This course was the first in a two-part series covering some of the algorithms underlying bioinformatics. It has now been split into three smaller courses.
This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.
What makes bioinformatics education exciting is that people of a variety of education levels can get started quickly, with just a computer and internet access.
Are you interested in learning how to program (in Python) within a scientific setting?
This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gentler-paced alternative to the first course in our Bioinformatics Specialization (Finding Hidden Messages in DNA).
Learn how to take scattered data and organize it into groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks.
In this course you will learn about the basics of how computation has impacted the entire workflow of photography (i.e., from how images are captured, manipulated and collaborated on, and shared).
The Internet is a computer network that millions of people use every day. Understand the design strategies used to solve computer networking problems while you learn how the Internet works.
For anyone who would like to apply their technical skills to creative work ranging from video games to art installations to interactive music, and also for artists who would like to use programming in their artistic practice.
Learn how to transform information from a format efficient for computation into a format efficient for human perception, cognition, and communication. Explore elements of computer graphics, human-computer interaction, perceptual psychology, and design in addition to data processing and computation.
Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.
This course introduces concepts, algorithms, programming, theory and design of spatial computing technologies such as global positioning systems (GPS), Google Maps, location-based services and geographic information systems. Learn how to collect, analyze, and visualize your own spatial datasets while avoiding common pitfalls and building better location-aware technologies.
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