Machine learning has been making its way into software programming in recent years. But this is just the beginning of a much more extensive paradigm change. Data Science, Artificial Intelligence, Deep Learning - all these terms are making waves in computer science, and they're posing questions that will test even the most experienced programmers.
What is Data Science?
Data science is a branch of statistics, but it veers into other established domains. Some areas include machine learning and data mining. Many successful U.S.-based firms provide data science services as part of the software development lifecycle (SDLC), e.g., A data scientist facilitates the workflows between software developers, business analysts, and users.
Data Science, Machine Learning, and Artificial Intelligence
Data science has a new promising layer related to software development and programming. Researchers are creating algorithms that learn skills and become experts at specific tasks in different fields with very little computational training. The advancement of machine learning, artificial intelligence, and data science makes it possible for developers to build more efficient software without investing too much time in designing their products.
The Biggest Challenges of Currently Using Data Science in Software Development
Software development is constantly evolving. This happens partly because there is a need to adapt the technology to personal preferences and better understand computer science. While new technologies are continuously being developed and introduced, it is challenging for companies to keep up with this progression. Data Science is a hot topic: mainly because companies realize how important data science is and whether or not their software product should be using it. Data science uses massive amounts of data to develop insights, predict potential risks, and create solutions. The biggest challenges are the technology needed to compete with other software products that offer this function to their customers. This new technology can benefit if used correctly out of the box. Companies that have chosen to maximize the benefits of Data Science are finding it challenging to find qualified talent across the United States, even in Canada. Mexico is rapidly becoming a high talent output demographic next door to the U.S. that boasts the most significant amount of Stem graduates across the Americas and with it a large pool of Data Scientists, Machine Learning experts, and AI specialists at a fraction of the costs. As an example; one of the Nearshore Product Development Excellence centers in Mexico, created by Framework Science, is using proprietary data technology to map out top Computer Science talent across the country and helping augment U.S. Engineering teams at a higher accuracy rate and at a velocity to meet the exponential growth technology has to be pushed to market.
Abusing AI with Unethical Algorithms
Software developers are outsourcing algorithms to the AI systems to expect the AI to become more intelligent and make better decisions. The issue is that humans cannot determine when these algorithms have gone rogue.
This blog concludes that Data Science can be used in software engineering today to increase productivity, improve quality, and support a minor team. For example, lower training times for new employees or those just starting the job, requiring less time for on-boarding and modifying the current software system because improvements are made to it instead of differentiating between tweaks.