Data Analyst vs Data Scientist is the most passionate debate in the big data community as some of them think that data scientist is just another glamorized job title for data analyst. Most of them are of the thought that both job titles come with the same responsibilities but that’s not the reality. Though a data scientist job description and a data analyst job description might look quite similar there are differences between the two job titles. The debate on Data Analyst vs Data Scientist is becoming increasingly complicated with rapid technological advances and that’s the reason we thought of digging into the details and set things straight by differentiating between a data analyst and a data scientist.

Whether you’re a data analyst planning to make a career transition as a data scientist or a fresher deciding on the best career path, you might be confused about what is the difference between a data analyst and data scientist and which one should you choose.  Post-reading this article you will be able to decide on the best job title for yourself. 

A Pro Tip: Don’t worry about the job titles, instead aspire to become a better data specialist.

Data Analyst vs Data Scientist  – The Definition

What is a data analyst?

A data analyst collects and organizes a large amount of structured data from a single source to derive statistical explanations out of it to make the data actionable. These statistical explanations are showcased through business reports and data visualizations for stakeholders to make strategic business decisions. A data analyst extracts information from data using various methodologies like data cleansing, data aggregation, data munging, data querying, and data modeling. Further, data analysts come with various job titles based on the industry and field they work in – Sales Analyst, Operations Analyst, Customer Success Analyst, Market Research Analyst, Marketing Analyst, Business Analyst, Financial Analyst, and more. 

For example, a data analyst at Spotify might be responsible for examining the music listening patterns of various users. Similarly, a data analyst working in the transport industry might collect, organize, and process various transportation datasets or dispatch records to discover trends and patterns that can help improve the efficiency of transport services.

What is a data scientist?

A data scientist is a programmer, statistician, storyteller, and fortune-teller who predicts the future by exploring data from multiple sources. Yes, a data scientist is a unicorn with multiple skills combined. A data scientist job role is built upon the core competencies of a data analyst job role with additional machine learning, programming, and software engineering skills. A data scientist explores novel ways of using algorithmic, statistical,  and machine learning tools and techniques to uncover valuable business insights in data and help organizations transform this information into the right actions.

For example, a data scientist at Spotify will work with gigabytes of data to build customer segmentation models that will help engineers develop personalized music recommendation systems or help create targeted advertisements based on the customer segment. Similarly, a data scientist in the transportation industry will build predictive models to study the impact of unplanned events like labor strike, accidents, breakdown of vehicles, etc which will help understand the impact on the overall operational efficiency.

The fundamental goal of a data analyst is to analyze and mine business data while that of a data scientist is to ask the right business questions and find ideal solutions for those questions. A data analyst finds a solution to a given business problem while a data scientist creates a business problem and finds a solution for it to benefit the business.

Data Analyst vs Data Scientist – Skills Comparison

The skills required by a data scientist and a data analyst can be split across three broad categories – Domain Knowledge, Programming, Math & Statistics.

  • Domain Knowledge / Business Acumen:

    Whether you are a data analyst or a data scientist, you will have to present the outcome of your analysis to business stakeholders. Some amount of business domain expertise is needed based on the industry you work in. For example, if you work in government, science, or healthcare sector, you’ll require a different level of domain expertise than if you work in education or marketing industry. A strong understanding of industry best practices is important for both the job titles. However, a data scientist should be able to interpret and explain complex machine learning models to stakeholders. Data scientists are required to have additional knowledge of techniques like LIME and SHAP to explain predictions of machine learning models to stakeholders in a reliable way.

  • Math & Statistics:

    Mathematics and Statistics are the bedrock of the analytics landscape. A data analyst requires basic math knowledge to train basic prediction models and understand their working. For instance, a data analyst will need to know the basic concepts of linear algebra, probably, and optimization to train a simple prediction model.  To the contrary, a data scientist requires heavy math and statistics knowledge because the implementation of almost all machine learning algorithms has deep mathematical underpinning. So, if you’re terrified of looking at math equations, you’re not going to enjoy being a data scientist. If you can get rid of the fear for numbers ad make mathematics your friend, data science is for you. To push the boundaries of machine learning, understand the math behind algorithms, and build cutting-edge tools data scientists make use of advanced math routinely in their work. To decode how and what a machine learning algorithm does and explain the decision to stakeholders requires an in-depth understanding of the underlying math behind the cool algorithms.

