Data Science vs. Big Data vs. Data Analytics

Data Science vs. Big Data vs. Data Analytics

Avantika Monnappa

Last updated February 8, 2018


Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate—in fact, more than 2.7 zettabytes of data exist in today’s digital universe, and that is projected to grow to 180 zettabytes in 2025.

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All this data—from your photos to the Fortune 500’s financials—has only recently begun to be analyzed to tease out insights that can help organizations improve their business. That’s why more organizations are seeking professionals who can make sense of all the data.

It is easy enough to become a data scientist. Once you get the art of data analysis right,  it is just a matter of practicing your newly-found skills well enough to become proficient.

Join our data science course and become proficient in the art of data analysis.

In this article, we’ll discuss what data science, big data, and data analytics are, recommended skills for each, and potential salaries.

Data Science

What is a data scientist? What do data scientists do? Data scientists combine statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently to find patterns, along with the activities of cleansing, preparing, and aligning the data.

Dealing with unstructured and structured data, Data Science is a field that encompasses anything related to data cleansing, preparation, and analysis. Put simply, Data Science is an umbrella term for techniques used when trying to extract insights and information from data.

Data Science with R

Education for Data Science Roles

Eighty-eight percent of Data Scientists have a Master’s Degree, and 46% have PhDs. Other skills data scientists need include:

  • In-depth knowledge of SAS and/or R. For Data Science, R is generally preferred.
  • Python coding: Python is the most common coding language that is used in data science along with Java, Perl, C/C++.
  • Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Experience in Hive or Pig is a huge plus.
  • SQL database/coding: Though NoSQL and Hadoop are the major focus for data scientists, preferred candidates can write and execute complex queries in SQL.
  • Working with unstructured data: It is extremely important that a Data Scientist is able to work with unstructured data—whether from social media, video feeds, audio, or other sources.

With these skills, projects the national average salary for a data scientist at more than $120,000.

[Related reading: Data Analyst vs Data Scientist - What's the Difference?]

Big Data

What is a big data analyst? According to Gartner, the definition of Big Data reads, “Big data is high-volume and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.” Big Data analytics find insights that help organizations make better business decisions.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates organizations of all sizes on a day-to-day basis. In other words, Big Data refers to humongous volumes of data that cannot be effectively processed with traditional applications. The processing of Big Data begins with the raw data that isn’t aggregated or organized—and is most often impossible to store in the memory of a single computer.

Education for Big Data Roles

For those seeking Big Data roles, you’ll need these:

  • Analytical skills: The ability to be able to make sense of the enormous amounts of data that you get. With analytical problem-solving abilities, you will be able to determine which data is relevant to your solution.
  • Creativity: You should have the ability to create new methods to gather, interpret, and analyze a data strategy.
  • Mathematics and statistical skills: Good, old-fashioned “number crunching” is absolutely necessary.
  • Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
  • Business skills: Big Data professionals should have an understanding of the business objectives that are in place, along with the underlying processes that drive the growth of the business as well as its profit.

Why do you want to become a big data analyst? These skills land Big Data engineers a national average of more than $102,000 per year, according to Glassdoor.

You too can join the high-earners' club. Enroll in our Data Science Masters program and earn more today.

Data Analytics

What is the role of a data analyst? Data Analytics is the science of examining raw data with the purpose of finding patterns and drawing conclusions about that information by applying an algorithmic or mechanical process to derive insights. According to Forbes, the big data analytics market will surpass $200 billion soon.

The work of a data analyst lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows; for example, running through a number of data sets to look for meaningful correlations between each other. Data Analytics is used in a number of industries to enable organizations to make better decisions as well as verify and disprove existing theories or models.

You can check the Course Preview of our Data Science Training with R here.

Education for Data Analytics Roles

Data Analytics roles typically require the following:

  • Programming skills: Knowing programming languages are R and Python are extremely important for any data analyst.
  • Statistical skills and mathematics: Descriptive and inferential statistics and experimental designs are also a must for data analysts.
  • Machine learning skills.
  • Data wrangling skills: The ability to map raw data and convert it into another format that allows for a more convenient consumption of the data.
  • Communication and Data Visualization skills.

With skills like these, even an entry level Data Analyst can make the national average of more than $55,000 per year, according to

Simplilearn has dozens of data science, big data, and data analytics courses online, including our Integrated Program in Big Data and Data Science. If you’d like to become an expert in Data Science or Big Data – check out our Masters Program certification training courses: the Data Scientist Masters Program and the Big Data Architect Masters Program.

With industry recommended learning paths, exclusive access to experts in the industry, hands-on project experience, and a Masters certificate on completion, these packages will give you need to become an expert in your field of choice and land a top job.

Watch this video on Data Science vs Big Data vs Data Analytics

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About the Author

A project management and digital marketing knowledge manager, Avantika’s area of interest is project design and analysis for digital marketing, data science, and analytics companies. With a degree in journalism, she also covers the latest trends in the industry, and is a passionate writer.


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