Advanced Linear Models for Data Science 2: Statistical Linear Models

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.

The Advanced Linear Models for Data Science 2 program at Johns Hopkins University is a non-degree course that focuses on statistical linear models. It is an online course taught in English and lasts several weeks. The main advantage of this program is that it provides students with a firm foundation in linear algebraic treatment of regression modeling.

The curriculum covers least squares from a linear algebraic and mathematical perspective. Students learn about linear algebra, multivariate calculus, statistics, and regression models. They also gain experience with the R programming language and proof-based mathematics.

After completing this program, students can take on roles such as Data Scientist, Statistical Analyst, Data Analyst, Quantitative Analyst, or Business Intelligence Developer. They can work in various industries, including finance, healthcare, or technology, and for employers like research institutions or consulting firms.