Data Analytics Vs. Data Science Salary in 2022


Big Data has evolved as a prominent element in the IT world today thanks to the advanced analytics and outcomes companies gain. Data Analyst and Data Scientist are two of the most in-demand, high-paying Big Data careers. According to the World Economic Forum’s Future of Jobs Report 2020, these roles are the most in-demand across big data industries. There is an increased demand for data professionals. 

Read this blog to understand the job roles, the difference between data analytics and data science, and how each role uniquely approaches data. 

Who Is a Data Analyst, and What Do They Do?  

A Data Analyst primarily collects data to uncover trends that will assist corporate executives in making strategic choices. The field is concerned with conducting statistical studies to answer questions and solve issues. A Data Analyst uses technologies to query relational databases. A data analyst may also clean or format data, removing unnecessary or unsuitable information or determining how to cope with missing data. 

A data analyst often works as part of an integrative team to identify the organization’s goals before managing the process of data mining, cleansing, and analysis. To get and communicate their conclusions, data analysts employ programming languages, visualization tools, and communication skills. 

Who Is a Data Scientist, and What Do They Do?  

A Data Scientist is often more involved in the design of data modeling procedures, as well as the creation of algorithms and prediction models. As a result, data scientists have to devote more effort to developing tools, automation systems, and data frameworks. 

A Data Scientist is more focused on inventing new tools and ways to extract the information needed by the company to address complicated challenges. It is also helpful to have business acumen and critical-thinking abilities to grasp the significance of the data. A data scientist possesses a mathematical and statistical understanding and the ability to address issues in creative ways. 

The Key Difference Between Data Analytics vs. Data Science  

The abilities of a data analyst and a data scientist overlap; there is a major difference between the Data Analytics vs Data Science roles. Both positions need fundamental arithmetic abilities, a grasp of algorithms, strong communication skills, and expertise in software engineering. 

Criteria  Data Analytics  Data Science 
  • A Bachelor’s Degree in any discipline, such as Computer Science, Statistics, Finance, or Mathematics, can give you an advantage in becoming a Data Analyst. 
  • A Data Scientist is the highest-level position in any company, requiring a Master’s or Doctoral Degree in any of the subjects such as Information Technology, Statistics, Data Science, or Mathematics. 
  • The competence to take non-technical workers’ inquiries and requests and decide what kind of information is available and from what kind of data  
  • Ability to acquire, handle, and analyze relevant data using computer languages such as Python, SQL, R, and SAS  
  • Excellent research abilities to better understand the questions presented and intended responses so that data sets may be enhanced to boost the value of the insights they can deliver  
  • Proficiency with data visualization tools, which will be utilized to provide data-derived insights in an easy-to-understand style for people with both technical and non-technical backgrounds  
  • Exceptional communication skills are required to ensure that any relevant information acquired is delivered clearly and precisely to the relevant personnel so that decisions can be taken 
  • Knowledge of data analysis and its methods. To pursue new areas of inquiry, a data scientist must understand how data works and what sorts of insights can be hidden within it.  
  • An understanding of machine learning, artificial intelligence, NLP statistical models, and other related subjects is required to face the problem of predicting the likely outcomes of numerous events.  
  • Proficiency in Python and other data analysis tools for building new models and writing new software that can help organize, arrange, and filter data with minimal human work.  
  • Exceptional soft skills to provide easy collaboration with teams and departments. This is important for gathering data from sources, expanding one’s grasp of the issue under consideration, and presenting conclusions. 
  • Data collection and interpretation  
  • To get fundamental ideas and patterns from data  
  • SQL is used to perform fundamental data operations.  
  • Data is analyzed using analytical tools such as descriptive analytics, predictive analytics, and the use of visualization tools such as Tableau, Excel, and others for a deeper understanding of the data. 
  • Data cleaning, processing, and validation  
  • Execute some operations on datasets, such as Exploratory Data Analysis.  
  • Using ETL pipelines, use Data Mining methods.  
  • Use the appropriate machine learning algorithm to cope with the given situation.  
  • Use ML libraries to automate the issue code.  
  • Identify new trends in data to make better business forecasts 
  • Most data analysts begin their careers as junior members of bigger data analysis teams, where they learn the fundamentals of the work in a hands-on environment and get valuable experience in data wrangling. After a few years of experience, a data analyst’s career can evolve to encompass increased responsibility and opportunities to lead and design initiatives. At the most senior level, data analysts become team leaders, taking command of project selection and allocation.   
  • A junior Data Scientist is hired for a position that focuses on data manipulation. Preparing data for analysis varies so much from case to case that it is far simpler to learn. Once conversant with the practicalities of data analysis, data scientist expand their understanding of machine learning and its applications by designing algorithms and tools. A more experienced data scientist pursuing team lead or management positions, distributing projects, and collaborating closely with stakeholders and decision-makers. 
Salary Differences 
Based on Skills 



  • Python pays around ten lakhs per year in India as it is necessary expertise.  
  • Knowledgeable professionals with Big Data and Data Science, salary boosts by 26%.  
  • Professionals in SPSS and SAS may expect to earn approx. INR 7.3 lakhs.  
  • In India, an ML income may get around 17 lakhs per year. 
  • The Data Scientist average salary with a machine learning skillset is INR 884,921 
  • The average salary for a data scientist with a data analysis skillset is INR 788,491 
  • The Salary of a Data Scientist with a statistical skillset is INR 921,187 
  • The Salary of a Data Scientist with a Python skillset is INR 825,405 
Based on Experience
  • Entry Level (more than one year of experience): INR 342,363/yr 
  • Early Career (1-4 years experience): INR 422,408 a year 
  • 5-9 years of experience: INR 690,734 per year 
  • Experienced (more than ten years of experience): INR 942,653 to INR 1,750,000/year
  • A beginner graduate in India may expect to earn INR 511,468 rupees per year as an entry-level data scientist. 
  • A data scientist in their early career with 1-4 years of experience makes INR773,442 per year on average. 
  • Professionals with 5 to 9 years of experience may anticipate a salary of INR 12 to 14 lakhs per year. According to payscale, the average mid-level data scientist’s salary is INR 1,367,306 per year.

Data Analysts’ Salary Trends Globally 

  • In the United States, the average yearly income for a data analyst is more than $65,000. 
  • In the United Kingdom, the average yearly income for a data analyst is more than £27000. 
  • In Canada, the average yearly income for a data analyst is more than C$56000. 
  • In Australia, the average yearly income for a data analyst is A$82,000. 
  • S$55K is the average yearly pay for a data analyst in Singapore. 
  • In Germany, the average annual data analyst income is €45,338.  
  • In Denmark, the average annual data analyst pay is DKK 881,794. 
  • In Malaysia, the average yearly income for a data analyst is RM 44127. 

Data Scientist Salary Trends Globally 

  • The average salary for a data scientist in Australia is AU$92,376. 
  • The salary of a Data Scientist in Canada is C$78,948. 
  • Data Scientist Salary in the United Kingdom is £49,954 
  • In Europe, the average salary for a data scientist is €55475 per year. 
  • The Data Scientist average salary in France is €45385 per year. 


We hope this article has helped you to grasp how Data Analysts and Data Scientists differ in terms of work duties, educational certifications, competencies required, income, and career advancement. Choose the finest job pathway between these two by considering your educational background, personal hobbies, and more. Both job roles are in great demand, with decent paychecks and attractive growth pathways. UNext Jigsaw allows you a chance to become digitally certified in Business Analytics and Data Science courses. 

Related Articles

} }
Request Callback