Busting myths related to Data Science

In recent years, data science has become increasingly popular. Data science is the unification of technology, mathematics, and business that affects every aspect of our life. Many people believe that data science is a complex field and they will need to learn math, statistics or programming. This is simply not true. You must not only achieve this, but also dispel the data science myths you have heard from others.

Data science is the study of large data sets and uses advanced tools and procedures in order to discover hidden patterns, generate valuable data and make business decision.

Companies will check whether a person has mastered the fundamentals before hiring a data scientist. Many organizations have used this field to process the massive amount of data they receive. Data science is also popularized by many thoughts and perceptions, some of them not true. Let’s debunk some of the most common data science myths together.

Data Science is the field of genius

Such myths are born from a lack of understanding. Data science requires a solid understanding of probability and statistics, as most predictive modeling techniques are built on these concepts. As a data scientist, you won’t have to use statistical methods to calculate complex equations. Here, common sense and logical applications are more important. It is no longer true that data science is a field for only the most gifted.

AI will replace data science

We expect that as the industry develops, all manual processes will be automated. As algorithms become more advanced, the need for data scientists will diminish. This is not likely to happen. Even the most advanced algorithms will require a solid decision, domain knowledge, and hardwork.

Data scientists are complete when they have mastered a tool.

Many tools and programming languages exist for organizing and modeling large data sets. It is a myth that mastering a single tool will make you a data scientist. This is not true. Data science requires proficiency in many tools and computer programming languages. Data science is not just about programming. This is just one aspect of the bigger picture. In reality, it is important to learn about all the tools.

Data science is only predictive.

Everyone has high expectations for the data science industry, as it is gaining popularity. It’s good to know what your clients want, but is it possible to predict this in every case? A data science project is actually a multi-layered process. The process of creating a model is divided into stages and includes market research. Market basket analysis is a combination of clustering algorithms, association rules and other methods.

Data Science is only concerned with bulk data

Once they have a large number of customers, even small companies begin to consider hiring data scientists . Even data scientists will believe that they are able to work in companies with large amounts of data. Bulk data is a great goal but not necessary. Data science can process any amount of data.


Data science has benefited businesses in many ways. To avoid falling for myths, it is important to understand the basics. With this information, I hope we’ve been able dispel some of the myths surrounding data science. Data Scientists are in high demand, so aspirants should equip themselves with the skills and expertise that will be most useful.


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