In recent years, “algorithm” has gone from a niche word relevant mostly to computer scientists to an essential term for understanding how our data-rich world works. And as the use of algorithms becomes pervasive, a firm grasp of how to use them has become a major competitive advantage.
The online Master of Computer and Information Technology (MCIT) degree from the University of Pennsylvania is tailored for non-computer science majors who want to acquire proficiency with algorithms, programming, and other computing skills. Let’s look at three examples of how algorithms covered in the program are used in the real world:
Biomedicine: Analyzing Gene Sequences with BLAST and Clustal
Low-cost DNA sequencing has opened up new possibilities for medicine, offering the potential to diagnose and treat the growing number of diseases with genetic components. However, merely sequencing the DNA isn’t enough to determine the genes that cause some disease. With 3 billion base pairs in the human genome, algorithms are essential for biomedical researchers making sense of today’s vast landscape of genetic data.
Dynamic programming algorithms have been deployed since the early 1970’s for critical tasks such as DNA sequence alignment, protein folding, RNA structure prediction, and protein-DNA binding. Today, biomedical researchers rely on programs like BLAST (Basic Local Alignment Search Tool) and Clustal to compare DNA sequences against relevant databases. Publications involving BLAST and/or Clustal rank among the most-cited scientific papers of all time.
Chemistry: Molecular Synthesis with Chematica
Synthesizing new chemicals is a similarly complicated task. How can one evaluate the trillions of synthesis pathways and millions of existing chemicals to identify those that are likely to be efficient and cost-effective?
The software program Chematica, hailed as “an internet for chemistry” upon its release in 2012, provides a solution using optimization algorithms to predict viable molecular synthesis pathways. Chemists can specify molecules using chemical names, registry numbers, or drawings of the molecule diagrams. Researchers have also demonstrated how to adapt Chematica algorithms to avoid synthesis pathways that have already been patented. This further boosts its usefulness for commercial applications.
Arts & Culture – Making Music Recommendations With Spotify’s Discover Weekly
Algorithms aren’t just for the hard sciences. We’re living in an era of unprecedented access to arts and culture that allows us to dive into archives of books, TV shows, movies, and music from all over the world in seconds. With humanity’s accumulated, cultural output now available at our fingertips, we face a new problem: how do we quickly find what we want to watch, read, or listen to at any given moment?
Providing automated and compelling cultural curation has become a huge business. One of the best examples of a successful use of algorithms in this area is Spotify. In particular, its Discover Weekly feature is popular — it delivers listeners customized playlists of songs they haven’t heard but are likely to enjoy. Discovery Weekly and similar services rely on collaborative filtering algorithms derived from graph theory to map preferences and predict what users enjoy.
Algorithm Fluency for Non-Computer Scientists
These are just three examples of how algorithms are creating new possibilities for a broad range of fields. MCIT Online will help students make high-impact contributions like the ones described, using the expertise they will acquire in dynamic programming, graph theory, optimization algorithms, and many other areas of computer science.