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Surprising Facts About Data Science

Why are so many professional people and college students surprised to learn a few of the basic facts about the field of data science? Maybe it’s because the discipline is relatively new, having grown out of statistics, computer programming, financial analysis, project management, and others. A decade ago, the term was unknown. Today, most top universities and colleges offer dozens of courses for students who want to get into the fast-growing career field.

What are the facts that surprise so many otherwise informed individuals? In addition to the fact that the best data science jobs go to degree-holders, people seldom know that collecting information is a core component of the process, that programming is not a prerequisite skill to enter the field, and that data science is not just a big tech profession. Consider the following details in order to get a clearer picture of what data science is all about.

The Best Jobs Go to Those with Degrees

There are a few myths surrounding education within the field of data science, so it’s important to parse them and deal with each one separately. First, there’s no need to hold a Ph.D. or even a grad school diploma to excel in the industry. However, getting a college degree is almost a requirement these days, particularly amid competition for the top jobs and career paths. For those contemplating data science as their future job category, step one is to figure out how to pay for college.

That’s why the great majority of prospective degree holders turn to private student loans as the most efficient method of covering school-related expenses for a four-year degree. The good news is that anyone can apply for a loan. Acquiring financing in advance is the smartest way to pay for college, especially when the goal is to learn real-world skills and secure a position as a data science expert upon graduation.

More Data is Not Always a Good Thing

Data science is about quality, not quantity. Gathering and cleaning bits and pieces of data-rich information can be a tedious process, but the resulting reports, charts, and other outputs are directly related to the quality of the input. The old programming acronym, GIGO, garbage in, garbage out, still applies and is of particular relevance to data scientists in the 2020s. Researchers can gather thousands of statistical studies, but if the original information that formed the basis of those reports was flawed, the results would not be relevant or usable. Instead of pushing for quantity, experienced data science professionals aim to locate as much high-quality information as possible. Quantity itself is not a bad thing as long as all the original input is useful and relevant.

It’s Not Just for Programmers

The data science field is not an exclusive club for computer programmers and math wizards. While there are a number of programming and math enthusiasts and experts who pursue data science as a career, most people are surprised to find a wide variety of other disciplines working in top jobs. One of the newest trends in business schools is for accounting, finance, marketing, and management majors to venture into data-based science positions after graduation.

Collecting Data is a Primary Part of the Science

So much emphasis in business literature focuses on cleaning, organizing, processing, and analyzing information, and it’s easy to forget about the collection phase. No matter what the industry is, professionals who gather statistics and other data spend a lot of time on two steps of the task. First, they need to identify where relevant bits of information might be hiding. Typical suspects include government reports, online public records, archived scientific files, hard-copy documents in libraries, and dozens of other locations. The next challenge is to gather those resources in the most efficient way possible.

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Ratan Kumar
Ratan Kumar
Ratan is a tech content writer who amasses inspiration from science fiction, cartoons, and psychology. Apart from writing, you can find him playing mobile games and depicting humans.


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