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For those interested in becoming a data analyst or data scientist, it’s essential to understand the differences between these two related but distinct careers. Companies investing in data science vs. data analytics should evaluate their needs, as each discipline offers distinct advantages for business intelligence.

What are the primary differences between data science vs. data analytics?

When considering the differences between data science vs. data analytics, consider the sought-after outcome for the data being worked with. Data science focuses on collecting and shaping raw data via modeling techniques and processes. Data analytics focuses on identifying patterns and trends that lead to problem-solving or predictive insights. The debate over data science vs. data analytics often centers on the scope of the two fields—the former focuses on predictive modeling, while the latter emphasizes historical trends.

Data analytics

Data analytics refers to , patterns or other evidence that can help an organization solve a particular problem, increase operational efficiency, save money or reach some other goal. Analytics projects often require communicating findings to the decision-makers in a company or organization. 

Data analysts often need to develop charts and other visuals that communicate and support their findings. 

Companies use the findings of analysts to make informed strategic decisions, manage risks, develop budget forecasts and better define targets of marketing campaigns. 

When considering differences between data science vs. data analytics, consider that data analysts try to find answers to questions in large data sets, but the approach can differ depending on the project. 

Here’s a look at the four primary types of data analytics:  

• Descriptive analytics answer questions about what happened in a specific situation by looking at the data surrounding the occurrence. Analysts use this information to explain outcomes, often related to key performance indicators (KPIs), which measure progress toward a final goal.
• Diagnostic analytics complements descriptive analytics and helps analysts understand why an event happened the way it did. This type of analytics digs deeper into the findings of the descriptive analysis to discover the reasons behind an event. For instance, diagnostic analytics may help a company understand why specific KPIs improved or declined. 
• Predictive analytics helps forecast what could happen in the future. This type of analysis uses historical data, including past trends, to identify patterns that are likely to continue. 
• Prescriptive analytics helps answer questions about what’s needed to solve an issue or reach a goal. This type of analytics aims to identify specific actions an organization can take to achieve future goals or objectives. 

When applying for a job as a data analyst, it can be helpful to be proficient in all four types of analysis.

Organizations and businesses use data analytics for a wide range of purposes. Here are some of the most common applications for data analytics. 

• Risk management: Through research, an analyst identifies possible risks (financial, cybercriminal and industrial) a business faces and makes cost-effective recommendations to help manage them. 
• Budgeting and forecasting: Through predictive analysis based on an organization’s historical information, analysts provide insights to help a company set realistic goals and budgets while identifying areas for improvement.  
• Marketing and sales: Descriptive and diagnostic data analysis helps businesses identify and track useful marketing and sales metrics, such as conversion rates. Using historical data from past campaigns, analysts find areas for improvement.  
• Product development: Data analysts help businesses develop better products by using data analytics to assess trends in their industry and define consumer needs. 

Those are some of the most common applications for data analytics. However, companies rely on data analysts for other purposes as well. For those working in the field of data analytics, focus may vary depending on employer needs and industry. 

The specific duties of data analysts depend on the type of organization they work for and the extent to which the organization adopts data-driven strategies and decision-making. Key responsibilities of data analysts include:

• Using statistical tools to interpret data while paying attention to trends and patterns to glean valuable insights. 
• Mining data from primary and secondary sources and reorganizing it into formats that are useful for the analysis project.
• Designing and maintaining data systems and databases, including fixing data problems such as coding errors.
• Preparing reports that offer data-supported insights and communicate trends, patterns and predictions.
• Collaborating with other IT professionals in an organization (such as programmers and engineers) to identify opportunities for process improvements, recommend system modifications and develop data-management practices.

Data analysts present insights that can affect the high-level decisions of a company. This role and the responsibilities require a solid educational foundation in technology as well as certain skills. Those interested in a career in data analytics can pursue a technology degree to gain the right knowledge. 

BLS notes that because few schools offer degrees in data analytics, students may want to consider similar degree programs in related fields of specialization, such as cybersecurity. 

