The long standing debate about whether data science is considered real science or not may never reach a consensus. However, we know that science, in general, is founded on the observation of events and the application of certain principles and techniques to analyze those events to explain phenomena or predict future events. Science starts with the formation of hypotheses or asking questions, research through experiments, observations, or analysis, followed by the interpretation of findings.
Thus, for both data science and real science to be conducted, data, statistical skills, and domain knowledge are involved. Data science, like research science and applied science, is to a great extent objective and empirical. For these reasons, experts consider data science to be real science or at least the fabric of real science. Therefore, whether you are a Purdue data science graduate, research science, or applied science professional, all these fields contribute significantly to operations in various sectors.
Applied science, research, and data science, on the other hand, are distinct from each other. Some have felt that data science is rid of novel research, which is often the focus of real science. The data science field encompasses mathematics, statistics, and computer science where vast complex unstructured datasets are analyzed to draw insight and useful information for the purposes of problem-solving. It may not involve novel research or new discoveries as is typical of real research, although most domains of real research are founded on the precepts of data science.
The data scientist, applied scientist, and research scientist: The distinction
While the roles of the data, research, and applied scientists are similar and overlap in many ways, they are also different in many ways. For those hoping to plunge into the field of research, it is important to know the difference between the three roles as they are applied differently in different scenarios to achieve different objectives. This will help them to decide the best career path to pursue.
Let’s distinguish the three.
The data scientist
The data scientist role is responsible for identifying data sources, collecting, processing, and modeling data. At times they also double up as analysts to analyze, interpret, and draw useful information from unstructured datasets that will solve complex business problems.
Data scientists will develop need-specific models that monitor specified business metrics. These could be sales revenue, gross margin, customer satisfaction, and others.
The research scientist
The research scientist, also known as the pure scientist role involves developing hypotheses, collecting information, and interpreting this information using theoretical and experimental techniques to answer questions about natural phenomena. The knowledge gained builds on existing knowledge in the natural sciences, health science, environmental science, and social science, among others. The application of the knowledge gained through research science is not necessarily the result of the research.
Research scientists are experts in their fields of interest and will typically possess master’s or doctorate qualifications.
The applied scientist
An applied scientist conducts scientific studies with the purpose of developing practical solutions to solving real-world problems. Unlike the research scientist, the information obtained by applied scientists from their studies has specific defined applications. The medical and engineering fields have widely benefited from applied science.
Why is data science important?
Our study has shown that data science is the foundation of all research. There are vast amounts of data or big data available to the world today. Data science is the interdisciplinary field that wrangles the data to provide information and insight for data-driven decision-making.
Data science for driving various business objectives
Different industries and enterprises often want to understand their customer behavior and expectations; predict the outcome of a business initiative, project, or decision; grow their brand; identify new revenue opportunities, or even implement the right tools to drive operations. Data science provides the vehicle that analysts use to analyze data, interpret their findings, and present this information as insight for informed and intelligent decision-making.
Data science for security and fraud detection
Also, data science has played a crucial role in the security and detection of fraud in the banking, finance, and other sectors, thanks to data models that have been developed to detect or prevent fraud in transactions, operations, and other system processes as well as to detect vulnerabilities and suspicious activities in systems and processes.
Data science for data management standards and best practices
Data science is larger in scope than the data analytics field as it encompasses not only data collection and data mining but also involves setting the standards and best practices for data handling. Data analytics is just that, analyzing data which is only a small portion of data handling.
Key differences between data science and real science
|Data science||‘Real’ science|
|Education background and qualifications||An undergraduate degree with a background in statistics, applied science, computer science, mathematics or a data related field will provide a good foundation for a career in data science.||Requires an advanced qualification, either a master’s or doctoral qualification, in a field relevant to life sciences and domain knowledge.|
|Employment||Data scientists work in diverse sectors including the private sector, public sector, and government agencies to develop data processes and standards as well as process data, analyze data, interpret, and communicate findings.||Researchers play a vital role in academia and are instrumental in the advancement of knowledge within their fields of expertise. They conduct research studies and publish their findings that can be used as references by experts in the field and other researchers.|
|Nature of work||Data scientists (and applied scientists) typically conduct studies whose findings become applicable solutions to real-world problems.||Research scientists usually conduct studies about the theory that mostly build on a particular body of knowledge without necessarily impacting real-world situations.|
|Average annual salary||$119,414 p.a. Depending on location, years of experience, level of education, and the specific employer||$102.289 p.a. Depending on location, years of experience, level of education, and the particular employer|
What are the top skills required for the data science role?
Technical skills, soft skills, interpersonal skills, and domain knowledge are all necessary for the data scientist role. Soft skills and interpersonal skills help professionals work with others and build strong relationships in a team or with clients and other stakeholders.
These top skills include
- Data mining
- AI and machine learning
- Ability to work with vast volumes of structured and unstructured data
- Statistical skills including data wrangling, data modeling, data analytics, visualization, and predictive analysis
- Creativity and innovation
- Analytical thinking
- Curious mind
- Critical thinking
- Attention to detail
- Leadership skills
- Teamwork and collaboration
- Excellent communication and storytelling skills
- Business acumen
Science can be viewed as more of a way of thinking than an accumulation of knowledge, said Carl Sagan. Every day, there is something incredible somewhere waiting to be discovered whether through data science, research science, or applied science. Interestingly, the discovery will always be in data, a key aspect of all research.
In your career pursuit, the first step to getting started is understanding what each of these fields is all about. Hopefully, this article has helped you gain an understanding of each of these fields.