IS DATA SCIENCE JUST A BUZZ WORD?

The word data science is definitely a broader word to use. There is a lot of demand for “data scientists” but the requirements for these positions are always ambiguous. When we search for data scientists in LinkedIn we can find more than 500k profiles. But in the real corporate world, there are only a few real data scientists. This because all the people who work with data consider themselves as data scientists to gain attention in the job market. This is leading to imposter syndrome among several data scientists and aspiring students to wants to be a data scientist. First, we will discuss the word data science and where it originated from to have in-depth meaning of data science and what exactly data scientists do in their workplace?

In 2009, Google executives insisted statisticians will have the “sexiest job” for the next 10 years. That was a decade ago, but I recall that being a strange sentiment. But in 2011 “Harvard Business Review” mainstreamed this concept called “data science” and declared it the sexiest job of the 21st century.SQL developers, analysts, researchers, quants, statisticians, physicists, biologists, and a myriad of other professionals rebranded themselves as “data science” professionals. Silicon Valley companies, feeling that traditional role titles like “analyst” or “researcher” sounded too limited, renamed the roles to “data scientist” which sounded more empowered and impactful. According to Wikipedia Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.

When I think word data scientist, the only thing that comes to my mind is the business perspective of domain and the project in which the techniques are applied. For any domain or project, the ultimate end goal is to achieve profit and customer satisfaction. So my goal is to achieve profit to the company with the help of data science tools and techniques. I believe our technological innovations are trending toward societal good — tools and services that make our communities safer, smarter and, yes, more efficient. Companies such as Amazon, eBay, Netflix, Facebook, Twitter, Spotify and many more rely on tailored content, advertising and personalized experiences. These companies were among the first to harness the power of big data, which is generated at an estimated 2.5 Quintilian bytes each day. To get insights from these large data set we need to have powerful computational hardware as well as a good data scientist who has good domain knowledge.

University of the Pacific's MS in Data Science program uses a hybrid approach that combines the convenience of online learning with hands-on experience in the classroom. Online sessions are taught on weekday evenings, and classroom sessions are taught on the weekends. All courses are conducted live, with your professors, including the online, interactive sessions. All lectures are recorded so that we can review them later, if necessary. The program culminates with the Capstone Project, which allows students to apply the knowledge they have gained by working with industry professionals to solve a real-world problem. We have a quite different approach of 1 and 2 credit courses than tradition 4 credit courses which helps to cover the vast data science field which includes subjects Frequentist Statistics, Bayesian Statistics, Machine Learning, Linear Algebra, Research Methods. Another interesting subject is Hot Topics, where we as a class discuss different aspects of Data Science. The discussions are not limited to business applications of Data Science. We converse about subjects like Ethics, Data Science in Politics and Mind Mapping.

A true data scientist is equipped with knowledge on business, data, statistics, coding and the ability to translate the output into plain English terms that is easy for the business to understand. However, this is often hard to find in one single person. Hence, this is usually expanded into multiple roles. There is a real shortage of data scientists and leaders in the corporate world who can actually help drive the function whilst making it profitable for the organisation.