Greenville, South Carolina, has emerged as a rising tech hub in the Southeast, making it an ideal location for data professionals. With major corporations like Hubbell Lighting, Michelin North America, and BMW Manufacturing Co. headquartered in the Upstate, plus a growing startup ecosystem, the demand for data talent continues to surge.
Greenville is home to over 40 Fortune 500 companies and 20+ headquartered operations, with more than 240 international companies. Greenville’s pro-business environment, quality workforce, and market accessibility make it marketable to companies across the world. From companies like BMW Manufacturing Co. and Fluor Corporation to healthcare leaders like Prisma Health, diverse industries across the Upstate are investing heavily in data-driven decision-making.
According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 36% from 2023 to 2033, making it one of the fastest-growing occupations. Also, data science jobs are projected to be one of South Carolina’s fastest-growing occupations. This growth trajectory, combined with South Carolina's thriving tech ecosystem and growing job market, makes Greenville an attractive destination for data professionals at all career levels.
Learning the ins and outs of a data science career can feel overwhelming. We hope this guide helps you sparse out the differences between a data scientist and data analyst, so you can decide which career route is the best fit for you.
Data Scientist vs. Data Analyst: What They Do
As a business grows and larger quantities of data become available, many companies are left with the difficult task of finding practical and scalable ways to interpret data in a meaningful way. This lack of insight can lead to missed opportunities.
This is why data analysts and data scientists are so important. They help companies identify actionable insights and make data-driven business decisions. Though both data analysts and data scientists offer their expertise to help companies uncover key insights for a company's greater good, their day-to-day routines look a bit different.
What Does a Data Analyst Do?
Data analysts retrieve and gather data and organize that data to reach meaningful conclusions.
The insights data analysts bring to a company can be invaluable in the South Carolina market.
- A data analyst's primary role is to scan and analyze data. They give explanations, reports, and visualizations to show insights determined from the data. Data analysts use their expertise to help people companywide understand data through visualization.
- A data analyst uses statistical tools to interpret data sets and analyze trends and patterns that are valuable for diagnostic and predictive analysis.
- Becoming a data analyst also means you must be proficient at extracting and processing data using SQL and business intelligence software. You should also be an expert in programs like Excel and Tableau to maintain metrics dashboards.
Analysts have been around well before big data, which is why data analyst roles are specific and well understood. They need to be good communicators because they work with different departments and need strong presentation/visualization skills. Data analysts don't always need expert coding skills but usually have experience with analytics software, data visualization, and data management programs.
An analyst is a storyteller and a prophet finding truths in data. For example, you can see the work of a data analyst in the form of a marketing report projecting future sales of a product or the success of an ad campaign.
Some data analysts do use code in their day-to-day duties, but it's typically not required or requires only a basic understanding to help clean and normalize a company's data. Learning programming languages is usually in a data scientist's purview, and advanced coding skills aren't critical to an analyst's success. Even so, it's not rare for an analyst to have Python, SQL, or R proficiency.
What Does a Data Scientist Do?
A data scientist is someone who collects, cleans, and explains data. The primary role of a data scientist is to tweak and adjust the statistical and mathematical models applied to acquired data. Perhaps more than anything, data scientists make discoveries while they swim in data.
Data scientists love to use data to navigate the world around them and find solutions. In the competitive Greenville business landscape, data scientists are vital to help local decision-makers shift from ad hoc analysis to an ongoing conversation with data.
- Data scientists are responsible for translating problems into workable questions. They also build predictive models.
- A good data scientist knows how to theorize, implement, and communicate the acquired data effectively.
- Data scientists are on a quest for novel solutions and discoveries. Data scientists communicate what they're learning and suggest its implications for new business directions.
- Data scientists are creative in displaying their information and discovering ways to make their findings more clear and compelling.
A data scientist interprets data, much like a data analyst, but can code models or algorithms to gain even more insight into that data.
Let's use Spotify to highlight the data analyst vs. data scientist difference:
Spotify, with its 600+ million users globally, is a great example of how companies distinguish between data analysts and data scientists in real-world applications.
Data analysts at Spotify focus on evaluating user behavior data, including what people like and how they engage with the product. They conduct hands-on user research, analyze data to inform product development, and deliver actionable insights by understanding the ecosystem of listeners, content, and creators. For example, a data analyst might generate reports that show streaming trends by region or track how user engagement shifts. These insights help teams make critical product and strategy decisions.
Data scientists at Spotify, on the other hand, work specifically in a product data science model, collaborating closely with product managers and engineers to design experiments, shape the product roadmap, and guide business decisions using data insights. They also focus on experiments and A/B testing, applying statistical methods and causal inference techniques to identify causal effects, especially when running experiments is infeasible.
Data Scientist vs. Data Analyst: Role Requirements
The role requirements for data analysts are as follows:
- Data analysts usually have STEM bachelor's degrees or have graduated from a data program. They aren't, on the other hand, required to have an advanced degree.
