Big Data Vs Data Science Vs Data Analytics

Data has an impact on the way people live. According to a recent survey, it is a fact that the data generating rate is more than the human birth rate. The extensive landscape of Big data has unveiled by the digital economy. Several industry experts in the fields of data analytics, data mining, data engineering, and data science are using it.

In this article, let’s have a look at significant differences between Big Data vs. Data Science vs. Data Analytics.

Big Data Vs Data Science Vs Data Analytics - What are the Differences?

What is Big Data?

In the present day scenario, we are witnessing an unprecedented increase in generating information worldwide as well on the Internet to result in the concept of big data. It refers to an extensive collection of data from distinct resources and not available through standard formats, which most of us are aware of. 

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Big Data

Big data is of several types of data as follows:

  • Structured Data: Transaction data, RDBMS (Relational Database Management Systems), OLTP, etc.
  • Unstructured Data: Emails, Blogs, Tweets, Social Networks, mobile data, Web pages, and so on.
  • Semi-Structured Data: text files, system log files, XML files, etc.

Therefore, regardless of types, information can be understood as big data, and processing usually begins with data aggregation through multiple sources. Still, some confusion exists between Big Data, Data Science, and Data Analytics though all of these are the same regarding data exchange, their role and jobs are entirely different.

Forbes magazine published an article stating that data is continuously growing than ever before and by 2020, more than 1.7 MB of new data in every second would be created for every living being worldwide. In this scenario, it is essential for one to know data rudiments since the future lies here.

Big Data is the term rounding everywhere for some time now. Still, there is a lot of conclusion about what it means. In fact, it is continually evolving as it remains the driving force behind several trending digital transformation waves such as Data.

It is not easy to process big data using traditional data analysis methods. Instead, specialized modeling techniques are essential to processing unstructured data, systems, and tools to extract information and insights required for organizations. Big data is the best way to solve unsolved problems related to handling and managing data. With big data analytics, unlocking hidden patterns and have an idea of customer’s to address their needs.

Big data generation is in multi-terabytes. Unlike traditional technologies like RDBMS, big data changes fast and appears in distinct forms that are difficult to process and manage. Big Data solutions provide the techniques, tools, and methodologies to capture, store, analyze and search the data in seconds to find insights and relationships for innovation and competitive gain.

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What is Data Science?

Data Science encompasses data cleansing, analysis, and preparation. It is an umbrella term in which several scientific methods apply. Tools like mathematics, statistics, and several others use data sets and scientists apply them to extract knowledge from data.

[ Related Article: Overview of Data Science

Data Science

Data Science is a tool to tackle Big Data and to exact information. Data scientists initially gather data sets from distinct disciplines and then compile it. After compilation, they apply predictive analysis, machine learning, and sentiment analysis.

Proceed with sharpening the point to derive something. Finally, he extracts some useful information from it. Data scientists understand data in a business view and provide accurate predictions and charges for the same, thus preventing a business person from future loss.

What is Data Analytics?

It is a fact that most of us think both data science and data analytics are similar, which is not correct. They both differ at a minute point, can notice that through deep concentration. Data analytics is the fundamental level of data science. 

Data Analytics

Data analytics are mostly used in business and computer science and in commercial industries to increase business efficiency. It is the science required to draw insights from raw information sources and discloses the metrics and trends t/o avoid massive data loss. Data analytics are used to verify existing theories and to enable organizations in several industries to make better decisions.

Applications of Big Data


The vital element when trying to advance in the retail business is only possible through staying competitively and serving the customer in a better way. It is possible only through proper analysis of all resources of disparate data dealt daily by organizations such as weblogs, customer transaction data, social media, loyalty program data and store-branded credit data.

Financial services

Big Data service offering firms like retail banks, private wealth management advisors, insurance firms, credit card companies, etc. for their monetary services use big data in distinct ways such as customer analytics, fraud analytics, compliance analytics, and operational analytics.


Telecommunication service provider’s priorities are to retain customers, expanding the existing customer base, and gaining new ones. To fulfill this goal, combine and analyze the customer as well as machine-generated data on a daily basis.

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Data Science

Before looking into data science applications, let’s have a glance at retargeting or remarketing- the ad banners on random websites or any products displayed while you search for on the internet.

These products are used to show a target audience who decided to use data science Data science enables the organizations to determine the patterns and behaviors of the users. Through this, the digital ads become popular and running successfully with better CTRs.

Search Engines

Search engine algorithms use data science to deliver accurate results for queries. Data science is used to process a significant amount of queries and convert them into useful patterns. It enables providing accurate results according to the user’s requirements.

Delivery Logistics

In this advanced technological era, e-commerce has become a robust industry with massive demand for online shopping. It led the logistics companies to improve their delivery experience and to attract the organizations to use data science to understand the absolute paths.

Fraud and Risk

Finance companies are required to verify continuously on their toes to not fall into fraud loans, debts and losses. Data science helps these companies with broader security check and improve customer profiling and to find patterns that help them in risk and fraud detection.

Frequently asked Data Science Interview Questions

Applications of Data Analytics

Management of energy

Many firms use data analytics for energy management including energy optimization, building automation in case utility companies, smart-grid energy, and energy distribution. The primary focus is to monitor and control network devices, dispatch crews, and service outage management. 


