Data never stop multiplying and therefore the collection, understanding, and handling of digital data became two key disciplines leading the way to data science and analytics. Although these two fields share some characteristics but are quite strikingly different. So to help you optimize your knowledge about two, here we have broken down the categories, on the basis of their differences, and reveal what they deliver. At present, more than billions of people use the internet creating quintillion bytes of data every single day.

After all, these are two of the hottest jobs in tech and are high in demand. Also, those who have the analytical mindset and interest in decoding data may consider a career as a data analyst or data scientist.

To comprehend, the data science and analytics fields these days have gone from largely being demoted to academia, to instead flattering of Business Intelligence and big data analytics tools. In spite of being interconnected, Data Analyst vs. Data Science pursues different approaches. Have a look to understand better.

What is Data Science?

Data science conceptually is used to tackle big data and comprises data cleansing, preparation, and analysis. A data scientist, therefore, gathers data from manifold sources and relates machine learning, predictive analytics, and sentiment analysis for the extraction of critical information.

They comprehend data commencing a business point of view and provide precise forecasts and insights to power critical business decisions. This multidisciplinary field focusses on finding actionable acumens from large sets of raw and structured data.

The main goal of Data scientists is asking of questions and locating potential avenues of study. They have less concern for answers and more emphasis on finding the right question to ask. Over a period of time, people define data science via a Venn diagram.

The Venn diagram created by Hugh Conway in 2010, consists of three circles – math and statistics, subject expertise and hacking skills. So if are capable of doing all three, you are already highly knowledgeable in the field of data science.

What is a Data Analyst?

A data analyst is typically the person who performs basic descriptive statistics, visualizes the data and communicates the data points for conclusions. It is essential to have an understanding of statistics, a good sense of databases, the ability to create new ideas, and the insight to visualize the data.

Hence Data analytics is referred to as the basic level of data science. It emphases on processing and performing statistical analysis on existing datasets. They concentrate on generating methods to capture, process, and organize data to expose actionable insights for current problems, and establishing the best way to present this data.

More simply, this field is directed towards problem-solving for which we don’t know the answers. In addition, it incorporates different branches of wider statistics and analysis by combining diverse sources of data and locating connections.

Data Analyst vs. Data Science- What is the Difference?

When it comes to data analyst vs. data science, only understanding the key characteristics is not enough rather one should set them apart from one another. Data science one hand is a canopy term for a more inclusive set of fields focused on mining big data sets and discovering innovative new insights, trends, methods, and processes. While Data analytics discipline is grounded on gaining actionable visions to succor in a business’s professional growth in an immediate sense. It is a wider mission and a branch of data science.

Concerning them, another notable difference between the two fields lies in the investigation. Typically, data science does no drill deep to specific queries for it is committed to arranging massive data sets to expose insights. Data analysis, by its nature, is operative based on specific goals, providing tangible answers to questions on the basis of existing insights. Using data analysis tools comprehensive intelligence makes a vital influence on obtaining a sustainable business program.

Another significant difference is the question of exploration. With, the science you centered yourself on the questions that need subsequent answering, while analytics lets your design process and solutions to issues, or roadblocks already present.

Skills and Responsibilities

Anyone interested in building a career in these domains should have skills in three departments: analytics, programming and domain knowledge.

Data Scientist Skills

  • Strong knowledge of Python, SAS, R, Scala
  • Hands-on experience in SQL database coding
  • Aptitude to work with unstructured data and correlate datasets from diverse sources
  • Understand multiple analytical functions
  • Knowledge of machine learning and new machine learning models
  • Identify new business questions for additional value.
  • Data Storytelling and Visualization.

Data Analyst Skills

  • Knowledge of mathematical statistics
  • The practice of R and Python
  • Data wrangling
  • Understand PIG/ HIVE
  • Writes convention SQL queries to find answers for business operations.
  • Analyze and mine business data and identify correlations
  • Identify any data quality issues and partialities in data acquisition.
  • Implements new metrics.
  • Map and trace the data from the system to system.
  • Design and create data reports via reporting tools for decision making.


The word “scope” here concerns to big and small, or more specifically, macro and micro. Essentially, science being a core macro and multidisciplinary field, covers a wider field of data exploration, and enormous sets of both structured and unstructured data.

On the other hand, analytics being a micro field, concerns with specific elements of business operations, viewing documenting departmental trends and streamlining processes for specific time periods or in real-time. Hence intent on structured data.


Data analytics as a field continues to expand and evolve, particularly in an era of digital information expertise or technology often used within the healthcare, retail, gaming, and travel industries for direct responses to challenges and business goals.

While science uses corporate analytics, search engine engineering, and autonomous fields such as artificial intelligence (AI) and machine learning (ML) industry to make its demand.


Well, it shouldn’t come as a surprise that data scientists significantly earn more money than a data analyst. The average salary of a data analyst depends on the category he is working for. For instance, financial analysts, market research analyst, operations analyst or other.

According to a salary survey report by the Bureau of Labor Statistics (BLS), the average salary of research analysts is $60,570, and financial analyst is $74,350. Whereas the entry-level salary for a data analyst ranges from $50,000 to $75,000 and for experienced data analysts it is between $65,000 to %110,000.

The median salary on the other hand for data scientists is $113,436. Average Data scientist salary in the US or Canada is $122K while data science managers lead at an organization earning an average of $176K.

Wrapping Up!

Even people who have basic knowledge of data science often confuse the data scientist and data analyst roles. So, here was the detailed guide on data analysts vs. data scientists. Although both work with data, the major difference is what they do with this data. So I hope this has given you a clear cut sight of these two innovative fields i.e. Data analyst vs. data science.

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