  • Programming:

    A data analyst does not require strong coding expertise as they usually work with packaged software and structured data to glean business insights based on hypotheses. A data analyst does not often go beyond using Excel and SQL when it comes to programming. Data analysts create reports and visualizations using various BI tools like MicroStrategy and are not involved in building complex machine learning algorithms that require strong coding expertise. While a data scientist should have proficiency with one or more programming languages like Java, Python, or R as the job role of a data scientist involves complex data structures complemented with complex machine learning algorithms.


What does a data analyst do?

  • Finding answers to business questions related to customer preferences and taste, marketing strategies, and their performance, etc are the core responsibilities of a data analyst.

  • Explore and analyze structured data from a single source like CRM

  • Use SQL for Data Querying

  • Use Microsoft Excel or other spreadsheet tools for data analysis and forecasting.

  • Use BI tools to create reports, dashboards and other forms of visualization to showcase analytic outcomes.

What does a data scientist do?

  • Boost revenue generation, enhance customer experiences and improve marketing strategies are the core responsibilities of a data scientist.

  • Explore structured and unstructured data from multiple sources and examine them.

  • Uses Python or R language for Data Cleansing

  • Builds custom machine learning models to suit business requirements.

  • Perform statistical analysis using different ML algorithms like Linear Regression, Gradient Boosting, KNN, Decision Trees, Random Forest, etc.

  • Monitor and analyze the accuracy and performance of ML models.


For any profession, salary is a key consideration not to ignore the job satisfaction though. Despite many overlapping skills, a data scientist and a data analyst earn different salaries and have different job descriptions. No doubt, a data scientist job comes with a much higher payoff than a data analyst considering the upfront investment one has to make in upgrading their skills. However, the salary for a data analyst or data scientist is very much dependent on the company and the industry they work for.

According to Glassdoor, the average salary for a data analyst is 5 lakhs per annum while a data scientist earns an average salary of 10 lakhs per annum in India. A data scientist earns almost double the salary of a data analyst not to ignore the fact that a data scientist is a unicorn or rather say a Rockstar of the analytics industry with a blend of diverse skills, unlike a data analyst that does not have a cool tag yet.

Can I make a career transition from a data analyst to a data scientist? How?

Many data analysts already have excellent domain expertise. One can easily gain entrance into the role of a data scientist by developing an in-depth understanding of various machine learning algorithms and have a strong ability to sling code i.e. develop coding expertise. Passion and strong motivation can help you go a long way in getting your foot into the data science industry. Learn how you can make a career transition from data analyst to data scientist.

Which career path is best for you?

With over 97K job openings in analytics and data science, India is the next biggest analytics and data science job hub after the US. So, if data is the new “oil” then India definitely is the new “Arabia”. According to a Bain & Company Report, the advanced analytics talent pool is anticipated to reach 1 million globally by end of 2020 with India alone contributing to a talent pool of over  2,00,000 professionals, a major share of the overall global analytics talent supply boom.

These statistics are a clear proof of evidence that there is a need for professionals to fill positions as data scientists as well as data analysts. The demand for advanced analytics talent is so huge that it exceeds the supply. 

Each of these job titles is significant on its own and one is incomplete without the other. Appreciating the differences between the two job roles and understanding the similarities will help you choose the best trending big data job that suits your skillset more. If you are still confused and cannot make a career choice, let Springboard counselors help you make the right decision. Get in touch with us, our career counselors will give you a call to discuss your career objectives and advice about how Springboard can support you in advancing your career.

For prospective candidates who are desperately keen on taking up a data handling job, Springboard offers a number of pathways to becoming a data scientist or a data analyst. Check Out Springboard’s Data Analytics Career Track and Data Science Career Track to decide which one is best for you.