Hence together, let's bust some of the common misconceptions about data science and check out myths related to Data Science.
Data science is a field that has gained popularity in recent years. It is a unison of technology, business, and mathematics that impacts every aspect of our lives. People feel the transitions in data science are complex, and you'll have to study math, statistics, or programming. But that is not the case. One must accomplish that, but you must also combat the myths about data science you hear from others and chart your course through them!
"Data science is a field of study that deals with large amounts of data and uses cutting-edge tools and procedures to uncover hidden patterns, generate useful data, and make business decisions."
Before companies hire data scientists, they will indeed check if the person is clear on the basics or not. This field has helped many organizations process their massive volume of data. Many thoughts and perceptions of data science are also circulating with popularity, some of which are not factual. Hence together, let's bust some of the common misconceptions about data science.
Lack of understanding gives birth to such myths. The fact for data science is that one needs an understanding of statistics and probability because most of the predictive modeling techniques are based on these concepts. However, you will never have to utilize statistical methods to calculate the outcomes of complex equations as a data scientist. The requirement here is more of common sense and logical implementations. This clears the air of data science being a field of only geniuses.
As it is a developing industry, we expect to see all manual processes become automated over time. Increasingly advanced algorithms are being developed to obviate the requirement for a data scientist. That, however, is unlikely to happen. Solid decision, domain understanding, and hard work will be required even with the most advanced algorithms.
SAS, Apache Spark, BigML, and many more tools and programming languages are available for modeling and organizing extensive data. The myth related to tools is that mastering one tool can make you an expert data scientist. In reality, that is not the case. Data science necessitates proficiency in a variety of tools and computer languages. Data science isn't all about programming. It's merely one part of a bigger picture. In reality, one needs to gain knowledge of all types of tools involved.
As the hype is created about the data science field, everyone has a lot of expectations about it. Knowing what your client requires is good, but can that be predicted in all cases? In reality, there are multiple layers in a data science project. Creating a model takes various stages, and there is a life cycle that includes market research. There is a term market basket analysis which is a mix of clustering algorithms and association rules.
Even small companies think to hire data scientists once they reach large customer strengths. In the same way, even the data scientist will think that they can work for companies dealing with huge amounts of data. However, bulk data can be your ultimate goal, but it is not needed. Any amount of data can be processed with the help of data science.
Data science has helped businesses in various ways. By not trusting on myths, one needs to be more clear about the basics. I hope with this information, we have been able to clear a few of the myths about data science. Demand for Data Scientists is already sky high, and the aspirants need to make the right career move by equipping themselves with in-demand skills and expertise.
