“Don’t get carried away by the suffix behind data science”, an expert said. He highlighted that data science has a long way to go if it is ever going to be truly considered as science.
In this post, we will take a look at the reason that expert believes that there is a huge gap between data science and true science.
Science is a journey towards discovering the best ways to explain what the world is about. A renowned author indicated that the characteristics of a great explanation are that it is accurate, clear, and difficult to vary. What he meant when he said “difficult to vary” is that a great explanation would remain unchanged despite any further discoveries.
While a lot of people may not agree with Charles Darwins’ Evolution Theory, science does. Hence, science will only discard this theory when a new discovery that refutes it is made. Science allows anyone to criticize and test explanations. The aim of this is to leave room for anyone to refute any explanation. Scientists will only change their perspectives if your argument is accurate and indisputable.
Another attribute of science is that it collates values. Typically, criticisms, modifications, and ideas are welcome by scientists. They tend to value the rational opinions of others and their humanistic values. such as doing all they can to make the world better.
DEFINING DATA SCIENCE
Data science aims to collate and extract value from data in other to enhance understanding of what it can tell us. Many consider it to be an evolution of BI, data analytics, statistics, and any other analytical field.
The massive stream of data available on the internet is one of the reasons for the emergence of data science in our world today. Other contributing factors are algorithms learning machines, the improvement of computer science, and the increase in more advanced computational power to process that data. Want to learn more about data science? Get more info here.
WHY SCIENCE MAY ALWAYS BE DIFFERENT FROM DATA SCIENCE
The first time the term “data scientist” was used was to designate a job title, the “data scientist” needed to be skillful in statistics, business knowledge, machine learning, and computer science to qualify for this role.
The fact that a lot of trials, errors, and mistakes are often involved in data is one of the reasons why experts may not consider data science to be real science. However, what matters is the way data is collected and analyzed. An expert highlighted the garbage in, garbage out system involved in huge data analysis. In other to buttress his opinion on how unpredictable data can be he further stated that a data scientist’s only need is to subset data to make it say anything.
This highlights the need for data scientists to take their time instead of jumping to conclusions whenever they analyze data. Regularization and cross-validation are two thorough ways a data scientist can minimize errors when analyzing data.
In conclusion, the reason why data science is quite a long way from science is that it is more like a kind of observational research. Some experts believe that while a data scientist will most likely identify some correlations, he does not know anything about the causes.