Guide to Data Science vs. Data Analytics vs. Big Data
By Indeed Editorial Team
Published November 12, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Many companies continually look for ways to collect, sort, evaluate, and extract meaning from their vast amounts of business data. Although these companies may sometimes use words like data science, data analytics, and big data interchangeably, there are actually several key distinctions between all three. Learning more about both the commonalities and differences between data analytics, big data, and data science can help you more effectively gather, assess, and leverage data findings in your role.
In this article, we discuss the distinctions between data science vs. data analytics vs. big data, review the applications of each, and explore the skills needed to work in each.
Data science vs. data analytics vs. big data
Here are the primary differences between data science vs. data analytics vs. big data:
While data analytics, data science, and big data have similarities, each one has a unique definition. Here are the meanings of each:
Big data: Big data is a data set with many values collected from an array of places.
Data science: Data science is a field that combines subjects such as statistics, machine learning, and scientific principles. Data science can help businesses extract meaning from both unstructured and structured sets of data.
Data analytics: Data analytics refers to a specific set of digital tools and software programs that can collect and evaluate big data. Professionals can then use the big data analyzed by these tools to help them make key business decisions.
Data analytics, big data, and data science all have different characteristics. The defining characteristic of big data is how much data it contains, a trait that is also called data volume. Big data also typically contains a range of types of data sets. For example, an organization might use media outlets, user search engine terms, and social media trends to collect its big data. In addition, digital tools often generate big data at a rapid pace based on real-time results.
By contrast, neither data science nor data analytics refer to specific data sets. Instead, data science and data analytics refer to tools and techniques that professionals can use to evaluate diverse sets of data, including big data.
Professionals interested in pursuing a career related to data may be interested in knowing that the career options can vary depending on whether they're more inclined toward data analytics, big data, or data science.
Those who prefer big data, for example, might end up in jobs related to data warehouses or management, such as data warehouse analyst, database developer, or portal administrator. Professionals primarily interested in data analytics, by contrast, may become data architects or analysts. In comparison, professionals drawn to data science often end up in roles that combine scientific techniques with technology skills, like data developers and data business analysts.
Applications of data science vs. data analytics vs. big data
Big data, data science, and data analytics also differ in their applications, meaning the fields or professional responsibilities they can affect. Here's more information about the diverse applications of these three concepts:
Applications of data analytics
Companies in the following industries often use data analytics:
Health care: Hospitals and other health care facilities can use data analytics to optimize processes related to patient care treatments, medical equipment, and lengths of patient stays. This information can help medical facilities determine how to process the highest number of patients while still providing high-quality care.
Energy management: Energy businesses may use data analytics to control or keep track of dispatch crews, network devices, and potential or current service outages. Data analytics can help these organizations with smart-grid management, energy distribution, energy optimization, and building automation.
Travel: Many travel websites use data analytics to offer users results that they're more likely to prefer. For example, a website that helps travellers find accommodation might display results based on what their data analytics software predicts that customer might enjoy most at a hotel or cabin.
Gaming: Video games sometimes have data analytics software programmed into them. This software can help the game optimize its resources while in use.
Applications of big data
Potential applications of big data include:
Finance: Financial institutions often use big data for a range of processes, including ensuring customer satisfaction and detecting fraud. For instance, the large sets of data that big data supplies can provide financial professionals with clues about what type of irregular data sets might suggest potential fraud.
Communications: Telecommunications providers typically invest lots of money in both gaining and retaining their clients. Big data can help these providers learn more about what types of services their customers prefer at various pricing points.
Retail: Professionals at retail shops spend lots of time trying to understand their customer base. Big data can offer retail businesses insights into customer transactions, feedback, store credit card usage, and loyalty program memberships.
Applications of data science
Here are some of the online functions and tools that data science can support:
Search engines: Online search engines use powerful data algorithms to determine what results to show end-users. These algorithms typically take into account an array of factors, including a user's personal preferences and the results that received the most traffic from similar searches.
Digital recommendations: Many websites use recommendation systems powered by data science. For instance, a video streaming service that knows you enjoy watching comedy films might recommend other comedic movies based on what you've watched in the past.
Digital advertisements: Digital ad systems also use data science algorithms to determine with which ads a user might feel most inclined to interact.
Skills of data science vs. data analytics vs. big data
If you're interested in pursuing a career related to data, it's important to understand the various skill sets involved in data analytics, big data, and data science. Professionals who specialize in any of these three subjects often share similar soft skills. Soft skills are personality traits and behavioural habits that can influence your professional conduct, such as organization and adaptability. Other soft skills that professionals in all three disciplines can benefit from developing include:
willingness to learn
attention to detail
Here are some of the unique skill sets that professionals interested in pursuing either data analytics, big data, or data science might want to develop:
Skills in data science
Skills that can benefit professionals in the field of data science include:
Analytical abilities: Data scientists are highly proficient at evaluating data and using their assessments to develop strategic plans.
Software development: A data scientists may contribute to the software development lifecycle. These professionals might troubleshoot, manage source code, or test software at various stages of development.
Unstructured data management: Unstructured data refers to data sets that don't have any inherent numerical values, such as images or videos. Data scientists excel at creating programs that can effectively organize and evaluate these types of data.
Skills of data analytics
If you're interested in a career related to data analytics, the following skills may prove useful:
Critical thinking: Data analysts use their critical thinking abilities to determine the best ways to organize and evaluate diverse data sets. Critical thinking skills that data analysts can benefit from developing include problem solving, inference, research, and curiosity.
Business intelligence: As data analysts often collaborate with business executives, it's important for them to understand how to extract meaning from data sets that can provide value to company leaders. This may involve familiarity with business terminology, a company's current goals, market conditions, or the business's target audience.
Data visualization: Data visualization refers to different methods of presenting data set with a visually useful layout, such as through charts or infographics. Understanding data visualization techniques can help professionals in data analytics explain their findings to those in other fields.
Skills of big data
Following are some abilities that can prove advantageous to big data professionals:
Data management: One of a big data professional's primary responsibilities is to collect, sort, and handle complex or large sets of data. This involves in-depth familiarity with data warehouses and life cycles.
Programming: Big data professionals typically are highly proficient in one or more programming languages. They may also know how to work with various types of coding databases.
Creativity: Creative thinking abilities may help professionals in big data think of innovative ways to gather, organize, and store disparate sets of data. Creativity may also help big data professionals find significant patterns within the data sets.
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