Data Science: Career Opportunities and Job Prospects

Data Science vs. Data Analytics - What's the Difference?

Data Science vs. Data Analytics - What's the Difference?

We live in a data-driven world—a world where billions of data in terms of zettabytes and exabytes are generated. As a result, the phrase "Big Data" was coined, and to better understand this big data, various data-related roles including data science and data analytics have moved from being mostly restricted to academics to being key components of industries and organizations. However, distinguishing between data analytics and data science may be difficult. If you need to analyze what role might suit you, it's critical to understand what each brings to the table and the main difference between data science and data analytics.

In this blog, we'll look at the Data Science vs Data Analytics argument, concentrating on the distinction between the two.

What is Data Science & Data Analytics

Every day, companies all over the world create massive amounts of data in the form of log files, web servers, transactional data, and different customer-related data. Data science and data analytics are all about the study of extracting useful insights from this data by combining subject experience, computer abilities, and understanding of mathematics and statistics. As a result, this helps us get insights that analysts and business users may employ to create meaningful commercial value.  

Down below is the critical difference between Data Science and Data Analytics that can help you gain a clear understanding of both fields.

Data Science

Data Analytics


Data science is an umbrella term for a variety of areas that use massive databases to harvest information. 

Data analytics is a more specialized form and is dedicated to generating actionable insights that may be implemented right away based on current inquiries.

Type of Data used

Data science investigates unstructured data by utilizing methods such as machine learning and artificial intelligence.

Data analytics and investigates structured data with tools such such as Microsoft Excel and data visualization software.


Data scientists are often responsible for constructing algorithms and predicting models, as well as establishing data modeling methods, to extract the information required by an organization to address complicated challenges.

Data analysts are frequently in charge of creating and managing data systems and databases, interpreting data sets with statistical techniques, and generating reports that effectively explain trends, patterns, and forecasts based on relevant results 

Skillset Required

Machine learning, software development, Hadoop, Java, data mining/data warehousing, data analysis, Python, and object-oriented programming 

Data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database administration & reporting, and data analysis


Data science looks at the big picture, asking strategic questions and fostering innovation

Data analytics focus on the micro, utilizing data to answer particular queries and uncover actionable insights.

Similarities Between Data Science and Data Analytics

To comprehend the distinction between Data Science and Data Analytics, you need first be aware of the similarities between the two.  There exist many similarities between data science and data analytics.

  • Both are professionals who can be held accountable for clearly and effectively communicating their results.
  • Both areas are primarily STEM-focused and in great demand across many sectors.
  • Both employ data to aid in the understanding of an organization's activities and aid in decision-making.
  • Both need a foundation in mathematics, statistics, and programming (Hadoop, R, SAS, SQL, and Python).
  • Professionals in both data science and analytics must be very analytical and meticulous in their problem-solving and project management approaches.

Career as a Data Scientist

With millions of Big Data job opportunities globally, the post of data scientist has emerged as the hottest job of the decade. You can have a successful career in data science if you have the necessary qualifications. Individuals with the right skill set will continue to be in high demand, and those who are presently in data science professions will see their incomes rise in the future.

Income estimates based on 7,491 salaries posted anonymously to Glassdoor by Data Scientist employees show that the national average salary for a Data Scientist in India is ₹ 11,70,000.  Oracle, Apple, Microsoft, Booz Allen Hamilton, State Farm, Walmart, and other well-known companies regularly post job openings for data scientists.

Data Scientists are still one of the fastest growing occupations, ranking in the top 20 positions in India for the year 2022 and according to LinkedIn, there are 20,000 plus Data Scientist jobs available in India.

Career as a Data Analyst

Data analyst works at the crossroads of information technology, statistics, and business. When it comes to data analyst careers, they are in high demand in practically every industry. Because the profession is so important to corporate success, the majority of companies require a data analyst, and job prospects are plentiful.

Data analytics is a branch of business intelligence that includes statistics, sociology, communication, and computer science. These practitioners must be numerical specialists as well as effective communicators with the ability to think abstractly. These analysts should collaborate closely with the project manager to aid in project planning.

In terms of employment vacancies, it is acceptable to prioritize data analytics above data science. According to LinkedIn, there are over 37000 job vacancies for data analysts across various firms and sectors. However, the data analyst salary might be less than the data scientist's. Based on 10,256 anonymous salary reports submitted to Glassdoor by Data Analyst employees from various organizations and industries, the estimated national average pay for a Data Analyst in India is 6,00,000.

Data Science vs Data Analytics - How to choose?

Data scientists are often far more technical, needing a mathematical mentality, whereas Data Analysts adopt a statistical and analytical approach. A Data Analyst's employment is more of an entry-level position in terms of career. Companies are looking for Data Analysts with good expertise in statistics and programming.

Data analysts often have a bachelor's degree in a science, technology, engineering, or math (STEM) subject, as well as a master's degree in analytics or a related discipline. They also have experience in math, science, programming, databases, modeling, and predictive analytics, whereas data scientists are primarily concerned with inventing and building new data modeling and production processes. They sift through data using techniques such as data mining and machine learning and generally have master’s level degrees.

Choose regardless of your educational background, because learning and further education may be obtained afterward, that is not a big deal. In any case, knowing which occupation matches your own interests can give you a better notion of the type of work you'll love and likely thrive at. Take the time to consider this aspect of the equation, as matching your work to your interests can go a long way toward keeping you satisfied in your career for years to come.


In this blog, we looked at the difference between data science and data analytics. As we've seen, Data Science and Data Analytics are two sides of the same coin, with functions that are inextricably interwoven. You're in luck if you're looking for a new job either in data analytics or data science. Whatever discipline seems right for you, both professions are in high demand, and this trend does not appear to be changing anytime soon.