A Glance Review on Data Science and its Teaching: Challenges and Solutions

The word “data science” has become more popular in recent years, with a growing number of people embracing it. Only a small minority of people, on the other hand, are able to offer a clear explanation of what the term refers to when it is used in context. With no defined term to communicate and understand one another, it is difficult for organizations that are devoted to the collaboration, utilization, and application Data Science to communicate and understand one another. As a result of technological advancements, it has become increasingly difficult to define and execute Data Science in a way that is compatible with how it was previously considered and understood in the past. Specifically, we could now set out to develop definitions of Data Science that are representatives of current academic and industrial interpretations and perceptions, map these perspectives to newer domains of Data Science, and then determine whether or not this mapping translates into an effective practical curriculum for academics. Aspects of data science that differentiate it include how it is now used and how it is projected to be used in the future. Data science is also characterized by its ability to forecast the future.

Keywords: Curriculum, Data science, Quality, Practices, Problem solving.