.
Higher level statistics course may be taken with departmental approval
Select one additional course from the following:
CSCI-B 555 Machine Learning (3 cr.) Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.
CSCI-B 565 Data Mining (3 cr.) Algorithmic and practical aspects of discovering patterns and relationships in large databases. The course also provides hands-on experience in data analysis, clustering and prediction. Topics include data preprocessing and exploration, data warehousing, association rule mining, classification and regression, clustering, anomaly detection, human factors and social issues in data mining.
Select one course from the following:
ENGR-E 516 Engineering Cloud Computing (3 cr.) Experience with Windows or Linux using Java and scripts. This course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage, and high-level language for proficiency with a complex ecosystem of tools that span many disciplines.
INFO-I 535 Management, Access, and Use of Big and Complex Data (3 cr.) Innovation today is emerging from a preponderance of data from sensors, social media, and the Internet. This course covers knowledge representation, data process, and data management for big and complex data. Specific topics include data integration, semantics, and provenance; workflows and pipelines; and distributed noSQL stores. Credit not given for both INFO-I 535 and I 435.
DSCI-D 532 Applied Database Technologies (3 cr.) 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, MongoDB, Neo4j, Cassandra, and HBase.
Computational and Analytical Electives (15 credit hours)
The remaining 15 credit hours can be selected from unselected courses above or additional data science-related course offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn credit for courses taken to fulfill core requirements.
No more than three (3) credit hours of DSCI-D 591 may be earned
No more than three (3) credit hours total may be earned in DSCI-D 590 Basic Data Science On-Ramp and DSCI-D 590 Advanced Data Science On-Ramp
Computational and Analytical Track Sample Schedule of Courses
Fall Year 1 (9 cr.)
Spring Year 1 (9 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Elective (3 cr.)
Elective (3 cr.)
Fall Year 2 (9 cr.)
Spring Year 2 (3 cr.)
Core Course (3 cr.)
Elective (3 cr.)
Elective (3 cr.)
Elective (3 cr.) (optional)
Elective (3 cr.)
Elective (3 cr.) (optional)
Managerial Data Science Track Overview and Requirements
The managerial data science track combines advanced knowledge in database systems and programming languages with strong interpersonal and project management skills. This track is most suitable for students with prior work experience who wish to develop organizational and project management skills.
Students following the Managerial Data Science track are required to complete 21 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 3 credit hours of Machine Learning, Data Mining, and Text Mining, 3 credit hours of Data Visualization and Storytelling, 6 credit hours of Management in Theory, and 6 credit hours of Management in Practice. The remaining 9 credit hours are 3 credit hours of capstone project and 6 credit hours of electives, selected to best suit individual interests, needs, and overall career goals.
Select one course from the following:
SPEA-V 506: Statistical Analysis for Effective Decision-Making (3 cr.) Non-calculus survey of concepts in probability, estimation, and hypothesis testing. Applications of contingency table analysis; analysis of variance, regression, and other statistical techniques. Computer processing of data emphasized.
STAT-S 520: Introduction to Statistics (3 cr.) Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares.
Higher level statistics course may be taken with departmental approval
Select one course from the following:
CSCI-B 505 Applied Algorithms(3 cr.) The course studies the design, implementation, and analysis of algorithms and data structures as applied to real world problems. The topics include divide-and-conquer, optimization, and randomized algorithms applied to problems such as sorting, searching, and graph analysis. The course teaches trees, hash tables, heaps, and graphs.
CSCI-B 551 Elements of Artificial Intelligence (3 cr.) CSCI-C 343 recommended. 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.
CSCI-B 555 Machine Learning (3 cr.) Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.
CSCI-B 561 Advanced Database Concepts (3 cr.) CSCI-C 241, C 335 and C 343 recommended. Database models and systems: especially relational and object-oriented; relational database design theory; structures for efficient data access; query languages and processing; database applications development; views. Transaction management: concurrency and recovery. Credit not given for both CSCI-B 561 and CSCI-B 461.
CSCI-B 565 Data Mining (3 cr.) Algorithmic and practical aspects of discovering patterns and relationships in large databases. The course also provides hands-on experience in data analysis, clustering and prediction. Topics include data preprocessing and exploration, data warehousing, association rule mining, classification and regression, clustering, anomaly detection, human factors and social issues in data mining.
CSCI-B 657 Computer Vision (3 cr.) Concepts and methods of machine vision as a branch of artificial intelligence. Basics of digital image processing. Local and global tools for deriving information from image data. Model-based object recognition and scene understanding.
