印第安納大學(xué)與普渡大學(xué)印第安納波里斯聯(lián)合分校

Gain new insights into big data decision-making

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.

A leader in the field of informatics and 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.

Internet of Things and more careers in Data Science

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:

• Data Scientist
• Informatics Scientist
• Informatics project manager
• Data Analyst
• Big Data Consultant
• Business Intelligence Analyst
• Business Technology Analyst
• IT Consultant
• Software Developer
• Database Administrator
• Web Administrator
• Information Architect
• Information Manager

Plan of Study

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.