An algorithm is a step-by-step process used to solve a problem or reach a desired goal. It's a simple concept; you use your own algorithms for everyday tasks like deciding whether to drive or take the subway to work, or determining what you need from the grocery store. Software programs are an example of much more powerful algorithms, with computing resources used to execute multiple complex algorithms in parallel to solve much higher-level problems.
As computers become more and more powerful, algorithms are helping them take on a life of their own - literally! Machine learning techniques rely on algorithms that learn and improve over time without need for a programmer's guidance. These techniques can be used to train algorithms for relatively simple tasks like image recognition or the automation and optimization of business workflows. And at their most complex, these algorithms are at the core of building the deep learning and artificial intelligence capabilities that many experts expect will transform our world even more than the advent of the internet!
Learning to understand and apply algorithmic techniques for problem solving is an incredibly important skill for solving complex computing problems, and studying this field requires more specialized prerequisites than some programming-focused computer science courses.
Because algorithms are central to so many types of computer programming work, professionals with skills in this area can end up working in high-paying roles in a wide range of companies. For example, experience with algorithms is important for work as a data scientist, one of the most widely in-demand jobs in tech.
Other algorithm jobs are more specialized. Tech companies working with artificial intelligence or other advanced applications may employ algorithm engineers, machine learning engineers, automation software engineers, and computer vision engineers. There are also highly specialized jobs with companies working with Internet of Things (IoT) applications, such as computer vision engineers, medical device algorithm engineers and self-driving car engineers.
Introductory courses on data structures and algorithms are a good place to start, often after completing prerequisites in discrete math and computer programming fundamentals. Higher-level students may want to continue into more specialized topics like machine learning and reinforcement learning, neural networks and deep learning, and AI engineering.
In addition to courses, Coursera offers short Guided Projects for you to practice and hone your skills.
The skills and experience you might want to already have before starting to learn algorithms may include fundamental knowledge of computers, computer science, and how algorithms work via inputs and outputs. Algorithms, in a sense, are the lifeblood of computer processing. They form a series of instructions that a computer user gives to a computer to transform a set of facts or data into useful information for the computer user. Algorithms are also used in modern streaming recommendations systems. Having this basic understanding of how algorithms work, from sorting data to displaying information on screens, is an essential component of learning this detailed subject.
The kind of people that are best suited for work that involves algorithms are computer science engineers, data scientists, mathematicians, and statisticians who have quantitative problem-solving skills and a solid background and passion in mathematics. These professionals may be graduates with a master’s degree or even a doctoral in computer science. These people best suited for work that involves algorithms might also have a strong background in dynamic programming, data analytics, data structure, and programming languages like Python and Java.
You might know if algorithms are right for you if you are knowledgeable about the basics of computer science and how they pertain to algorithmic processing. These basics would include an understanding of computer architecture, data structures, math, and logic. Insights gleaned in these areas might include arrays, linked lists, binary trees, set theory, and linear equations.
The topics you might want to study that are related to algorithms include logistic regression, neural networks, data mining, automated financial trading, artificial intelligence, and quantum computing. These might be on top of other hefty topics such as deep learning, mathematical equations, and statistics. Knowing these may help you understand how algorithms intersect with computers.