Core pressure is the primary challenge that hospitals are facing today as it tightens the treatment of several patients, data analytics helps hospitals to improve the quality of care. Machine and instrument data is used growing for optimizing and tracking treatment, patient flow, and equipment use. 


Data analytics in gaming includes data collection to optimize and spend across games. These manufacturing companies get a good insight into likes, dislikes, and the user's relationships.


Data analytics in Travel helps to optimize buying experience via website blog, data analysis, and social media. Customer’s desires and preferences can correlate with the existing sales followed by browsing can enhance conversions.

Big Data vs Data Science vs Data Analytics

Skills and Tools

To become a Big Data professional, considering the below things are necessary:

  • In addition to mathematical and statistical skills, it is essential to know the programming can go for big data. 
  • Data visualization skills and analytical skills are crucial to look at meaningful patterns and solving problems from the massive amount of data.
  • Proficiency in database development and management with necessary programming skills in languages like R.
  • Having good business skills like understanding business objectives and communication is essential.
  • Finally having a significant data certification is an added advantage. Consider taking an online course to be successful.

To become Data Scientist

  • Data Scientists have high analytical skills and exceptional in data management. Most data scientists hold a Ph.D. degree or a master’s degree with excellent programming, statistics, and mathematics skills.
  • Technical skills like data mining, machine learning tools, unstructured data techniques, and data managing skills are essential for a data scientist.
  • Programming skills such as Python, C/C++, R, Pearl, SAS, and Java languages
  • Sound knowledge of Hadoop platforms and database systems
  • Essential business skills like industry knowledge and communication
  • Get certification from a prominent institution.

To become a Data Analyst

  • Data analysts and data scientists have similar skills: some of the essential skills required to become a professional data analyst:
  • Technical skills including mathematical, data mining, machine learning, and statistical
  • Programming skills such as SQL, Python, Matlab, R, SAS, and Excel.
  • Comparing Roles, Responsibilities, and Salaries of Big Data professional vs. Data Scientist vs. Data Analyst in general.
  • Essential business skills like industry knowledge and communication

Big Data Roles and Responsibilities

Big Data Roles

  • Chief Data Officer
  • Data analyst
  • Big Data Visualizer
  • Big Data Solutions Architect
  • Big Data Engineer
  • Data warehouse manager
  • Data Architect
  • Business intelligence analyst
  • Data warehouse analyst
  • Data modeler
  • Database developer
  • Portal administrator
  • Database administrator
  • Business System Analyst
  • Data mining analyst
  • Data strategist
  • Business Data Analyst

Big Data Responsibilities

  • Selection and integration of any big data tools and frameworks based on the requested capabilities
  • ETL process implementation
  • Monitoring and advising performance as per infrastructure trends
  • Defining policies for Data Retention

Data Scientist Roles and Responsibilities

Data Scientist Roles

Data Scientist Responsibilities

  • Data processing and Cleansing
  • Predicting business problems and ideas to achieve better results in future
  • Develop analytical methods and machine learning models.
  • Finding new features that add value to the business.
  • Data mining using state-of-the-art methods
  • Doing the ad-hoc analysis and presenting results in a more precise manner.

Data Analyst Roles and Responsibilities

Data Analyst Roles

  • Database Administrators
  • Operations
  • The Data Architects
  • A Data Analysts

Data Analyst Responsibilities

  • Identifying quality issues in data acquisition.
  • Mapping and tracing data to solve business issues.
  • Coordinating with engineers to gather new data.
  • Performing data statistical business analysis.
  • Documenting business types and structuring data.

Comparing Salaries

  • The average salary for big data analysts is around $69,885 p.a. in the USA.
  • The average salary for the data scientist in the USA is $130,200 p.a.
  • The average salary for a data analyst in the USA is $69,885 p.a. 

Comparing Big Data vs Data Science vs Data Analyst Salaries

Economic Importance: Big Data vs Data Science vs Data Scientist

  • In the current scenario, data has become the dominant backbone of almost all activities, whether it is education, technology, research, healthcare, retail, etc. It is the fundamental knowledge that businesses changed their focus from products to data. Even a minute piece of information is essential for deriving information as much as possible. As a result, the need for professionals and experts increased to bring in significant insights to use in distinct industries.
  • The IT industry is experiencing a revolutionized phase regarding data due to the slash in price in hardware with cloud adoption throughout the world. Today, distinct means of data storing, manipulating, and innovating methods are available according to the demand.
  • Several industrial sectors are enjoying the benefits of data creation through accessing huge volumes of data every day.
  • The demand for professionals in the field with appropriate techniques is increasing every single day. It is due to grow in the companies using this data for product innovation and driving service than before that makes them excel.


Whether it is all about Big Data or Data Science or Data Science vs. Data Analytics or Data Analytics vs. Big Data, it is a universal fact that maintaining some specialties in those areas which an essential skill is to companies today. So, if you are an IT expert planning to make your career in data analytics to the next level, then it is vital to consider any of these fields. Also, companies can also find employing services of these professionals as they help out in several years of insight and decision making.

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Last updated: 25 Apr 2023
About Author

Ravindra Savaram is a Technical Lead at His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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