Why You Should Become a Data Scientist

March 11, 2021

What do you look for in a career? Chances are, you’re looking for a way to make use of your particular talents, a field that’s secure and reliable, a work/life balance, and good compensation. For the right people, data science offers all of that and more! It was LinkedIn’s #1 Most Promising Job in 2019, and Glassdoor’s 2nd Best Job of 2021! Actually, Data Scientists topped that list from 2016 to 2019 before being dethroned by developers, which we also train at Codeup. So why all the hype? What makes it the best? Keep reading to learn why you should become a Data Scientist!

Use Your Talents

Do your colleagues hate you for overanalyzing things? Have you found yourself turning everything into a spreadsheet? Do you make your life decisions based on careful calculations using past data? You might thrive as a data scientist! This is the first section on our list because data science isn’t for everyone, and it’s important to consider where your strengths and interests actually lie. If you don’t have a penchant for numbers and historical patterns, or if careful analysis bores you, this may not be for you. For some people, though, data science is fascinating, rewarding, and a great way to make use of their talents and natural thought processes. Check out how much our students love what they do every week in this blog post.

The Field is BOOMING

The field is booming, the demand is high, and the job is the best. 2020 was the first year since 2016 that Data Scientist was not the number one job in America, according to Glassdoor. Now it sits at Number 2, but rest assured, the growth and demand show no signs of slowing down. Since 2012, there has been a 650% increase in data science positions. However, the supply of data scientists remains relatively low. Sure, it’s been the number 1 job for a while now and people are catching on, but more and more positions are also opening, so supply isn’t actually catching up to the demand. At least, for qualified data scientists.One reason for the continued low supply is that prospective data scientists who go traditional routes are often stuck in school for 2-4 years. Meanwhile, the demands and technologies are changing rapidly, so what they are learning risks being outdated by the time they graduate. Another reason is that a Master’s Degree does not in and of itself prepare grads with the skills needed for the job, or even just to nail their technical interviews. They might learn about data science for years, but if they don’t practice it, don’t have a repository that they push to regularly, haven’t built a data pipeline, don’t have project experience, etc., they aren’t ready to work as a data scientist. This lack of post-grad readiness is another contributor to the low supply, despite many graduate students thinking they’re ready for the job search. (Thinking of getting a Master’s Degree? Learn more, here.) Altogether, this makes a trained data scientist all the more valuable, relatively rare, and in very high demand.

Job Security

So, you know that demand is high now and that properly qualified data scientists (with flexible demands) will have no trouble finding a job. But you don’t just want a job now, you want a career for the long haul. Is this just a trend that’s going to fizzle out? Unlikely. Will this still be a secure path in the future, say 10 or 20 years down the road? All the data say yes!The U.S. Bureau of Labor Statistics lists Data Scientists as one of the fastest-growing occupations. They project a 31% growth rate with nearly 12 million new jobs between 2019 and 2029. More and more companies will have no choice but to start using data-driven decision making, lest they risk being put out of business by their data savvy competitors. Artificial intelligence (AI), a subset of data science, alone is creating millions of jobs! However, it's also wiping out over a million jobs. Want to make sure your skillset stays in-demand? Start a career with job security. Employers will always need humans to program the machines, understand the data pipeline, write the algorithms used by AI, and continue maintaining the tech and the data.

Work/Life Balance

Many of Codeup’s career transitioners know what it’s like to work in oilfields, call centers, food service, sales, retail, and teaching. The ones that don’t require manual labor or being on your feet all day still require odd hours, lots of emotional exertion, or giving up weekends. Some people love this sort of work, but others are missing that work/life balance. Work/life balance is something that’s completely subjective based on where you are in your life, how much you enjoy your work, how much is required of you, and so on.With a career in tech, you will very likely be working at a desk from 8 or 9am to 5 or 6pm with weekends off. You won’t be manual laboring, rushing to get someone to cover your shift, stressing about getting in hours, or speaking to customers or children all day long. Will some days be more stressful than others? Are you going to have to stay late sometimes to meet deadlines? Will you spend some of your “free” time learning the newest technologies? Yes. But for the most part, you can relax. You’ll have a steady work schedule, an average stress level, above-average flexibility should you need to adjust your schedule or take time off, and you will almost always have evenings and the weekends to yourself or to spend time with family and friends.

Great Compensation and Benefits

Interested in boosting your earning potential? Data science is a lucrative field with a highly valuable skillset. You could potentially double your current salary with your very first in-field job. Your employer might also cover health insurance, provide retirement contributions, and offer flexible paid time off. Entry-level data scientists have some of the highest starting salaries around, with averages of:

And it’s only upward from there as your career progresses. In San Antonio, which is where our headquarters are, we’re seeing salary averages of:

Want to start earning six figures within a few years? Become a data scientist. With just a few years of experience, your talent becomes much more valuable. Keep reading to see how long it takes to advance.

Room for Growth

In the tech field, room for growth is limitless. There are always new technologies to learn and adapt to, new skills to perfect along the data pipeline, and career opportunities abound. The next step after beginning your career as a Junior Data Scientist is often Senior Data Scientist, which is the middle level. Typically, this requires 3-5 years of relevant work experience and the abilities to write reusable code, formulate machine learning algorithms, and build strong data pipelines in cloud environments. After senior is the top level, the most experienced member of the data science team. You may see them referred to with different titles, such as Vice President, Director, Head, or Chief. They lead the data science team, have over 5 years of experience, and are fluent or very well-versed along the data science pipeline. This person knows best practices for building and deploying a predictive model, can efficiently write code, and will know the highest impact projects to work on (TowardsDataScience).So, you’ve got a booming industry, a secure field, a skill set that’s in high demand, the ability to advance your career, a lucrative salary, a solid work/life balance, and to top it off, you get to do what you’re good at and passionate about. No wonder it’s the best job out there! If you enjoy working with data, this could be an incredibly fulfilling career path. Did you know it only takes 6 months to actually become a data scientist? Codeup will take you from “never heard of it” to your first job in-field. Interested? Apply now, then we’ll set up a call to discuss it further!