Data Science and Data Analytics are the highly demanding fields of artificial intelligence in 2025. In this article we will be exploring Data Analytics vs Data Science and also understand what are these.
What is Data Analytics?
Data Analytics is a field where you are converting the raw data into useful or actionable insights. Data Analytics covers basic skills to perform analytics includes python programming language, knowledge of statistics is required. The process is simple which covers collection of data then clean the unstructured data, post process is transformation of data.
What is Data Science?
Data Science is little advanced field in comparison to data science because data scientists work on complex machine and deep learning algorithms to predict & train models.
Data Science is a highly demanding field among working professionals and require skills like Python Programming Language, Machine Learning, Deep Learning, Statistics, Gen AI and many more.
Data Analytics vs Data Science (Salary Differences)
There are three levels that we are covering in the table given below i.e, entry level, mid level and senior level.
| Experience Level | Data Analyst Salary (INR per annum) | Data Scientist Salary (INR per annum) |
|---|---|---|
| Entry-Level (0–2 years) | ₹4 – ₹8 LPA | ₹6 – ₹10 LPA |
| Mid-Level (3–5 years) | ₹8 – ₹15 LPA | ₹10 – ₹20 LPA |
| Senior-Level (5+ years) | ₹15 – ₹30 LPA | ₹20 – ₹35 LPA |
Data Science vs Data Analytics is most demanding career also in the field of AI nowadays. We are looking here for the number according to different cities in the form of tabular representation.
| City | Data Analyst Salary (INR) | Data Scientist Salary (INR) |
|---|---|---|
| Bangalore | ₹12.5 – ₹15 LPA | ₹12.5 – ₹15 LPA |
| Mumbai | ₹10.5 – ₹13 LPA | ₹10.5 – ₹13 LPA |
| Chennai | ₹9.5 – ₹12 LPA | ₹9.5 – ₹12 LPA |
| Pune | ₹9.0 – ₹11.5 LPA | ₹9.0 – ₹11.5 LPA |
| Hyderabad | ₹9.0 – ₹11.5 LPA | ₹9.0 – ₹11.5 LPA |
| Kolkata | ₹7.0 – ₹9.5 LPA | ₹7.0 – ₹9.5 LPA |
Data Analytics vs Data Science: Key Differences
| Aspect | Data Science | Data Analytics |
|---|---|---|
| Definition | A multidisciplinary field focused on extracting insights using advanced methods | The process of examining data to draw conclusions based on existing data |
| Objective | Predict future trends, build models, and make data-driven decisions | Analyze historical data to discover patterns and insights |
| Key Techniques | Machine Learning, Deep Learning, AI, Predictive Modeling | Descriptive & Diagnostic Analytics, Basic Statistics |
| Tools Used | Python, R, TensorFlow, PyTorch, Hadoop, Spark | Excel, SQL, Tableau, Power BI, Python/R |
| Data Type | Structured, semi-structured, and unstructured data | Mostly structured data |
| Complexity | High – involves advanced math, statistics, and programming | Moderate – requires strong understanding of data and visualization tools |
| Outcome | Predictive and prescriptive insights | Descriptive and diagnostic insights |
| Programming Requirement | High – Python, R, SQL, etc. | