Data Science VS Data Analytics

Data Science VS Data Analytics

Data science and data analytics are emerging technologies. There is a fine line between data science and data analytics. They both are almost similar with a slighter difference. Data science builds questions and answers them for the future whereas data analytics involves checking hypotheses. In today’s world, everything is done by either data scientists or data analysts. Let us know about these more in-depth.

Data Science

Data science is a multidisciplinary field that uses specific methods and systems to extract information from structured and unstructured data. It uses various algorithms, statistics, mathematics, and data analysis to find out the hidden patterns from the raw data. Data science brings insights that can be helpful for a business to understand the trend and make better decisions. Data science is related to computer science and it is a subset of AI.

Data Analytics

Data analytics is the process of examining data sets in order to retrieve information from the data. Data analytics is helpful in making scientific decisions. It includes numerical analysis of numerical data that can be measured statistically. There are different types of data analytics, they are descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics.

Data Science VS Data Analytics – Difference  

Data science arises the question which may not concern others but is necessary from the future aspect. Data science tries to build connections to extract information from the data. Whereas data analytics is a branch of data science that focuses on specific areas and questions that are brought by the data science and answer them. Data science focuses more on machine learning while data analytics focuses on viewing historical data. Data science finds meaningful relations between data sets and data analytics is designed to uncover the hidden insights of data. The scope of data science is macro and data analytics is micro. Data science involves the development of algorithms, data interfaces to solve analytically complex business problems and on other hand, data analytics involves various branches of analysis and statistics. Data science is used for image recognition, internet research, speech recognition, and digital marketing whereas data analytics is used in travel, healthcare, gaming, finances, etc.

Skills Required

Data science: 

  • Highly experienced in mathematics, machine learning, programming, and advanced statistics.
  • Expert in Python, MATLAB, programming in R, Java, and SQL.
  • Ability to write, collect, and prepare maintainable code.
  • Great understanding of how and when to use AI and machine learning appropriately for the business.

Data Analytics:

  • Skilled in Excel and SQL database.
  • Good computer and technical skills with analytical and numerical skills.
  • Experienced in Python Or R programming language.
  • Data visualization and presentation skills.

Job Role and Responsibility

Data scientist:

  • To process, clean, and verify the integrity of the data.
  • Fact-finding data analysis.
  • Identifying new trends in data to make future predictions.

Data Analyst:

  • Extracting data and analyzing it.
  • Using statistical tools discovering new patterns.
  • Data cleansing.

Career Perspective 

Career perspective is similar for both data science and data analytics. Data science aspirants must have strong knowledge of software engineering, computer engineering, and data science. Data analysts should have a good knowledge of statistics, mathematics, information technology, and computer science. Data scientists require more technical skills and a mathematical mindset whereas data analysts take an analytical and statistical approach.


 Data analytics and data science are the buzzwords and are trending today. Many people get confused between data science and data analytics. However, they both work with big data but there is a slighter difference between them. Hope this blog helped in clearing the concept.  There are great career opportunities in both the field. Future belongs to these fields.

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