A banner image titled, 'Data Scientist vs Data Analyst, Which is the better career path in India?' shows two data analyst professionals working on datasets displayed on the desktop. They analyze the data through pie charts' and bar graphs'.

Data Scientist Vs. Data Analyst: Know The Ultimate Career Difference

By Sonalisha Mohanty Category Data Science Reading time 4.5 min Published on Apr 12, 2023

Data Analyst vs. Data Scientist: An Extensive Guide for Aspirants With Career Transition Plan!

India is struggling with economic uncertainties due to the post-pandemic and recession condition. The economic uncertainties have affected the job market with the threat of unemployment. The challenges like sudden layoffs and seasonal unemployment crises restrict future resilience. Despite economic imbalances in India, 2022 witnessed 16% hiring growth in the tech industry.

The data analyst vs. data Scientist is gradually becoming the center of attraction as it welcomes people not only from tech but also from the non-tech sectors. With basic computing skills, anyone can join this industry and stated job roles with a higher probability of career rise. Thus, data scientist and data analyst jobs are in demand lately.

The key advantage of these two roles is that they are not limited to the core IT sector. Rather every single industry, starting from BFSI to e-commerce to healthcare.

But what are the differences between a data scientist and a data analyst?

Many aspirants are hunting across the internet to get the answer.

Let us first understand the specialties of data analysts and data scientists in the job market. This blog will further help in understanding roles and responsibilities.

A data science professional seated in front of a desktop analyzes the 'line graphs' and 'charts.' A few overlapping figures show a pie chart, two line graphs, and a bar graph in the background.

What is Data Science? And How does it shape up Data Scientist's job role?

Data science is a field of study that helps professionals extract valuable data insights for understanding business processes. A business consists of several datasets and disciplines that are complex.

It combines maths, statistics, data visualization, programming, data collection, and AI knowledge with expertise to extract insights from the data. Data science professionals use these skills and tools to visualize business conditions. This role helps businesses in having better insights.

Hence, data scientists are the lifeline of any industry that not only predicts uncertainties but also addresses them through a situational output.

Let us read more about the data scientist role and the responsibilities that help businesses grow.

Interested in understanding the broader view of data science? Read the blog What is Data Science? | Uncovering The Explicit Truth

Who are Data Scientists?

Data Scientists are the professionals who help the business study both structured and unstructured data for insightful gains. They source raw and large amounts of data, extract actual meaning, and communicate with the business teams. It makes data gathering and data visualization easier.

Related read: Top Data Visualization Tools Every Data Scientist Must Know in 2023

As we know, businesses include several ambiguous pieces of data and information. Lack of understanding leads to massive failure. Data scientists play a pivotal role in transcribing unstructured data into valuable information.

Smart business decisions require the professional use of analytical skills, programming skills, and statistical model competencies. Data interpretation becomes easier when AI/ML tools are applied.

What is Data Analysis? And How does it shape up Data Analyst's job role?

Data analysis is a systematic process of using available data, cleaning, and analyzing the patterns to have future predictions. It focuses on data optimization.

Data analysis may take the form of descriptive, prescriptive, inferential, textual, or predictive analysis. However, the purpose is to help with optimized decisions for business benefits.

It formally takes up skills like Python, SQL, and SAS and makes a professional utilize them progressively for data insights.

The data analyst role and responsibilities are shaping the IT industry in India. So who are they?

Who are Data Analysts?

Data analysts are the experts who extract, analyze, and translate valuable meaning from the unstructured datasets sourced. Their purpose is to optimize business and generate better profits.

They are professionals who believe in reading through data sources, enhancing operations, minimizing risks, and improving business performance.

The data analytics market in India projects growth of the $118.7 billion mark by 2026. Several industries like HR, healthcare, and finance look forward to data analysts for enhanced decisions.

Data analysts' and Data Scientists' job roles may seem identical to professionals', but it has different applications in the software industry.

