When organizations are awash with data, they need a lifeline. You can be the one who channels the data and converts it into actionable knowledge that adds value.
Medical statistics, institutional knowledge, consumer buying habits—where there’s data, there’s the potential for knowledge. Data science can optimize the delivery of health care, or improve a company’s marketing strategy.
Learn to manage massive stores of data in the cloud and the data life cycle when you earn our Master of Science degree in Applied Data Science.
The first of its kind in the nation, the IU School of Informatics and Computing at IUPUI is a pioneer in the field of informatics and data science. Key ties to industry, employment, and research distinguish our school and its programs.
Students learn methods of data mining, ways to transform large datasets into usable knowledge, and how to represent information visually. The master’s in Applied Data Science provides students with core competencies in the latest methods of data management, analysis, and infrastructure and high-throughput data storage.
Our curriculum includes instruction in client–server application development, and ethical and the professional management of informatics projects.
Emerging technologies such as GPU-based deep learning and the Internet of Things have fueled the demand for data science skills. Students who earn their master’s degree can find jobs in many sectors, including:
The plan of study is comprised of 30 credit hours. It includes eight required courses on the following topics: informatics, data visualization, relational databases, statistics, web and database development, project management or research design, statistical learning, and cloud computing. In addition, there are six credit hours of approved electives.
Substitute course accommodate special interests and scheduling needs, such as for an online course. A substitute course satisfies the core course requirement.
A student wishing to substitute a course or take an elective course from our Data Science program in Bloomington must apply for graduate non-degree status one month before the start of the semester.
International students on an F-1 visa may only take one online course per semester.
|CSCI 54100 Database Systems (3 cr., prerequisite: CSCI 44300 Database Systems or equivalent)||LIS S511, INFO B512, or INFO B556|
|CSCI 55200 Data Visualization (3 cr.)||INFO H517|
|CSCI 57300 Data Mining (3 cr., prerequisites: CSCI 24000 Computing II and STAT 30100 Elementary Statistical Methods I or STAT 35000 and MATH 35100 Elementary Linear Algebra and MATH 51100 Linear Algebra with Applications)||INFO H515|
|CSCI 59000 Cloud Computing (3 cr.)||INFO H516|
|LIS S517 Web Programming (3 cr.)||NEWM N510|
|ECON E570 Fundamentals of Statistics and Econometrics (3 cr.), HPER T591 Introduction to Statistics in Public Health (3 cr.), PBHL B561 Introduction to Biostatistics I (3 cr.), or STAT 51100 Statistical Methods I (3 cr.)||PSY 60000|
|INFO I575 Informatics Research Design (3 cr.), LIS S506 Introduction to Research (3 cr.), or STAT 514 Design of Experiments (3 cr.)||INFO B505|
The following approved electives are available in the fall and spring semesters.
The Thesis/Project is available to highly motivated students ready to carry out publishable research. Students must prepare a prospectus and gain a commitment from a primary faculty advisor with research interests in data science by the end of the first semester. By the end of the second semester, students must complete a course on research design and methods (e.g., INFO-I 575, LIS-S 506, or STAT 514).
The thesis or project must be completed within two semesters or within a semester and summer. Students register for a total of six credits of Thesis/Project. They are required to prepare and defend a research proposal with a timeline of deliverables in addition to the thesis or project.
Students will demonstrate competency in data analytics.
Students will demonstrate competency in data management, infrastructure, and the data science lifecycle.
Students will demonstrate competency in client–server application development.
Students will demonstrate competency in the management of massive, high-throughput data stores, and cloud computing.
Students will demonstrate competency in data visualization.
Students will demonstrate competency in the ethical and professional management of informatics projects.