Data Analyst vs Data Scientist: Key Differences

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If you’re an aspiring professional in the technological world and love to play with numbers and codes, you have two career paths- Data Analyst and Data Scientist. Accordingly, Both these job roles have a huge demand in the market today. Therefore, the future job opportunities present more than 11 million job roles in Data Science for parts of Data Analysts, Data Engineers, Data Scientists and Machine Learning Engineers. What are the critical differences between Data Analyst vs Data Scientist? Let’s find out! 

Who is a Data Scientist? 

A data scientist is a professional who uses statistical and computational methods to extract insights and knowledge from data. Effectively, they analyse, interpret, and model complex data sets. Further, Data Scientists are also responsible for using machine learning algorithms to identify patterns and trends, make predictions, and solve business problems. Significantly, Data Science experts have a strong foundation in mathematics, statistics, and computer science. Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise. 

Who is a Data Analyst? 

A Data Analyst is an expert in collecting, cleaning and interpreting data that helps solve or answer business problems. Accordingly, they work with different data types, including sales figures, customer data, financial records and market research data. Effectively, Data Analysts use other tools like SQL, R or Python, Excel, etc., in manipulating and analysing the data. Accordingly, the main job of Data Analysts is to help businesses make data-driven decisions and improve their business performance. Furthermore, they mainly use analytical techniques to derive insights and statistical methods to identify patterns and enable informed decision-making. 

Key Differences between Data Analysts and Data Scientists

  • A Data Analyst is responsible for conducting routine analysis and evaluating reports daily. On the other hand, a Data Scientist is responsible for designing, storing, manipulating and analysing data.
  • Effectively, a Data Scientist is responsible for evaluating new ways of capturing and analysing data using Data Analysts. The latter is responsible for bringing sense to the data.
  • Furthermore, a Data Analyst is essential for answering specific business-related questions. On the other hand, a Data Scientist must work at a macro level to develop new ways of asking and answering questions.
  • Additionally, Data Scientists and Data Analysts use valuable tools to analyse and interpret data for actional insights. However, Data Scientists use tools like Python, Java, and Machine Learning for manipulating and analysing data. Significantly, in contrast, Data Analysts utilise their proficiency in a relational databases, Business Intelligence programs and statistical software.

Data Analyst vs Data Scientist: Roles and Responsibilities  

The roles and responsibilities of a Data Analyst and Data Scientist differ in analysing the reasons for something happening and finding what will happen. However, it is essential to read job descriptions carefully before filing a job application because the roles and responsibilities of a Data Scientist may involve a job posting for a Data Analyst or vice-versa. Significantly, here is an overview of the job description with the roles and responsibilities of a Data Analyst and a Data Scientist.

Data Analysts:

  • Using SQL for Data Query
  • Utilising Excel for Data Analysis and forecasting
  • Furthermore, using Business Intelligence software to create Dashboards
  • Conducting different types of analytics like descriptive, diagnostics, predictive or prescriptive

Data Scientists:

  • May involve in scrubbing data for 60% of their time
  • Additionally, you need to use APIs or build ETR Pipelines for Data Mining.
  • Indulging in the use of programming languages like Python or R for Data Cleaning
  • Chiefly conducting Statistical analysis using Machine Learning algorithms like NLP, Logistic regression, etc.
  • Evidently, developing and creating techniques of programming and automation for simplifying day-to-day processes using tools like TensorFlow for training machine learning models.
  • At length, use Hadoop, Spark, and tools like Pig and Hive to develop big data infrastructures. 

Data Analyst vs Data Scientist: Education and Work Experience 

A bachelor’s degree in a quantitative field is considered essential in statistics, mathematics or computer science to become a Data Analyst or Data Scientist. However, it is not necessary if you have a graduation or a post-graduation degree in a technical field because it is not mandatory considering if you opt for online certification courses. Accordingly, having technical subjects like Mathematics or Computer Science might be advantageous. On the other hand, pursuing a certification course in Data Science or Data Analytics might help you choose your career path. Significantly, Pickl.AI offers data analyst course online and Data Science Course Online, allowing you to become a professional with hands-on experience in the field. Accordingly, you may even have a master’s degree to ensure you can grow your career as a Data Analyst or Data Scientist.

Evidently, in the case of work experience, you may opt for Data Science Bootcamps or Data Science internships which allow you to become a professional with practical skills. Effectively, internships allow you to have experience in the real world, enabling you to aim for higher job responsibilities.

Data Analyst vs Data Scientist: Required Skills

Some common skills necessary for Data Analysts and Data Scientists include Data Mining, Data Warehousing, Math, Statistics, Computer Science, Tableau and Data Visualisation. Effectively, the differences in terms of additional skills for both job roles are as follows: 

Data Analyst vs Data Scientist

Data Analyst vs Data Scientist: Salary

Significantly, as per the evaluations of Glassdoor, the average salary of a Data Analyst in India stands at Rs 6 lakhs per annum. On the other hand, a Data Scientist’s average salary in India is Rs 9 lakhs per annum.

Data Analyst vs Data Scientist: Career Growth

You must grab an entry-level Data Analyst role to build a career as a Data Analyst. Accordingly, as a beginner, you will learn to use real-world business data for deriving valuable insights for the organisation. Additionally, you will utilise your skills for querying databases, generating reports using BI tools and analysing critical data. Eventually, you can develop your skills with time and efficiency to upgrade your career as a senior Data Analyst or Data Consultant.

The scope of career growth for a Data Scientist is high, with companies hiring for the job role vehemently. Effectively, Data Science is essential in every field, from Healthcare and Ecommerce to Manufacturing, Logistics, etc. Significantly, in India, by 2026, job roles in Data Science are supposed to expand by 11 million. Moreover, there is a skill gap in the Data Science field, making it a lucrative carer for you to choose from. Accordingly, you can develop your skills and enhance your efficacy as a professional expert.

Wrapping Up!

In this blog post, critical differences between Data Analyst vs Data Scientist helps you provide a clear distinction in choosing your career path. You now know the differences between the two job roles and can opt for an approach you want to explore. However, you can consider the role of a Data Analyst as a preliminary stage in becoming a Data Scientist.


  • Asmita Kar

    Written by:

    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.