Linear Algebra from Elementary to Advanced

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.

This program is a non-degree course in Linear Algebra from Elementary to Advanced, offered by Johns Hopkins University. It's for students who want to learn linear algebra from the basics, with no prior knowledge required. The program lasts several weeks and covers key topics in linear algebra.

The curriculum covers linear equations, matrices, and their properties, such as determinants and eigenvalues. Students will learn about symmetric matrices and quadratic forms, and develop both algebraic and geometric understanding of linear algebra concepts. The program uses examples and pictures to illustrate concepts in low dimensions before moving to higher dimensions.

After completing this program, students can pursue careers in data science, AI, machine learning, finance, mathematics, computer science, or economics. Possible job titles include Data Analyst, Machine Learning Engineer, Financial Analyst, Computer Scientist, or Mathematician. These professionals work in various industries, including technology, finance, and research.