Data Science Decisions in Time: Using Causal Information

Johns Hopkins University in Baltimore, founded in 1876, is a leading research university with a global reputation for excellence across medicine, public health, engineering, the arts and sciences. The university enrolls around 32,049 students, including approximately 5,233 international scholars, and offers an extensive portfolio of programs—about 238—across undergraduate, graduate and professional levels.

Johns Hopkins emphasizes research-led education, interdisciplinary collaboration and hands-on learning through labs, clinics and community partnerships. International students can access comprehensive support services for admissions, visas, academic advising and career development, and can participate in research opportunities, internships and global exchange programs that enhance professional prospects and real-world experience.

Baltimore’s urban setting provides access to vibrant cultural life, medical centers and industry partners, making it well suited for students interested in translational research and public service. The university’s strong alumni network, career services and industry links help graduates move into competitive roles worldwide, while campus resources support wellbeing, student activities and a diverse international community.

Data Science Decisions in Time is a non-degree program for those with basic knowledge of statistics, probability, and linear algebra, offered by Johns Hopkins University in English, lasting several weeks. It helps students make informed decisions using causal information.

The curriculum covers using causal information to make decisions, focusing on datasets from business, medical, or technology fields. Students develop skills in data analysis and interpretation, learning to make informed decisions based on data.

Graduates can pursue careers as Data Analysts, Business Intelligence Developers, or Operations Research Analysts, working in industries like healthcare, finance, or technology, for employers such as hospitals, banks, or tech firms.