CSCI-P 556 Applied Machine Learning (3 cr.) The main aim of the course is to provide skills to apply machine learning algorithms on real applications. We will consider fewer learning algorithms and less time on math and theory and instead spend more time on hands-on skills required for algorithms to work on a variety of data sets.
ENGR-E 511 Machine Learning for Signal Processing (3 cr.) The course discusses advanced signal processing topics as an application of machine learning. Hands-on signal processing tasks are introduced and tackled using a problem-solving manner, so students can grasp important machine learning concepts. The course can help students learn to build an intelligent signal processing system in a systematic way.
ILS-Z 534 Search (3 cr.) The success of commercial search engines shows that Information Retrieval is a key in helping users find the information they seek. This course provides an introduction to information retrieval theories and concepts underlying all search applications. We investigate techniques used in modern search engines and demonstrate their significance by experiment.
INFO-I 513 Usable Artificial Intelligence (3 cr.) Building foundational skills in machine learning, natural language processing, and artificial intelligence for data collection, data analysis, data visualization, and decision-making.
INFO-I 606 Network Science (3 cr.) Requires strong working knowledge of mathematics and programming, specifically, proficiency in the topics such as probability, statistics, linear algebra, data structures, and algorithms. Python is the main programming language. This course teaches the fundamental theories, algorithms, and key applications of network science across social and biological systems.
Select one course from the following:
ENGR-E 583 Information Visualization (3 cr.) This course provides students with a working knowledge on how to visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users. Credit not given for both ENGR-E 583 and E 483.
ENGR-E 584 Scientific Visualization (3 cr.) Teaches basic principles of human cognition and perception; techniques and algorithms for designing and critiquing scientific visualizations in different domains (neuro, nano, bio-medicine, IoT, smart cities); hands-on experience using modern tools for designing scientific visualizations that provide novel and/or actionable insights; 3D printing and augmented reality deployment; teamwork/project management expertise.
INFO-I 590 Topics in Informatics
Topic: Data Visualization (3 cr.) 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.
Select two courses from the following:
ILS-Z 513 Organizational Informatics (3 cr.) Introduction to information, technology, and social behavior in the organizational context. Concepts of organization theory and organization behavior, including knowledge and information management, organizational analytics, and organizational intelligence, provide a critical foundation for managing information, people, and information and communication technologies (ICTs) in rapidly changing and dynamic environments.
ILS-Z 645 Social and Organizational Informatics of Big Data (3 cr.) This course surveys organizational, legal, political, and social issues surrounding the creation, dissemination and use of big data from the perspective of social and organizational informatics. It focuses on ways in which the integration of big data is changing structure, culture, and work practices in private and public sector organizations.
ILS-Z 604 Topics in Library and Information Science
Topic: Social and Ethical Impacts of Big Data (3 cr.) This course introduces students to new social and ethical challenges arising from the use of data in a broad sense, and the technical and societal approaches to address such challenges. More specifically, this course provides a survey of the social, political, legal, and organizational issues that surround the creation, dissemination, and use of big data from the perspective of social informatics.
Select two courses from the following:
ILS-Z 512: Information Systems Design (3 cr.) Students identify, design, and implement a significant information design project, such as the redesign of a complex Website for a local business, library, or nonprofit. Principles and practices of project management are discussed in the context of team-based website redesign.
ILS-Z 556: Systems Analysis & Design (3 cr.) This course introduces the basic concepts underlying systems analysis and design, focusing on contextual inquiry/design and data modeling, as well as the application of those analysis techniques in the analysis and design of organizational information systems.
ILS-Z 586 Digital Curation (3 cr.) Preserving and providing long-term access to digital materials over time is a Grand Challenge. They require constant and ongoing maintenance. This course provides an overview of research, policy and current practices in curating and preserving digital data, gives students practical experience, working with digital materials, and creating digital curation plans.
Managerial Data Science Capstone Project (3 credit hours)
Students will be required to work on a project that applies the knowledge and skills learned to solve real-world problems for a company, organization, or individual. This may be fulfilled through a capstone course, an internship, or an independent study project. The aim of this requirement is to demonstrate students' capabilities to prospective employers and inspire innovation.
DSCI-D 590 Topics in Data Science
Topic: Faculty Assistance in Data Science (1-3 cr.) The Faculty Assistance in Data Science program provides faculty with access to expertise and assistance in advanced data analytics, visualization, and development for the purpose of catalyzing their research. This program pairs faculty from across the campus in any discipline with graduate students pursuing a M.S. in Data Science through the Luddy School of Informatics, Computing, and Engineering for summer research projects.
DSCI-D 591 Graduate Internship (0-3 cr.) Department Approval. Students gain professional work experience in an industry or research organization setting using skills and knowledge acquired in Data Science coursework. A written report will be required upon completion of the experience. May be repeated for a maximum of 6 credit hours.