Below are a few points that will allow you to understand the distinctive features of Data analyst and Data scientist roles.

Three Distinguishing Features of Data Analyst vs. Data Scientist

Feature#1 - Skills and Responsibilities

Skills and Responsibilities Data Scientist    Data Analyst
Skills Python, Java, SAS, Data Visualization, Hadoop, Machine learning, Deep learning, AI models, Programming, Data Wrangling, Algorithms, Mathematics, Statistical analysis MS SQL Server, Python, Mongo DB, MySQL, Excel, BI tools, Econometrics, Probability & Statistics.
Responsibilities Collect and process the data considering business needs. Collects data from different sources and filters it.
Unstructured datasets initiate data mining. Uses SQL to write up findings and queries.
Use of AI models, statistics, and ML tools for data analysis. Design bar charts and graphs, helping the business visualize.
Visualizing the data sets for optimal model creation Datasets are studied to point out complex patterns.
Business communication with stakeholders designs models to deploy. Fixes business meetings with teams and managers on visualization findings.
Automation of the datasets with the use of ML/AI algorithms. The exploration of datasets starts with basic statistical knowledge.

Feature#2 - Salary range in India

Considering career growth, data analyst vs. data scientist salaries in India carries upward-trending professional careers. However, the depth of responsibilities affects their salary range in India.

Glassdoor states the average data scientist's salary ranges around ₹12,00,000 per annum. The minimum basic pay ranges around ₹5,00,000-₹6,00,000 per annum, and the maximum goes towards ₹25,00,000 per annum.

Similarly, a review by Glassdoor also suggests that a data analyst's average salary range is ₹10,00,000 per annum. The minimum basic pay ranges around ₹5,00,000 per annum, and the maximum goes towards ₹14,00,000 per annum.

An illustration shows a data analyst holding a laptop in his hands. The background shows several figures that indicate the salary and future growth of the mentioned job role.

Feature#3 - Basic eligibility criteria

There are no advanced skills required to become a data scientist or data analyst. Only you need industry-grade upskilling with the required level of statistical, programming, and analytical skills.

Qualification requirements for Data Scientist: An individual with a bachelor's degree in computer science or statistics specialization can fulfill this requirement with the added advantage. However, a basic understanding of applied mathematics and ample interest in coding is enough eligibility to start.

Qualification requirements for Data Analyst: For data analysts, the criteria are a bit relaxed. However, one should hold the eligibility of understanding (or learning) the basics of statistics. Although core programming is not always needed, a basic understanding of the same is always recommended. Moreover, candidates with analytical tools knowledge (like advanced Excel) can easily grab data analytical skills.

So, in brief, any graduate can opt for a data scientist or data analyst role followed by a proper data science certification course or a data analytics certification course with hands-on project opportunities.

Concluding Remarks

Data scientist vs. data analyst is the hottest discussion now in the career discussion. In fact, both offer ample scopes to the techies and mainly non-techies toward successful career transformation. A professional who wants to pursue a career in analytics chooses a data analyst job role, and a professional who wants to go with machine learning, deep learning, and AI in the future can choose a data scientist job role.

I must say, the Data Science vs. Data Analytics course/roles complement each other. So based on their interest and passion for statistics and coding, candidates can choose either one.

Where to get the right guidance?

For a data analyst role, register for Data Analytics Program. With interactive learning sessions and project handling, non-technical professionals get benefits.

For a future-proof data scientist role, register for Advance Data Science and AI Program or Data Science & AI for Managers (in case you hold 7+ years of work experience in any field).

So here, we may easily conclude that a data scientist and data analyst's job choice entirely depends on the professional's opinion. However, in case you have ample statistics and coding knowledge data scientist role seems to be the more promising. For other data, analysts are a good option to start from. But later, they can also switch to the data scientist role with continuous skill upgradation. Anyway, keep in mind there is no one-size fit solution for the data analyst vs. data scientist career battle.