Data Science vs. Data Analytics: Differences and Similarities
In 2025, research and advisory firm Gartner released a forecast for how the business landscape is expected to change in the coming years based on analyzing current trends. They predicted significant changes related to the use of artificial intelligence (AI), including that 50% of business decisions will be augmented or automated by AI agents by 2027, 25% of supply chain key performance indicators (KPIs) will be driven by AI models by 2028, and 10% of global organizations’ boards will use AI guidance and recommendations to challenge executive decisions.
We are in the midst of an AI revolution, and AI models are created using vast amounts of data. The work of data science and data analytics professionals is directly contributing to these models and is helping to drive the revolution. Through a Master of Science in Data Science and Analytics program, individuals can prepare not only for this changing landscape but also to be a part of the change themselves.
What Is Data Science?
The data science and data analytics disciplines overlap, with data science being the larger umbrella term and data analytics falling under that umbrella. Data science professionals are responsible for a wide variety of tasks, which include finding patterns in datasets, training machine learning models and artificial intelligence platforms, and creating frameworks using data to forecast outcomes, solve problems, and automate processes.
What Is Data Analytics?
Data analytics is a subsection of data science that is focused on contextualizing, interpreting, and visualizing datasets. Data analysts work on four types of analytics: predictive, which identifies trends to forecast future events; prescriptive, which uses forecasts to make recommendations; diagnostic, which identifies the reasons why something happened; and descriptive, which looks at the quantitative and qualitative properties of datasets.
Comparing Occupations: Data Science vs. Data Analytics
While data science and data analytics professionals share many similarities, as data science essentially encompasses data analytics, there are notable differences between their two occupational fields. Data science and data analytics professionals may follow similar education paths, but they generally need to acquire distinct skill sets.
Career Paths
Data scientists work in a variety of settings and often have a specialty. A data scientist with a strong engineering background may work on building machine learning algorithms, whereas another data scientist with a background in research may work on studies for an academic journal.
The median annual salary for data scientists was $108,020 as of 2023, according to the U.S. Bureau of Labor Statistics (BLS). The field is experiencing tremendous growth, with the BLS projecting a 36% increase in positions for data scientists between 2023 and 2033, which far outpaces the national average job growth projection of 4%.
Many professionals who work in data analytics are called data analysts, but others have different titles, depending on their industry and their focus. For instance, a data analyst working to help organizations identify and solve problems and improve operations might be called an operations research analyst. According to the BLS, the median annual salary for operations research analysts as of 2023 was $83,640. Operations research analysts are also experiencing significant growth and are expected to see a 23% increase in jobs between 2023 and 2033.
Educational Requirements
Both data scientists and data analysts typically need a solid educational background in mathematics. Although these positions both require a bachelor’s degree, many employers prefer applicants with a master’s degree. Common fields of study for professionals in both areas include data science, computer science, mathematics, and statistics.
Necessary Skills
Data scientists are truly scientists, which is to say that their work typically follows the scientific method. They form and test hypotheses to see if a desired outcome is possible using available data. This experimental process requires skills in data mining, cleaning, and exploration, as well as in feature engineering, predictive modeling, and data visualization.
They also typically need to have technical skills, including the ability to write and decipher programming languages, and experience working with unstructured data and in SQL database coding.
Data analysts also work in data exploration and data visualization, but unlike data scientists, these two tasks are typically their primary focus. Depending on where data analysts work, they’ll use different tools to sort, calculate, visualize, and interpret the data they are working on.
Data analysts are often called in to address why something occurred, such as why a marketing campaign failed or why a particular feature has a notably low adoption rate in a software application. As their name implies, data analysts must be analytical thinkers who have the ability to dive into data and use it to answer questions.
Prepare for a Data Science or Data Analytics Career with NEC
Professionals in both data science and data analytics play a vital role in helping organizations understand data and use it to drive decision-making. Using the work of data scientists, technology innovators can build tools like AI models that promote automation and growth. And through the work of data analysts, organizations can unpack their data to turn it into actionable insights.
New England College’s Master of Science (MS) in Data Science and Analytics program can help individuals prepare for a career in either field. Students learn to look at data critically, contextualize it, and apply the knowledge they gain to real-world settings. The on-campus program offers the core curriculum necessary for data scientist and data analyst roles, with courses covering database design, information visualization, and data mining.
Students also choose electives that allow them to focus on data science or data analytics, picking from classes on subjects that include Python and Java, information security, and information technology (IT) project management.
Being on campus allows students to take advantage of NEC’s many additional learning and engagement opportunities, including its clubs, networking opportunities, internships, and professor-led trips.
Prepare to play your part in the digital data revolution with a degree from NEC.
Learn More
Apply to NEC
Explore Academic Degree Programs
Read More NEC News