DSCI-D 592 Data Science in Practice (3 cr.) Students gain critical, practical skills in applying data science to real world problems. Students will work in teams of 3-5 to tackle a real-world problem defined by a project sponsor. Project sponsors can be academics or industry practitioners. Students work with the project sponsor to understand the problem domain, identify where their data science skills can be applied, and to design, implement and test a solution.
DSCI-D 699 Graduate Independent Study in Data Science (1-6 cr.) Must be a student in the Data Science graduate program. Independent Study under the direction of a faculty member, culminating in a written report and/or database development and/or documented laboratory experience. May be repeated 2 times for a maximum of 9 credit hours.
INFO-I 590 Topics in Informatics
Topic: Luddy Artificial Intelligence Development and Experience Laboratory (1-3 cr.) This is an on-campus research practicum for Luddy graduate students that allows participants to develop new AI applications or apply existing AI technology to new problems. As part of the LAIDEL practicum, students will have a chance to apply their existing and learn new skills while working on a class project with external organizations and industry partners. The external partners provide a real-world problem specification, which is then assigned to a team of students, along with a mentor from the external partner’s organization. The student teams will explore the problem, create and execute a proposed solution, and upon completion will receive academic credit for their experiences. Throughout the course, students will get an opportunity to network with external partners and learn more about their organization and potential careers within it.
ILS-Z 690 Capstone in Information Architecture (3 cr.) The capstone course integrates within a single project the theoretical and practical components of the Information Architecture Certificate program. Working with one of the program co-directors, who serves as the student's project advisor, the student will determine both the scope and extent of the project. The student will publicly present and defend the capstone project upon completion.
MGEN-Q 581 Introduction to Quantitative Biomedical Sciences (IU Indianapolis) (3 cr.) This course is designed to introduce students to diverse topics in the quantitative biomedical sciences connecting basic biology concepts with advanced quantitative methodologies. It will serve as an introductory course for students with either basic biology or computational backgrounds. The topics cover bioinformatics, medical informatics, imaging informatics, and data sciences.
Managerial Data Science Track Sample Schedule of Courses
The following is a sample schedule of courses for the Managerial Data Science Track. Students should consult with their advisor and the Director of Graduate Studies in order to select courses that will best support their plans and career goals.
Fall Year 1 (9 cr.)
Spring Year 1 (9 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Core Course (3 cr.)
Elective (3 cr.)
Fall Year 2 (9 cr.)
Spring 2 (3 cr.)
Course Course (3 cr.)
Capstone Project (3 cr.)
Core Course (3 cr.)
Elective (3 cr.) (optional)
Elective (3 cr.)
Elective (3 cr.) (optional)
Capstone
Please see information below for more detailed information of some of the capstone options. If 1 or 2 variable capstone credits are taken to fulfill the capstone requirement, then the student may enroll in any 1 or 2 credits Luddy course to fulfill the remaining credits.
Independent study classes and all research classes taken prior to entering candidacy require that the student and the instructor define the study, including the deliverables. Students need to identify a faculty advisor who is willing to supervise the study, and complete the Independent Study Request Form.
The Luddy Graduate Studies Office will process the form and notify students by email of any issues or that students may proceed with registration for the term. Students then register for courses via one.iu.edu.
Any course substitution or exception must be approved in advance. Email the Luddy Graduate Studies Office for additional information (gradvise@indiana.edu).
Any course substitution or exception must be approved in advance. Email the Luddy Graduate Studies Office for additional information (gradvise@indiana.edu).
A minimum of a B (3.0) average in graduate work is required for continuance in graduate study. Courses completed with grades below C (2.0) are not counted toward degree requirements, but such grades will be counted in calculating a student's grade point average. Note that no work may be transferred from another institution unless the grade is B (3.0) or higher.
A student whose semester GPA falls below a grade of B (3.0) will be put on probation. The student must raise their semester and cumulative grade point average to a B (3.0) or higher by the end of the following semester. Failure to do so in two consecutive semesters (excluding summer) may result in academic dismissal from the program.
A student will be placed on academic probation if the student’s cumulative or semester GPA falls below a 3.0 and/or if a student fails to make satisfactory progress in the program. To return to satisfactory progress status, students must bring their cumulative and semester grade point averages to 3.0 or higher by the end of the next semester. Failure to do so may result in academic dismissal from the program.
To help with the registration process, students are given a Course Planning Checklist. Students meet with their advisor prior to registering to plan courses for the upcoming semester. The student may email gradvise@indiana.edu to schedule an appointment.
