With Data Science jobs on the rise, there’s a question that often lurks in the minds of aspirants – What’s the difference between a Data Analyst and Data Scientist? Are these two the same thing? Such questions have caused considerable consternation among young people aspiring to a successful career in data science. In this article, we will discuss in detail the difference between data analysts and data scientists.
Before delving into the job profiles of a Data Scientist and a Data Analyst, let’s first understand the key differences between the two:
Table of Contents
Data Scientist Role –
Data Scientists are highly skilled professionals with a diverse set of coding, mathematical, statistical, analytical, and machine learning skills. Even in a Data Science interview, the majority of the questions revolve around these concepts. They investigate and examine large datasets gathered from various sources, cleaning, organizing, and processing them to make interpretation easier.
While they can perform analyst tasks, they must also work with advanced ML algorithms, predictive models, programming, and statistical tools to make sense of data and develop new data modeling processes. Depending on the skill set and job demand, a Data Scientist may also be referred to as a Data Researcher or a Data Developer.
Data Analyst Job Description –
Data Analysts, as the name implies, are primarily responsible for day-to-day data collection and analysis tasks. They must sift through data to extract meaningful insights. They examine business problems and attempt to answer a specific set of questions using a given set of data.
Furthermore, Data Analysts create visual representations of data in the form of graphs, charts, and other visual representations for the ease of understanding of all stakeholders involved in the business process. Depending on the skill set and job demand, a Data Analyst may also be referred to as a Data Architect, Data Administrator, or Analytics Engineer.
Based on this description of the two job profiles, it is clear that a Data Scientist is primarily concerned with extracting meaning from incoherence (unstructured/semi-structured datasets), whereas a Data Analyst is responsible for answering questions based on the findings of a Data Scientist. However, job roles do occasionally overlap, creating a grey area. While Data Analysts and Data Scientists have some similarities, there are some key differences between the two roles.
Also Check:
- Artificial Intelligence Jobs for Fresher
- Career in Artificial Intelligence after 12th
- Artificial Intelligence Jobs in the Future
- Artificial Intelligence Career Paths
- A Career in Artificial Intelligence
Difference between Data Analyst and Data Scientist based on responsibilities:
| Data Analyst | Data Scientist |
| Data should be gathered from various databases and warehouses, filtered, and cleaned. | Ad hoc data mining is used to collect large amounts of structured and unstructured data from various sources. |
| Create complex SQL queries and scripts to collect, store, manipulate, and retrieve data from relational database management systems (RDBMS) such as MS SQL Server, Oracle DB, and MySQL. | Design and evaluate advanced statistical models from massive amounts of data using various statistical methods and data visualization techniques. |
| Using Excel and BI tools, create various reports using charts and graphs. | Create AI models by combining various algorithms and built-in libraries. |
| Recognize trends and patterns in large datasets. | Using machine learning models, you can automate tedious tasks and generate insights. |
Difference between Data Analyst and Data Scientist based on skills:
| Data Analyst | Data Scientist |
| Excellent knowledge of statistics and probability. | A strong grasp of calculus, linear algebra, statistics, and probability. |
| Python programming and SQL knowledge are required. | Proficient in Python, SQL, R, SAS, MATLAB, and Spark. |
| Data analysis with MS Excel and report creation with Tableau. | Power BI data visualization and Tableau storytelling. |
| Data manipulation. | Data manipulation and data modeling. |
| Analyzing exploratory data. | Cloud computing and machine learning. |
Differences between Data Analysts and Data Scientists based on the average salaries per annum in India and the other four countries:
| Country | Data Analyst | Data Scientist |
| India | ₹6,00,000 | ₹10,25,000 |
| United States | $65,668 | $1,17,212 |
| United Kingdom | £35,000 | £49975 |
| Canada | $60,238 | $87,248 |
| Switzerland | CHF 96,121 | CHF 1,07,773 |
Conclusion
In the above article, we have learned in detail about the difference between data analysts and data scientists based on several parameters. Both Data Analyst and Data scientist has a huge career scope in India and worldwide. In a developing country like India, there is plenty of room for data scientists, data analysts, big data engineers, big data managers, and data architects.
FAQs regarding the difference between data analyst and data scientist:
-
Is it better to be a data analyst or a data scientist?
Data Analysts and Data Scientists are both in high demand. Many students and working professionals want to work in these industries. A Data Analyst position is more suitable for those who want to start a career in analytics. A Data Scientist position is recommended for those who want to build advanced machine learning models and use deep learning techniques to help humans perform tasks.
-
Who makes more money, data scientists or data analysts?
Data Scientist is one of the highest-paid professionals in the industry.
-
Which is in higher demand, data analysts or data scientists?
The demand for data scientists varies by industry, but when we look at overall demand, the number of data analyst job roles is much higher.
-
Is it easier to learn data science or data analytics?
Becoming a Data Scientist takes more effort than becoming a Data Analyst. If you are skilled in statistics, mathematics, and programming, Data Science is the field for you. Working toward becoming a Data Analyst is a wise decision if you want to get a head start and have time to practice programming.
-
What is the difference between data scientists and data analysts?
A data scientist can predict the future based on past patterns, whereas a data analyst simply curates meaningful insights from data.” “A data scientist’s job involves estimating the unknown, whereas a data analyst’s job involves looking at the known from new angles.”
-
Can a data analyst advance to the level of a data scientist?
Because there is some overlap between the roles of a data analyst and a data scientist, a data analyst may be able to transition into a data scientist. Everyone’s path is unique but gaining relevant data science skills and continuing education is a common step.
