In today’s data-saturated world, data science has become one of the most important tools for success. Businesses and organizations of all kinds gather data about their products, processes, customers, and employees on a regular basis. But unfortunately, they often don’t know how to properly organize, filter, and decipher that data. In other words—they have all of this information, but what does it mean? What can they do with it?
From this need, data science has emerged as a relatively new discipline with a lot of excitement and growth. In the simplest terms, data science is the act of making sense of data. It requires a specific set of skills, includes a range of fields and specializations, and offers multiple lucrative career options.
If you’re already working in data science, or want to make a pivot into this booming field, then be sure to read our latest resource, “Diving Into Data Science: Skills, Careers, Trends, and More”. The e-book contains pertinent information on industry stats, job projections, and the difference a master’s degree can make in your career.
Before you start reading the e-book, though, we recommend reading this blog to get a better understanding of data science and its applications, as well as some of the specific knowledge areas, education, and job titles associated with data science career paths.
What is Data Science?
Data science is an exciting, diverse discipline that includes mathematics, statistics, advanced analytics, specializing programming, machine learning (ML), and artificial intelligence (AI). Professionals in data science combine these areas of expertise to understand large, often unorganized or unfiltered sets of data. From this, they can gather insights and form conclusions to help organizations make better decisions.
Another benefit of data science is that there are many different specializations within it. This can include business intelligence, data mining and statistical analysis, data visualization, database management, data engineering, cybersecurity data analysis, data analytics, machine learning, and artificial intelligence. Its widespread use and benefits also apply to multiple industries, such as telecommunications, health, agriculture, retail, finance, marketing, information technology, and insurance.1
A Brief History of Data Science
The term "data science" has been used since the early 1960s, when it was often interchanged with computer science and statistics.2,3 Statistician John Tukey is credited with making the first academic move toward modern data science by remarking on the connections between statistics and computers. As more distinctions grew throughout the 70s and 80s, including a piece by Peter Naur that used the words “data science” over and over, William S. Cleveland of Bell Labs published the paper, “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics” in 2001.4
Since then, the demand for data science professionals has exploded. Data scientist was deemed the “sexiest job of the 21st century” in 2012, has a projected 35% job growth through 2031, and ranked third in job satisfaction in 2022.4,5,6
How to Become a Data Scientist
If you aren’t already in the field, making the transition to data science will require a little bit of time and effort—but it will pay off exponentially once you land your first job. Make sure you’re prepared by obtaining the right education, training, and experience.
According to Guy Gomis, senior vice president for recruiting firm Brainworks, “Data science degrees pay off since the skillset is in such high demand.”7 Because of their importance and technical qualifications, career opportunities in data science almost always require a bachelor’s degree. Depending on what the role entails, a job description might ask for a bachelor’s degree in data science, statistics, or business analytics.
But for many mid to higher-level data science positions across the board, a master’s degree is often expected, if not required. To help you qualify for the titles you want, a master’s degree in data science will give you deeper knowledge in specific, in-demand knowledge areas; train you in the most up-to-date techniques and platforms; and give you exposure to how other industries, companies, and professionals are using data science to their advantage. Even better, studies show that data scientists with a master’s degree earn an average of 17% more than those with just an undergraduate degree.7
At New York Institute of Technology, there are no course prerequisites or technical background required for admission to the Online Data Science, M.S. That means you can learn and apply advanced data science to whatever position or industry you currently work in.
As previously mentioned, data science includes a lot of other focus areas. You can complete bootcamps or one-off courses in certain subjects, but full mastery of data science requires much more time and immersion than short-term training can give you. That said, the top competencies that employers look for are:
- Computing theory
- Advanced data science
- Machine learning
- Data visualization
- Programming languages (SQL, R, SAS, Python, etc.)
- Deep learning
Data Science Career Paths
“Companies are… looking for ways to operate more effectively and efficiently using data and AI and machine learning,” says Michael Yoo, general manager of technology and developer portfolio. “All of this means nearly every company on the planet needs data scientists and data analysts.”7
Data science professionals work with everything from text and numbers to audio, video, and images to make connections, analyze past events, predict outcomes, and uncover new hypotheses within and between datasets. They usually work in a larger tech team with other data scientists, analysts, engineers, and developers.
First, let’s clarify a common conundrum for a career in data science and analytics: whether to become a data science vs. data analyst. Luckily, these two roles are similar enough that they don’t warrant radically different education or experience, but they do have noteworthy differences. While data scientists find ways to collect, clean, organize, and export data, data analysts spend most of their time reviewing and interpreting data that is at their disposal. Data analysts must then translate that data and communicate their findings and recommendations through visualizations, reports, and presentations.8
Depending on your interests and data science skills, you might be more partial to science jobs instead of analytical ones. For example, there are a lot of data science soft skills that are required for the titles listed below, but analysts must be especially good at communicating, presenting, and receiving feedback. With that in mind, it’s important to note that there will likely be a little bit of each title in whatever data science career path you take.
Job Titles & Average Salaries
Here is a sampling of career opportunities in data science, along with projected job growth and average salaries.
- Job growth: 35%
- Average salary: $137,3655
Information security analyst
- Job growth: 32%
- Average salary: $112,0009
- Job growth: 15%10
- Average salary: $210,63711
- Job growth: 25%
- Average salary: $124,20012
- Job growth: 8%13
- Average salary: $95,02814
Define Your Future in Data Science
With a global market of $273.4 billion for big data by 2026, the demand for data science expertise is staggering.15 If you want to learn more about the current state and future of data science careers, download “Diving Into Data Science: Skills, Careers, Trends, and More” from New York Institute of Technology.
Don’t let these lucrative career opportunities for data science pass you by. Earning an Online Data Science, M.S. will put you ahead of the competition in this rapidly growing field, especially as the capabilities for AI tools, machine learning algorithms, and cybersecurity platforms grow.
Explore the Online Data Science, M.S. curriculum at New York Institute of Technology to preview what topics you would develop an expert understanding in, and review the admissions requirements to see how to get started.
- Retrieved on September 26, 2023, from https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
- Retrieved on September 26, 2023, from https://aws.amazon.com/what-is/data-science/
- Retrieved on September 26, 2023, from https://www.investopedia.com/terms/d/data-science.asp
- Retrieved on September 26, 2023, from https://databasetown.com/a-brief-history-of-data-science/
- Retrieved on September 26, 2023, from https://www.bls.gov/ooh/math/data-scientists.htm
- Retrieved on September 26, 2023, from https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
- Retrieved on September 26, 2023, from https://fortune.com/education/articles/how-much-do-grads-with-a-data-science-degree-make/
- Retrieved on September 26, 2023, from https://uk.indeed.com/career-advice/finding-a-job/data-analyst-vs-data-scientist
- Retrieved on September 26, 2023, from https://www.bls.gov/ooh/computer-and-information-technology/information-security-analysts.htm
- Retrieved on September 26, 2023, from https://www.bls.gov/ooh/management/computer-and-information-systems-managers.htm#tab-1
- Retrieved on September 26, 2023, from https://www.glassdoor.com/Salaries/it-director-salary-SRCH_KO0,11.htm
- Retrieved on September 26, 2023, from https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm
- Retrieved on September 26, 2023, from https://www.bls.gov/ooh/computer-and-information-technology/database-administrators.htm#:~:text=in%20May%202022.-,Job%20Outlook,on%20average%2C%20over%20the%20decade.
- Retrieved on September 26, 2023, from https://www.indeed.com/career/database-administrator/salaries
- Retrieved on September 26, 2023, from https://www.marketsandmarkets.com/Market-Reports/big-data-market-1068.html