Some courses require course permission from the instructor and/or the department prior to enrollment. This information is found in the Schedule of Classes which is located at http://registrar.indiana.edu/calendars/schedule-of-classes.shtml. If the course is listed as requiring permission from the instructor or the department, students must contact via email the instructor and/or the department listed for the course to obtain permission. The email reply must be forwarded to gradvise@indiana.edu.
Additional steps on how to register are available through the UITS Knowledge Base.
Any deviations from a student’s approved Course Registration Form requires that the student request approval from the advisor or from the program director if the course to be dropped/added is a program core course or otherwise required. Approval should then be conveyed in writing (email or signed document) to the Luddy Graduate Studies Office, gradvise@indiana.edu.
Fees/Refund. Starting two business days after the student’s initial registration, a system access fee will be charged every calendar day the student makes one or more successful adjustments to their schedule. A late schedule change fee will also be assessed for each course dropped after the first week of classes. The late schedule change fee also applies to a section change, a change of arranged hours, or an audit change.
Students are responsible for paying all drop and add fees. 100% of tuition is refunded for a course dropped during the first week of classes. After the first week, the amount of tuition refunded (if any) for a dropped course depends on the type of session the course is and when the course is dropped.
If a course is shown as full, the student should add themselves to the waitlist, which serves as a placeholder in the registration line. When students who enrolled in the course drop or when the enrollment cap is expanded, students on the waitlist will be admitted into the course in order. Note: The waitlist runs for the last time on the Thursday of the first week of classes. The Drop if Enroll feature allows a student to enroll in another course while waitlisted for their course of first preference. Students must remember to cancel this feature if they decide to remain in the class of their second choice. The Swap feature allows a student to delay dropping a course until they are safely enrolled in their new class.
During the automatic withdrawal period (see the Registrar’s Official Calendar for exact dates), students who withdraw will be assigned an automatic grade of W. After that period, withdrawals are only possible with approval from the Dean, which is normally given only for urgent reasons such as illness. Instructors may award a grade of F for a student who is failing and withdraws after the automatic withdrawal period.
All graduate students are encouraged to participate in Commencement. Indiana University hosts two university wide commencement events – Winter and Spring. The majority of the students attend the Spring Commencement. Students who finish their degree during the fall can attend the Winter or Spring Commencement. The solemn yet colorful academic pageantry can provide a fitting culmination to a period of intense study and work.
In addition to Indiana University’s Commencement Event, the Luddy School of Informatics, Computing, and Engineering hosts a Celebration Event. Be sure to watch for these emails as many of the deadlines are time sensitive.
Transcripts will ready "Master of Data Science."
Diplomas will read "Master of Science in Data Science."
A student must be enrolled in a minimum of eight (8) credit hours each semester to be considered full-time. Audited courses are not counted in the definition of “full-time study.” It is imperative that international students maintain full-time status to remain in visa compliance. For questions about visa compliance, contact the Office of International Services (ois@iu.edu).
Approval must be given for a student to be enrolled as a part-time student (less than 8 credit hours). Email the Luddy Graduate Studies Office for additional information (gradvise@indiana.edu).
A leave of absence allows Informatics graduate students to deal with unforeseen events that interfere with their academic progress. During a leave, the student is not expected to make progress toward the degree. Although the student may complete coursework from previous terms during a leave, the student may not attend class or use the leave to catch up on current coursework, prepare for exams, work on the capstone, and/or the master’s thesis project.
To be eligible for a leave, the student must be enrolled full time in an Informatics graduate program and have completed at least one semester (a minimum of nine credit hours) in the program. The student must be in good academic standing—if they are on academic probation, they are not eligible for a leave.
Approval must be given for a student to take a leave of absence (less than 8 credit hours). Email the Luddy Graduate Studies Office for additional information (gradvise@indiana.edu).
Throughout this handbook, you will see references to the following important websites, forms, and other resources:
Luddy Graduate Studies Office (GSO) is committed to supporting graduate students from admissions to graduation. Luddy GSO can assist with questions about:
Graduate Admissions
New Student Orientation
Academic Graduate Policies
Program Administrative Forms
Transfer Credit Requests
Grade Changes
Degree Audits
D. interested in getting MS/MA
Under Enrollment Requests
Probation Issues
Registration (late/add/drop/withdraw)
Leave of Absence
Advising Holds
Program Transfer within Luddy
Student Travel
A11 Holds
R10 Immunizations
V03 Academic Hold
Fee Waiver Requests
International Student Services
CPT/OPT e-form requests
Academic Training Requests
Under enrollment permission
I-20 Extensions
Data Science Program resources and social media channels
