Although you may see the following terms used differently by professionals across data analytics, the distinction between data science, data analytics, and data analysis will be presented consistently throughout your course experience at SNHU. Upon completing this course, you will be equipped with a fundamental, yet essential, understanding of each term.
Data science is a research-oriented field of data discovery that includes data sourcing, experimental design, statistical modeling, hypothesis testing, prototype development, and advanced data analysis to gain deeper insights.
Data analytics is the broader process of using data sources, tools, and environments to derive insights using the data analytics lifecycle (DAL). The DAL is an iterative cycle used by data analysts and data scientists and is commonly presented in six phases, or stages. The six stages are data discovery, data preparation, model planning, model building, communicating results, and operationalizing the models. The purpose of the DAL is to inform and support decision making.
Data analysis is a set of specific methods that are used to prepare, profile, and visualize data from existing data sources. These methods involve inspecting, cleaning, transforming, and modeling data into information that supports decision making.
Data analytics can lead to a wide variety of careers in many disciplines. Check out this guide from SNHU Career to learn more about opportunities in data analytics. As you read, consider what type of organization might provide opportunities to move into these more specialized areas.
Choose one of the entry level: data analytics and business careers from the list and click Learn More. As you read about the type of career, take notice of the knowledge, skills, and dispositions that employers seek in candidates for this type of position.