Questions, Answers, and Myths About Data Science Degrees

How can students increase their chances of loan approval? What are the top institutions for people who want to earn degrees in data science? What’s the 2023 job market like, and what kinds of skills are hiring agents looking for? Additionally, is data science really all about coding and intense IT programming expertise?
Those questions and many others should be on your mind if you are aiming to complete a college or graduate degree in the subject. It’s important to know the opportunities, expenses, and facts before committing to a career path. Here’s the least you should know.

Can Students Use a Cosigner for a College Loan?

Not only do private student loans with cosigners have a better chance of being approved, but they also usually come with much more favorable rates, terms, and other conditions. If you want to assist an employee or family member who’s headed to college, serving as a cosigner is a powerful move.

Many young adults have little or no credit history and few financial resources to fall back on. Cosigners can completely change someone’s chance of getting the funds needed to cover tuition, fees, books, and other expenses.

What are the Best Schools?

It can be misleading to focus on the top five or ten schools for any given professional area, but there is value in learning what institutions lead the pack. That’s because, while local universities, state schools, and community colleges do a great job of educating specialists in data science, some candidates prefer to apply to at least one top-five program in the hopes of gaining acceptance.

Currently, according to US News & World Report, the leaders are Cal Berkeley, MIT, Carnegie Melon, the Georgia Institute of Technology, and Stanford University.

What’s the Job Market Like for 2023 Grads?

According to recent information about the profession from the US Bureau of Labor Statistics, the typical profile of an entry-level job in data science includes a six-figure salary, the need for a bachelor’s degree, no prior experience or on-the-job training, and full-time work in an office setting.

Compared to most other employment categories, the projected growth rate for the next decade is about 36%. There’s never been a better time to earn a college or graduate degree in the subject.

What Skills are Needed to Succeed?

Surprisingly, a large number of the skills needed to succeed in other disciplines, like business, liberal arts, and engineering, are also needed for data scientists. Aside from a core set of unique talents related to computer languages, information analysis, and other technical skills, data science professionals need to be capable communicators and problem solvers. While there’s no need to be a math genius, the very nature of analysis calls for high-level mathematical acumen.

For those getting ready to pursue degrees, time management is another essential piece of the success puzzle. Not only do college students need to plan their classroom time, projects, study sessions, and free time, but they should maintain this ability once they transition to the working world. In independent, smaller companies, it’s common for new hires to set their own schedules and meet numerous deadlines per week on a variety of assignments.

Data Science is All About Coding

For reasons unknown, millions of people believe that the profession is focused on computer language coding or programming, or both. While it’s always helpful to know how to code and to have top-level skills in at least two languages, the emphasis on coding is a non-starter. Because data science is such a wide field and includes dozens of skill sets, limiting it to a single capability, particularly one as narrow as coding, is far from the reality of the profession.

Who Does the Gathering?

There’s a common misconception about many data-related professions. It is related to the analysis of information as opposed to the initial step of gathering it. Those who are not in the field or who are just embarking on a degree program tend to think that the collection phase is separate from the analytical tasks that follow. However, in the real world, it’s quite common for analysts to amass their own datasets for no other reason than necessity.

Keep in mind that many startups and small companies in the tech and science fields have limited personnel. That means a handful of people are doing all the work, multi-tasking on any given day, and studying information sets that they culled themselves. While the textbook version of what a typical data scientist does might not mention collecting raw facts and statistics, those preparing for careers should know that collection is part of the job description in many cases.

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