Data science

Data science involves the design and creation of data modeling techniques and processes. The goal of a data scientist is to collect and organize information into forms that are useful for analysis or other purposes.

The data science toolbox includes algorithms and mathematical models that analyze and interpret data sets automatically. The goal of data scientists is to use these tools to create data-driven solutions for businesses or organizations. 

In other words, data scientists lay the groundwork for all the different types of data analysis and data usage within a company or organization. 

In the context of data science vs. data analytics, consider that data science is a broad and developing field, and data scientists can focus on any of the following specialties: 

• Data mining involves the analysis of large sets of data to produce meaningful information. Data miners often create mathematical models that find and define patterns, trends and correlations in raw data. 
• Data engineering focuses on building and maintaining frameworks that transform data into useful formats. This specialty involves setting up database systems for collecting and organizing data from different places into one usable collection.
• Data architecture involves designing, creating and managing a complete digital framework for an organization’s data. Data architects often create the larger system in which specialist data scientists work. 
• Machine learning involves using data to improve the performance of computer-driven systems. This data science specialty focuses on creating algorithms that allow computers to interpret data and adjust operations according to the new information. 

Regardless of their specialty, data scientists need to develop knowledge of both mathematics and computer systems. 

Data science has a wide range of applications, and data scientists can specialize in a variety of areas, depending on the needs of their employer. That said, data scientists always focus on facilitating data-driven activities for their employers or clients. Here are some common foci in the field of data science: 

• Data wrangling involves sourcing, cleaning and structuring data into various data sets that are accessible and ready for analysis. 
• Statistical modeling involves running the data through a set of mathematical equations to identify relationships, make predictions or assess performance. For example, regression and clustering models identify relationships between variables in the data and help put information in a useful, visualized format. 
• Programming involves creating computer programs that can function with databases to access, organize and filter information. Data scientists use languages such as SQL and Python when programming tools for this purpose. 

The duties of data scientists depend on their area of focus. Data scientists can work, for example, in the finance or medical industries or at academic institutions. As more companies adopt data-driven operations, these professionals are in high demand. 

Daily duties of data scientists depend on their employer’s needs, industry and reliance on data analysis. 

Here are some typical duties that data scientists may need to handle daily: 

• Developing custom mathematical models and algorithms to apply to the analysis of data sets.
• Measuring the usefulness of newly discovered data and recently deployed data discovery algorithms.
• Creating systems to mine and analyze data to aid operational efficiency, product development, and marketing and business strategies for their employer.
• Developing and using predictive modeling to solve problems and guide future operational decisions.
• Coordinating with different analytics and IT personnel to implement models and monitor outcomes.

Data professionals should consider training that teaches them how to handle both the technical and analytical aspects of their job. 

Specific degrees can help students enhance their preparation for both data science and data analytics careers. They can start on a career path by enrolling in a Bachelor of Science in Data Science degree program, which focuses on data mining and modeling, data programming languages, statistical analysis and related subjects. 

While most data scientist entry-level positions require only a bachelor’s degree, qualified candidates are in high demand. If leadership in technology is a career path of interest, a Master's in Information Systems may be beneficial. 

This answer depends on interests. For someone who likes developing algorithms and models to manage or interpret data, data science may be a better fit. Those who prefer evaluating data to answer questions or solve problems may fit better with analytics. Choosing between data science vs. data analytics depends on career goals, technical skills, and the type of data-driven solutions required.

As of May 2024, data scientists earned between , according to BLS. Unfortunately, BLS does not currently show salary data for the job title of data analyst for comparison.

The salary ranges are not specific to students or graduates of ��ɫ��Ƶ. Actual outcomes vary based on multiple factors, including prior work experience, geographic location and other factors specific to the individual. ��ɫ��Ƶ does not guarantee employment, salary level or career advancement. BLS data is geographically based. Information for a specific state/city can be researched on the BLS website.

Curious to learn more about data science vs. data analytics? ��ɫ��Ƶ offers online programs, including a data science bachelor degree online and an online data science master’s degree.

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