- Data analysts are also not required to have advanced coding skills. Instead, they should have experience using analytics software, data visualization software, and data management programs.
- As with most data careers, data analysts must have strong skills in mathematics and predictive analysis.
- Data analysts need to be organized and detail-oriented.
- Data analysts should have the ability to deal with ambiguity and competing objectives in a fast-paced environment.
- Data analysts need to be effective communicators with strong analytical and technical skills, reporting acumen, creativity, teamwork, and intellectual curiosity.
The role requirements for a data scientist include:
- Data scientists often have advanced degrees, and are experts in math, statistics, or computer science.
- Data scientists should have experience in data querying languages like SQL, scripting languages like R, Python, or Java, or statistical/mathematical software like Weka, SAS, Hadoop, or Matlab.
- Data scientists should possess depth and breadth in quantitative knowledge and have excellent quantitative modeling, statistical and analytical skills, and problem-solving skills.
- Data scientists' most essential and universal skill (and the one that sets them the most apart from data analysts) is the ability to write code. As the data scientist interprets data, they can use code to build models or algorithms that will help them gain even more insight into the data.
- Similar to data analysts, but even more true, data scientists must be able to communicate in a language that all of their stakeholders will understand.
- Data scientists should know statistical computer languages including R, Python, and SQL and have at least three years of experience working with them, as well as experience working with and creating data architecture. They also need to be able to manipulate data sets and build statistical models.
- Data scientists should also possess an intense curiosity that pushes them to scratch much deeper than the surface of a problem and find answers, then distill the answers into a clear set of hypotheses that can be tested.
Data Scientist vs. Data Analyst: How Much Do They Earn in Greenville, SC?
How much does a data analyst make?
As the volume of data generated by businesses continues to grow, so does the need for skilled data analysts who can extract meaningful insights from it. Organizations across industries rely on data analysts to interpret complex datasets, identify trends, and inform key business decisions. Because of this crucial role and the technical expertise it requires, data analysts are often well-compensated. According to ZipRecruiter, the average salary for a data analyst in Greenville, SC, is $77,706 (August 2025).
How much does a data scientist make?
The high earning potential in data science stems from a unique blend of factors: the specialized technical skills required, a shortage of qualified data professionals, and the significant influence data-driven insights have on strategic business decisions. As organizations increasingly rely on data to optimize operations, predict trends, and drive innovation, the demand for skilled data scientists continues to rise, making it one of the most valuable and well-compensated roles in today’s economy. According to ZipRecruiter, the average salary for data scientists in Greenville, SC, is $115,410 (August 2025).
Business Analyst vs. Data Scientist vs. Data Analyst
When we talk about data and data-related careers, there's a third role that commonly gets grouped in with data scientists and analysts: the business analyst. What's the difference between the three roles?
- Data Analyst: Data analysts are the gatekeepers over an organization's data, helping stakeholders understand the data and surfacing meaningful insights.
- Business Analyst: Business analysts focus on using information discovered by data analysts to identify problems and find solutions for the business.
- Data Scientist: Data scientists might use the data visualizations the data analysts create, but they'll take it up a notch by sifting through data to find weaknesses, trends, and opportunities for their organization.
Top Greenville Companies Hiring Data Scientists and Analysts
- BMW Manufacturing: As the largest BMW plant in the world, BMW regularly hires data scientists to enhance production system and identify inefficiencies in the production process.
- Michelin North America: The tire manufacturer's North American headquarters uses data analytics for everything from optimizing supply chains and improving customer experience.
- Prisma Health: The largest healthcare system in South Carolina uses data analytics to enhance patient care, improve operational efficiency, and drive research and innovation.
Choosing Between a Data Analytics and a Data Science Career
Data analyst and data scientist careers are both in demand in a field that continues to grow. In fact, data scientists rank #4 in Best Technology Jobs, and the global data analytics market is expected to grow from $7.03 billion in 2023 to a staggering $303.4 billion by 2030.
When deciding between a career as a data analyst or a data scientist, there are few things to keep in mind, including:
Personal background
Data science and data analysis have their own unique sets of requirements. If you're choosing between the two, you should consider your background, education, work experience, and other pertinent factors to see which career aligns best with your skills and future goals. Analysis is typically less senior and requires fewer technical skills than data science, so it largely depends on how interested you are in learning to code.
Your interests
If you love numbers, programming, and statistics, you will love being a data analyst. If you're advanced in math, statistics, or computer science and knowledge of the business world, you may be better aligned with a data scientist career.
Your desired salary and career path
As with any career in Greenville, your potential salary and career path are essential factors when deciding between a data analyst or data scientist career. Since the level of experience and education requirements are different for data scientists and data analysts, compensation differs, too.
Keep in mind that even if you decide to start as a data analyst, you can become a data scientist with a few additional programming skills since the roles have a decent amount of overlap. The key difference between a data analyst and a data scientist is the required coding experience.
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