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Debunking The Four Most Common Data Science Myths
IT Graviti

Debunking The Four Most Common Data Science Myths

Data Science Myths: Data Science is a diverse field that uses scientific methods, and processes of algorithms to extract useful information from a bunch of data. And the data is generated by organizations which can be of user’s data, sales data, or any other kind of data. The information extracted by the use of data science will help the organization to take steps that will help in their success.

Every Organization collects data whether it is user’s data, sales data, financial data, or any other data regardless of its size. That data becomes very useful when it is used correctly such as by application of data science. Organizations use data science to get insights into the user data for the success of the organization. The problem occurs when the organizations are not able to use data science to its fullest potential. To use data science correctly, every organization needs a data scientist who knows the tools and techniques of data science.

Data science is very useful for the success of any organization.

But there are myths about this field. And some are very serious myths that need to be uncovered and the truth behind the myth should be told to people.

Normally, anything that gains more attention quickly becomes what everyone is talking about. The more people talk about it, the more misconceptions and myths generate. The same happened in Data Science, with the increase in the use of Data Science, the myths about it are continuously increasing.

In this article, we are going to uncover the four most common myths about data science. And will tell you what is the reality behind the myth.

Everyone should know the truth behind the myths as they only give a negative impact on anything which is revolving around us. So let us start debunking the most common four myths about data science below.

Myth 1: Data Science is only a hype that people talk about it.

Many people think that data science is just hype and won’t last long. Data science has become one of the most important aspects of any organization’s success. As it helps the organization to gather useful data which will help them find what changes are needed to succeed. This data includes the reviews of their customers, their interests, and more information. The data plays a vital role in any organization’s success as it finds the insights that will ultimately help the organization to take steps for its success.

Data Science has made a significant difference in the instant rise in the company’s success by the data being generated every minute in the organization. The data generated is evaluated then to get only useful data, and important decisions will be made based on the data generated.

This is a very common misconception in data science. A lot of people debate on this topic that it is just hype and it won’t last long. But it is not true, data science-led many organizations to stand out from their competitors and get succeeded. The data generated by companies will be of no use if we don’t use data science. With data science, the data can be structured, analyzed, and useful data can be fetched, which will eventually help the organization to find insights into the data and take suitable actions for success.

Myth 2: Data Science is only about model building.

This is a very common data science myth you may have heard. Data Science is not only about building models, as Building models is 15-20% part of total data science application. The data science pipeline includes data cleaning, data visualization, data preparation, data acquisition, and model deployment. And all these aspects take up to 50-60% part of Data Science. This means data science is not only modelling the data even if it is a part of the whole data science application.

Data Science is more than just building models. Building Models include tasks such as data collection and data cleaning etc. There are various layers of any data science project. The model building is just a part of the layers or the lifecycle of the data science project. The lifecycle of a data science project includes- gathering data, building a hypothesis model, collecting useful data, verification, cleaning, and analysis of data. After that, the part comes from designing the model and verifying the model. As we see, it is just a part of the lifecycle of data science.

So it is not all about building models. It is more than that. Data Science is not limited to model building, instead, it includes many steps for a project.

Myth 3: To go into this field, higher education is required

This is another very common myth in Data Science that says to go into this field, you need a higher education degree which is not true. This is a misconception. Many people think that it is a must to complete a Master’s in Data Science to become a Data Scientist. The degree is not important at all. The most important thing is that do you have sufficient knowledge in this field or not. If you take admission to get a degree and don’t get that much knowledge of data scientist level, then it will be of no use. Going to university and earning a degree will help you get a theoretical aspect of data science, networking, and the deep knowledge of the technology that you want to use in Data Science.

It is just a myth that earning a degree will help you go in this field, but you should have interests in this field to learn and know more about data science, you should go to university with the motive of learning. That will benefit you in your journey to become a data scientist. The other factors also matter such as your financial condition and others. So before taking admission to the university to become a data scientist, you should think about every aspect such as the motivation to study data science, your financial situation, and your ultimate goal in this field.

Organizations today are lacking the right use of data science due to this myth that people don’t go in this field because they think Ph. D. or any other higher degree is a must to become a data scientist. Those people who have knowledge of data science tools and strategies used to manipulate the data and fetch useful information, but are not professionals in this field can also grow and become data scientists. You just need the knowledge and skills in this field that you can show up in the organization and can enter this field without any big problem.

Myth 4: Not every business requires data science

We can see several companies getting benefits by the use of data science such as tech companies, product-based companies, service-based companies, e-commerce companies, etc. These companies use data science to gather the information or data about their customers, their likes, their dislikes about their product, the reviews, and other useful information that can benefit them for growth.

But those businesses that don’t need to collect data or anything like that, then data science will be of no use for them. Regardless, it is a vital part of organizations as data is the most important factor when it comes to looking up previous growth, strategies, sales, and all. So data is the thing that an organization can’t ignore. The company needs to find new opportunities, identify the customers, and create effective decisions based on past experiences, then data Science can benefit your business to succeed.

Many organizations have the wrong opinion that they don’t require data science to be used in their company, and this is only for large businesses that generate a big amount of data. Instead, small organizations that generate a small amount of data can also benefit from data science if used in the right direction.

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