Computer Science Has Uses For Everybody
By Aarushi Mathur
It was said in the 20th century that everyone should learn a language and mathematics. In the
21st century, computing is the new language to learn.
Coding in the 21st century is as much a soft skill as it is a hard skill. It teaches you how to
approach unique problems and how to think critically about solutions in both general and specific ways. Going out on a limb on that note, one could even say that computing teaches discipline, minimalism, and encourages creativity and empathy at the same time. Trying to solve a problem will often lead to solving a set of problems with common characteristics, generalising cases and putting oneself in the shoes of users with unique situations to be included and covered by your code. All the same, working with limited resources of both the physical and mental kind, encourages some of the most innovative recycling of algorithms you will see, and at the end of the day, there is hardly a sensation that can beat the satisfaction and pride of watching hours of sweat and tears (and careful thought, preferably on paper, the secret weapon to organisation), come alive in the form of a perfectly functioning code.
However, if you are not the sort to be swayed by romantic notions of nurturing your code only to be rewarded with the sight of seeing it work fruitfully, coding has practical benefits to offer too. In fact, unfortunately for you, computing may be inescapable. As the world moves too rapidly integrate itself with the latest technology so as to not be left behind, nearly every area of work is being swiftly transformed and enhanced by automation of some sort. From medicine to music, medieval art and language restoration to the latest trends in media, space exploration to oceanography and even the day-to-day running of businesses, technology is either aiding or leading the way.
But backing up a bit, what even is computer science? What does it consist of? Is coding its
beginning and its end?
At its very heart, computer science is an application of mathematics. Nothing more than
mathematics running on electricity, but never straying too far from its premise. As a philosophy, computer science strives to find better and more efficient ways of performing a task. This could be as perfunctory as building a calculator or atypical as aiding in creating art or consolidating data to help make better decisions in business and medical diagnosis. Computer science relies heavily on logic and linear algebra, while image creation and recognition often require a bit more calculus (think graphs and surfaces).
Data analysis requires creation and interpretation of statistical models. Most large companies opt to outsource the building of these models to secure third-party platforms and provide them with the raw data to analyse. Data visualisation is a big part of computational statistics, as the models need to return data that is easy to read, especially when working with big data (large amounts of data that cannot be processed without special computing technology). These analyses help companies track trends in the market and in their own performance. For example, banks predict what products customers will buy using propensity models and advanced data analytics. Companies like FractalAnalytics use data models to arrive at customers’ risk scores to help institutions determine customers’ spending practices for lending purposes. Companies like Amazon rely on AI-driven data models to build and match customer profiles to recommend items to users
Introductory courses found on learning platforms like Coursera that teach basic coding and introductory data analysis using languages like Python, or some form of database querying are good ways to begin to get a feel for computer-aided analysis. Courses include the University of Michigan’s introductory Python and applied data analysis course, and IBM’s Data Sciencecertificate on Coursera.
In recent years, artificial intelligence and machine learning have become key proponents in decision making and automation. In business as well, newer fields like Business Intelligence aim to apply machine learning models to business data to help in making key business decisions. Business intelligence has been around since the 1960’s in more primitive forms, making use of relational databases and sharing historic data, but today, it is a more present and ongoing process. Algorithms learn from their outcomes and fine-tune their processes, allowing decisions to be informed by incoming data as much as historical data.
Of course, there is no need for everyone to know how to build a machine learning algorithm from the ground up. Most good learning models in use today are evolving algorithms that have been in use for at least a few years. However, for those interested in learning more about it while coming in from various lines of education, there are degrees and online certificates offered at the undergraduate and graduate levels that could serve as an upscaling of skills in data analysis, information gathering and reporting, for anyone with basic programming and database familiarity.