Data scientists and data analysts are the perfect career option for someone who possesses an analytical blend of mind and loves decoding data. Both these job profiles are the most sought after and well-paying careers in the tech industry today. The data scientists and data analyst jobs are in demand, and it has been projected that these jobs are here to stay and grow in the future.

Most of us are well aware that these two job profiles are among the industry’s most lucrative ones today. However, if you ask the difference between the two, then the majority of them would falter. Even people who possess some knowledge of data scientists and the data analyst job profiles would be confused if asked to explain the differences between the two.

Data scientists and data analysts both work with data. The difference between data analyst vs. data scientists lies in what they do with the data. Read below to understand the key differences between data scientists and data analysts.

Let us start with the definition of the two profiles.

  1. Data Analysts
  2. Data Scientists
  3. Data Analyst
  4. Data Scientist
  5. Differences Between Data Analyst vs Data Scientists

1. Data Analysts

A data analyst will sift through huge amounts of data and try to identify a trending pattern. He will analyze the story that the data wants to tell. He then tries to draw out some decisions based on the insights that he gets from the data. The data analyst will use visual representations like graphs and charts to showcase what the data reveals. Sifting through data, the data analyst will make reports to explain the hidden insights in the data. So if there is someone in your company who tries to explain queries using charts and other graphical representations, they are the data analysts.

2. Data Scientists

The data scientists are professionals who are a pro at interpreting the data. They also have mathematical modelling and coding expertise. The data scientists have an advanced degree. He would have moved the career ladder chain from a data analyst to a data scientist in most probability. The data scientists would have started as a data analyst. They also have additional expertise in machine learning and have advanced programming skills. They are well-versed in creating new data modelling processes.

The data scientists make use of predictive models, algorithms, and much more. The data scientists will analyze the data and garner the insights that they can take action on. They then share their insights with the company management.

In a nutshell, a data scientist is the one who collects, cleans, and mungs data because data is never sorted when in the raw form. So basically, a data scientist is the one who does not just collect data but also builds patterns, algorithms, designs the experiments, and shares the results of data with their members in a digestible format.

3. Data Analyst

A data analyst gathers data, organizes it, and reaches an insightful conclusion with it. Every industry benefits from the data analyst work, be it retail, healthcare, or any other industry. The data analyst will develop new processes and systems to collect and compile data and draw business conclusions.

A data analyst’s role is to deliver reports, examine patterns, collaborate with the stakeholders across various departments, consolidate data, and set up the infrastructure. Consolidating data is the key work of a data analyst.

4. Data Scientist

A data scientist is someone who helps a company to benefit from data. Data scientists are experts at data science, statistics, R programming, Big Data, SAS, and Python. They are some of them who enjoy the best salaries in the industry.

Data scientists are problem solvers who determine the questions for which answers are sought for. They come up with various solutions to try and solve a problem. Some of the tasks that a data scientist tries to tackle are pulling our data, merging, and analyzing it. They look for patterns or data trends. The data scientist makes use of various tools like Python, Tableau, Excel, Pyspark, Hadoop, etc., to test the algorithms.

In the data scientist roles, they try to simplify data problems and then develop some predictive models. The data scientist will build the data visualization and then write the results and pull out their concepts.

5. Differences Between Data Analyst vs Data Scientists

Now that we are clear about the data scientists’ job roles and responsibilities, the data analysts let us quickly go through the differences between data analyst vs. data scientists.

  • A data scientist needs to have strong data visualization and business acumen skills. A data analyst does not need such high-level skills.
  • The data scientist will explore and examine the data through various sources that may be disconnected from each other. A data analyst usually gets data from a single source online, like through the CRM system.
  • A data analyst will solve questions that are given by a business. The data scientist will formulate various questions whose solution is likely to be beneficial to the business.
  • A data analyst may not need to possess a machine learning experience. They are not required to build any statistical models. On the other hand, the data scientist should be able to build statistical models and be well-versed with machine learning.
  • The data scientist will mine data using the ETL pipeline or the ATIs. They will use languages to clean the data. The data analyst uses SQL to perform a data query, and they analyze and forecast it using excel or Spark, or Apache Hadoop frameworks.
  • The data analyst will usually need to deal with data that is small or localized in scale. The data scientist will have to focus on the business needs of the data, the market requirements, and explore more of the business from the black data that is available.


The difference between a data analyst and a data scientist is that a data scientist is higher than a data analyst. The data scientist needs to have a lot of expertise in data science, which is not wanted in data analysts. The scope of work of a data analyst is at the macro level. The data scientists deal with data at the macro level. 

Data analysts are beneficial in industries like gaming, healthcare, and travel. Data science is used in digital advertising and internet searches.

The data analysts need to know the BI tools and only a basic level understanding of statistics. Broadly if you notice, then the data analyst and the data scientists are a similar kind of profile, with data scientists being a notch over the data analyst profile. Both these job roles are inevitable to an organization, and businesses need to invest in both these fields to sustain themselves in the market.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 



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