Common Data Science Misconceptions

Continuing the discourse surrounding the new oil of the 21st Century ie. Data, it is noticed that this is still a sensitive topic or rather a touchy subject for businesses of all sizes. Despite the enormous advantages and benefits data science has to offer, not only are many reluctant to adopt the related systems and hardware, but when they do make the leap, they lag when it comes to the proper use of the information collected. As a new field, it is no surprise that there are several misconceptions surrounding it. In this article, we will take a look at a few of these misconceptions and clear up all doubts you may have about the Data Science field.

Data Science is only to be used by large organizations handling vast amounts of data. This in fact is one of the most popular misconceptions about Data Science. Many companies and business owners believe that data science cannot be applied in small and medium-sized organizations since it needs sophisticated infrastructure in order to operate on data.

The truth is a small team of professionals can establish statistical analytics in the organization by using a data-driven strategy to extract useful information from the information that is already accessible. Speaking about infrastructure, there are several open-source tools available for accurately and effectively processing enormous amounts of data.

The integration of workflow with data science seems quite challenging to a lot of people, as they find it confusing due to little or no understanding of how data science works. This brings me to the second most popular misconception about data science – “Data science is complicated and difficult to adopt”.

Instead of collecting all the data through which the finest insights can be extracted, a common practice by most organizations is the collection of only the data that they think is valuable and insightful. There is no single right path to adopting data science, and thus, sometimes the wrong path complicates the problem even though it has a simple solution.

There is a shared misconception that data science only provides accurate results with large data sets. Many individuals and organizations fall under the notion that large sets of data analysis lead to greater accuracy. More data does not necessarily mean higher accuracy. It doesn’t mean more insights, nor does it mean you’re getting more value out of your data. Like everything else, it is quality over quantity.

Even if you are holding on to an enormous amount of data, it might not provide any value. It’s the quality of the information collected that matters. The procedure adopted for analysis should be effective and precise and not the amount of data to get accurate results.

Another misconception that is gradually selling is the idea that Artificial Intelligence will soon replace data scientists. It is possible to assume that as data science develops, artificial intelligence may eventually take the position of the manual tasks performed by data scientists. However, a machine cannot decide for itself how to clean the data, create an effective model, improve the accuracy of models, The individual with the appropriate qualifications must make these judgments.

Even if efforts are being made to create increasingly complex algorithms with the intention of reducing the need for data scientists, it is highly unlikely that this will happen anytime soon. Even with the most sophisticated algorithms, maintaining the businesses would still require someone with sound judgment and domain knowledge.  

Data science is currently one of the most in-demand skills because it is one of the most popular topics of discussion. However, it is equally crucial to conduct the required study and dispel all misconceptions and assumptions before actually engaging in Data Science in order to fully realize its potential.

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