# Top 3 Myths About Data Science Debunked!

March 29, 2022 2022-04-03 16:16## Top 3 Myths About Data Science Debunked!

Although a new field, data science has emerged with both excitement and confusion. Confusion because it is still new and evolving and because of this a lot of myths about data science are being thrown around.

With our industry experts, we set out to bust the top three myths about data science that are confusing those who wish to join this lucrative field.

Data science is everywhere. It influences every facet of our life, yet sometimes we never know. Data science backs together a beautiful combination of business, technology and mathematical concepts to achieve so much that is applicable across all industries.

Different industries use data science to achieve different goals. For example, marketers use data science to predict the likes and dislikes of their target audience. The banking industry uses data science to predict the risk involved in lending out to different customers.

If you are into politics or understand sports, you’d realize that Sports clubs use data science to prevent injuries to their players and politicians use data science to find out their chances of success in fundraising for their campaigns. These are just simple applications of data science.

The 21st century has witnessed the highest demand for data scientists. There have been millions of data scientist jobs available globally, yet at least 2 to 3 requirements in these jobs go unfilled due to a lack of matching skills.

Even today, the gap between demand and supply of data science talent is still so huge. This presents an exciting opportunity for those who wish to join data science to join now.

Testament to any emerging field, data science also has an equal share of excitement and confusion. For one, companies are asking themselves how to select a good data scientist, while professionals, on the other hand, are wondering how do they become good at data science.

This has led to a barrage of perceptions and floating opinions about data science, with tens of myths being thrown around by non-experts. So we’ve picked out the top three myths about data science, and we want to bust them now. Shall we?

## The top 3 data science myths busted!

Let’s go.

### Data science belongs only to mathematical geeks.

Although data science indeed requires a good understanding of probability and statistical concepts which are primarily math-based, data science is widely applied in business.

The use of statistical formulae to calculate and obtain results of complex equations is long gone among data scientists. The use of these statistical formulae has already been replaced with sophisticated software, some of which are free while others are paid.

All that is left for data scientists of today is to understand the interpretation of these techniques, which would mean when and how to use them, as well as how to interpret the results that are yielded— rather than focus on the mechanics of the applications.

**Illustration: Chi-square**

One of the most popular statistical measures among data scientists is the Chi-square. Today, the knowledge of the chi-square is nearly irrelevant. The crucial thing is what a data scientist needs to understand and know about the Chi-square.

Unfortunately, most of the education available today is concentrated on the formula and calculation of the Chi-square test, not the application and how to interpret the results of the Chi-square test.

Excel, R and SAS have replaced the need to understand the formulae and calculations involved in the Chi-square test. What’s important now for data scientists is to understand when to use this test and the interpretation of the Chi-square test results.

For this reason, data scientists don’t need to be necessarily mathematical geeks. They need more common sense and logic than pure mathematical abilities. And though a data scientist needs to be comfortable with the numbers, logic and common sense are extremely important in arriving at a good analysis.

That said, those interested in data science shouldn’t be intimidated by the mathematical complexity that they think is involved in data science. Most of this work is done using software that leaves common sense, logic, and moderate mathematical abilities as key ingredients of success in data science.

### Learning a data science tool is the equivalent of learning data science

Most people recklessly equate learning data science tools such as SAS to becoming a data analyst or scientist. You can learn SAS to become a SAS programmer, but never a better scientist.

A good data scientist will go beyond the tools of data science and master skills that will build their ability to accurately apply the various predictive modeling tools.

Learning data science tools is essential and useful but is not the only thing that data scientist needs to learn and know in their job. One never becomes a data scientist by simply registering for tool-based certifications, like those common over the internet today.

Data science requires more than just knowledge of tools. Even organizations that are hiring data scientists look for mathematical programming and business skills, and not just the ability to use data science tools.

### Data scientists will soon be replaced by artificial intelligence

This one is almost convincing. The fact that data science is a new field and continues to evolve means that some of the activities that are currently done manually will soon be automated in the future. A good example is data cleansing.

However, in order to cleanse data, one must have the right qualification, skills, and understanding of data to tell the machine the right thing to do.

Automation is a game-changer in any industry. As technology evolves, more and more sophisticated algorithms are being pushed into the market with the hope of eliminating data scientists.

Success to any extent, even with the use of sophisticated algorithms, still requires sound judgment, hard work, and domain expertise. Unfortunately for them, data scientists are here to stay and the demand for their skills is now higher than the years before.

In fact, data science is ranked number four in the list of top 50 best jobs in the world today by Glassdoor.

## Wrap up

Data science is a lucrative profession. And although it’s new to many people, its relevance across industries cannot be undermined. The myths about data science that people throw around are purely due to misinformation. We hope we have helped you debunk these myths. If you have a passion for this field, take the first step forward and sign up for a class.

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Learning data science without working on real-world projects makes no sense. At PlumlogixU, you’ll complete at least 2 major capstone projects worthy of your portfolio as a data scientist.

Besides, you’ll work on and complete small projects that reinforce specific technical aspects learned throughout the Bootcamp. These are projects you can show to your future employers soon after graduation.

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