Data Analyst vs. Data Scientist: Comparing CareersData Analyst vs. Data Scientist: Comparing CareersData Analyst vs. Data Scientist: Comparing Careers

Students interested in gathering, organizing, interpreting, and using data have many potential career options. These opportunities are only expanding as companies and organizations increasingly rely on information gathering and data analysis to make decisions. Data scientist and data analyst are two rewarding and diverse potential career paths in this field.

While they share a number of common characteristics, there are also differences between the two. Both are valuable, especially in today’s data-driven world, but they play to different strengths and offer different career trajectories.

Understanding the similarities and differences between a data analyst versus a data scientist is helpful for students who are considering advanced studies in data science, allowing them to choose the program that best fits their needs.

In assessing these two career trajectories, it’s important to take into account the different aspects of the data analyst versus data scientist roles and responsibilities.

Both data scientists and data analysts focus on analyzing data. However, there are also distinct nuances between the two. A data scientist’s primary duty is to collect data, and these individuals tend to be more involved in programming machines and optimizing systems. On the other hand, a data analyst focuses more on organizing data to identify trends, curate meaningful insights, and communicate the implications of collected data.

Data scientists often spend their time improving the effectiveness of programs that collect and analyze data. With a strong background in computer programming, machine learning, data mining, and deep learning, those who pursue a career in data science are often responsible for the machines that store and interpret information. Effective data scientists are skilled in a variety of programming languages, as well as statistical analysis and data interpretation.

Data analysts tend to spend a great deal of their work hours searching data for useful insights and trends. They then communicate what they’ve learned to company stakeholders, along with recommendations for moving forward. This allows businesses to address problems in their industry, find missed opportunities for growth, and develop data-driven business plans.

Additionally, the capacity to parse data and find the underlying trends requires a strong mathematical foundation, analytical skills, and the ability to confidently communicate findings and make recommendations. Data privacy and security are also key aspects of this role.

Those pursuing a job in either data science or data analysis will need to earn a bachelor’s degree in a field related to data science or business. They will then likely consider one of two advanced degrees: a Master of Science in Data Science or a Master of Science in Data Analytics. Both degree programs offer potential paths toward working with data in powerful ways. That said, each program presents its own focus and opportunities.

Students interested in pursuing a career as a data scientist — with a stronger emphasis on computer programming, coding, cloud computing, and machine learning — could benefit from earning an advanced degree. Master’s in data science programs, such as Maryville University’s online Master of Science in Data Science, focus on developing students’ proficiency in high-demand skills.

Through the program’s curriculum, students encounter various coding languages, such as Python, SQL, SAS, and R. Participants also select elective courses that support specific areas of interest in the data science field. In addition, the project-based nature of a data science degree allows students to learn through action. As a result, graduates gain real-world experience and confidence in their skills, allowing them to seek data scientist positions upon graduation.

Those who want to learn how to take complex data sets and use them to identify trends, share information, make decisions and recommendations, and predict outcomes tend to pursue a master’s degree in data analytics. Through the fully online Master of Science in Data Analytics program offered at Maryville University, students learn how to tell the story behind the data.

This program dives into topics like data analytics, data visualization, data mining, and forecasting. Through project-based coursework, students create charts and graphs that communicate findings, use statistical analysis to create predictive models, and draw insights and inferences from data sets. This generally prepares students for a data analyst role after graduation.

Individuals drawn to this field will want to be aware of salary expectations. Consider the following typical data analyst vs. data scientist salary ranges:

The U.S. Bureau of Labor Statistics (BLS) reports salary data for the operations research analyst role, a data analyst position. According to the BLS, the median annual salary for this role was $82,360 as of May 2021. Those who earn in the upper 10% of this field can expect a salary closer to $160,850 annually, while the lowest 10% of earners can expect an annual salary of about $48,690.

The BLS projects a job growth rate of 23% for operations research analysts between 2021 and 2031, a rate that’s much faster than the average for all professions.

According to BLS data, the median annual salary for the data scientist role was $100,910 in May 2021. Earners in the top 10% can expect a salary of $167,040 annually, while those in the lowest 10% earned $59,430.

Additionally, the BLS projects job growth for data scientists to increase at a rate of 36% between 2021 and 2031. This represents a growth rate that’s much faster than the average for all professions.

Maryville’s data analytics program provides the opportunity to hone a wide range of practical skills that can lead to long-term career success, including business intelligence, data collection, dashboard and analytics, and data modeling skills.

The data science program is similarly career-focused, offering a robust blend of skills in data mining, machine learning, big data, and beyond. Both programs can open the door to exciting and sustainable careers in an increasingly data-focused world.

Whether weighing a future as a data scientist or data analyst, it’s clear that the era of big data is here to stay, and those who can effectively manage information continue to be in demand. In a world dominated by data, an online Master of Science in Data Analytics or Master of Science in Data Science from Maryville can support graduates in securing a rewarding career.

5 Types of Programming Languages for Data Science

What Is Data Wrangling?

Predictive Analytics in Insurance: Types, Tools, and the Future

BuiltIn, “Data Analyst vs. Data Scientist: Similarities and Differences Explained”

Dataquest, “What’s the Difference Between a Data Analyst, Scientist and Engineer?”

U.S. Bureau of Labor Statistics, Data Scientists

U.S. Bureau of Labor Statistics, Operations Research Analyst