The median starting salary for an entry-level data scientist is $95,000 per year. Mid-level and experienced data scientists can make up to $185,000 or $250,000 per year, respectively.

Data Science and Information Overload

Despite pessimistic expectations from some market experts, the average data science salary shows no signs of slowing down. Indeed, data science professionals are entering the job market in record numbers, but the average data science salary continues to climb steadily, year-over-year. This is because the demand for data science is scaling with supply, keeping wages growing at a steady pace. But, what exactly is driving this need for more data science professionals?

If you use technology on a regular basis, there’s a good chance that you’ve experienced the overwhelming feeling of “information overload.” Information overload is the uncomfortable feeling you get when you try to go back and forth between multiple devices at once, but instead of multitasking, you end up getting your wires crossed and forget what you were doing. For most of us, this occurs while working on our laptop, blasting our favorite music, and texting friends on our smartphone—all while trying to watch a show on TV. After a while, our brain just can’t process it all and it starts to tune certain things out. And this doesn’t have anything to do with one’s intelligence; it’s simply a physical limitation of the human brain. No matter how smart you may be, you will eventually encounter this threshold at some point or another.

Even the smartest data scientists, who analyze mountains of data from millions of users, eventually hit the limits of what they can comprehend. Today, experts refer to this phenomenon as “Big Data.

But, what is big data exactly? In short, big data is a constant flow of digital information that’s too large for humans to analyze piece by piece. For this reason, the field of Data Science was developed, shifting the focus from pure statistics to the use of AI and Machine Learning to understand massive amounts of information. With these tools, data scientists can better organize and understand large datasets, without getting overwhelmed by the sheer amount of information.

Data Science Salary and Big Data

Because of the arrival of “big data,” data scientists are required to possess a highly technical understanding of programming and computer science. Even a complete understanding of statistics won’t cut it, anymore. You need to first know how to interpret data, and secondly, to create the programs that will interpret it for you. All of these skills require years of experience and education to develop. Unlike an entry-level job in web development, you can’t forgo a formal education in data science.

Furthermore, large corporations that handle digital information, such as Facebook, Amazon, Google, Apple, and Netflix, need data scientists more than ever. These companies, in particular, use key insights drawn from big data to improve their offerings and grow their customer base. Should you decide to pursue a career in Data Science, you’ll become the guy/gal that digs through the dirt to find gold nuggets.

According to Glassdoor, the average data science salary is currently $113,436 per year. A further study from Burtch Works showed that data science salaries grow rapidly with acquired experience. For example, the median starting salary for an entry-level data scientist is $95,000 per year. Mid-level and experienced data scientists, however, can make up to $185,000 or $250,000 per year, respectively.  

What Does a Data Scientist Do?

In essence, a data scientist is someone who analyzes data and draws actionable insights. This description may seem a bit loose, but that’s because Data Science is less about the methods you use and more about the results. Corporations who hire data scientists want to see an increase in sales, downloads, revenue, and customers. They don’t necessarily care how you do it. However, data science jobs do include a few common tasks, such as collecting large amounts of data from various sources, organizing the data to ensure accuracy, applying mathematical models and algorithms to identify trends, interpreting the data, and communicating all findings to decision-makers in the organization. At the end of the day, your success as a data scientist (and data science salary) is dependent on the quality of your findings and the impact that they have on your organization’s goals. 

How Do I Become a Data Scientist?

A job in the field of Data Science requires an intimate knowledge of mathematics and statistics, as well as computer science and programming. Unlike some entry-level coding jobs, Data Science requires formal education and a 4-year degree in the fields of Computer Science, Mathematics, Statistics, Information Systems, and even Marketing. According to research firm KDnuggets, 88% of data scientists hold at least one master’s degree and 46% hold a Ph.D.

Beyond a deep knowledge of mathematics and statistics, data scientists depend heavily on statistical programming languages like R and Python. At the moment, the majority of AI programs are built using Python, since there are a ton of existing frameworks and libraries. TensorFlow, one of the most popular AI frameworks built by Google, is written in Python. On a personal level, Data Science also requires a strong sense of curiosity and willingness to think outside the box. Data Science is both challenging and extremely rewarding because every problem you encounter will be different. If you love puzzles and brainteasers, Data Science is likely to be a great career choice for you.

Well, are you ready to get started? One of the best ways to begin your career in Data Science is by enrolling in a “Powered by Woz U” university. Doing so gives you a whole host of benefits like lifetime access to updated coursework. Plus, students who attend partnering “Powered by Woz U” universities not only receive a complete college education and the support of live teachers and staff but also cutting-edge tech curriculum.

Please check out our page for our Data Science program to get started.