As technology expands, so do career opportunities. One century ago, there was no need for data science. In the 21st century, it’s one of the fastest-growing fields in the tech industry. But what exactly is data science, and what role does it play in the business sphere? In this article, we’ll take a deeper look at the importance of data science, the responsibilities of data scientists and how you can get a job in this exciting career path.
While “science” might evoke images of scientists in lab coats holding microscopes and test tubes, data science doesn’t unfold in a laboratory. Yet, it’s no less systematic, and it takes a logic-oriented mind to grasp the principles of data analysis and engineering.
Simply put, data science studies data to extract meaningful insights. Given the vast amounts of data collected daily (some estimate that 328.77 million terabytes of data are generated daily), companies would drown under all that information if it wasn’t sorted through and studied. Furthermore, companies need someone to comb through and extrapolate insights from that raw information to see how it might impact business decisions.
Data science is the bridge between information and action. It evaluates data sets, draws conclusions from them and shows stakeholders how business models and strategies can be improved.
What Does a Data Scientist Do?
Within data science, a data scientist gathers, organizes, studies and concludes information from data sets. They see no stone is left unturned, using their statistical findings to build predictive models about future outcomes.
Data scientists use a systematic process to draw conclusions from their findings. First, they gather data through data acquisition and extraction. This is done utilizing ETL tools such as SQL or Excel.
Next, they organize that data through warehousing, staging and cleansing, where data goes from its raw form to a form from which information can be extracted.
After the data has been organized, it gets processed. Then, through data mining, clustering and modeling, data is examined for patterns and anomalies. These help data scientists draw conclusions for predictive analysis.
Once the data is gathered, cleaned and modeled, the analysis begins. Data scientists use specific analysis methods in this stage to make informed decisions.
Finally, it’s time to report the findings to the appropriate stakeholders. The data scientist now presents the results of the analysis, as well as the proposed solutions to the problem at hand. Once stakeholders have all the necessary information, they can decide on the best course of action.
What Language Is Used for Data Science?
Many careers in the tech industry today rely heavily on markup and programming languages. Data science is no different. There are dozens of programming languages for data science, each useful for a specific purpose, but four of the most common are R, Python, SQL and Java.
R: Great for statistical computing and data visualization, R is most commonly used for data mining. It’s an open-source language, making it available for user modification and distribution.
Python: Because of its robust libraries and tools, Python is heavily used for data analysis and machine learning. It’s versatile, easy to use and offers much to the field of data science.
SQL: This Structured Query Language is used for data retrieval, exploration, cleaning and visualization. It allows data scientists to access data in regional databases and enables them to work with various data sources.
Java: Perhaps less commonly used but still immensely useful, Java can handle large-scale data processing and allows data scientists to develop web applications useful for data analysis and modeling.
These tools have enormously impacted the tech industry and are invaluable for data scientists today. If you’re unfamiliar with these tools, data science bootcamps are an excellent resource for learning more from actual experts in the field rather than an unreliable Google search.
How Much Math Is Involved in Data Science?
Because data science relies heavily on statistics and mathematical models to interpret data, it does require a strong familiarity with Math, particularly these branches:
While math is essential to data science, it doesn’t mean a math degree is necessary. However, if you are pursuing a career in data science, it would be wise to brush up on your math skills. Educational bootcamps provide a great way to re-train yourself in those forgotten skills or learn the ones you never entirely understood.
Professions in Data Science
Careers in data science are on the rise, and the numbers reflect that with a predicted 36% growth in job opportunities by 2031. Google’s Chief Economist Dr. Hal R.Varian agrees, saying, “The ability to take data–to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it–that’s going to be a hugely important skill in the next decades.”
The field offers a wide range of career options. Some of the most common are:
Data analyst: Responsible for gathering, cleaning and studying data to give stakeholders better insights into where a company might be performing poorly and where they can improve. They help companies make informed, proactive decisions for best practices.
Business analyst: Similar to data analysts, business analysts look for trends and patterns in data, drawing conclusions based on their studies that can then be presented to stakeholders. As a result, they help businesses improve their efficiency and success.
Data engineer: Because data takes up so much storage space, data engineering is necessary for designing and maintaining the framework that supports the storage of vast amounts of data. Data engineers manage data pipelines and make sure data is accurate.
Data scientist: As we’ve learned, data scientists analyze data by utilizing statistical models and identifying patterns. Then, they help stakeholders understand the best decisions for the company.
Machine learning engineer: Because machine learning is so integral to the field of data science, engineers are necessary for designing, implementing and maintaining machine learning algorithms. Machine learning engineers work with a team to develop models for extracting and cleaning data.
As you can see, there are many career paths within data science. The field is creative and constantly evolving.
Salaries in Data Science
Across the field of data science, salaries are well-paying. Of course, amounts will vary depending on location and experience, but these are the average salaries, according to Glassdoor.
Data analyst: $70,306.
Business analyst: $82,130.
Data engineer: $114,979.
Data scientist: $126,393.
Machine learning engineer: $132,510.
Learn More About Data Science
Interested in learning how to break into the field of data science? The initial steps aren’t as hard as you think.
Step one: Sharpen your skills.
Step two: Build your portfolio.
Step three: Make connections.
An educational bootcamp can help you with all three. All on your own time, you can lay the groundwork for a successful career. Explore the Stony Brook University Data Science Bootcamp to see how you can get ahead of the curve in this exciting, innovative field.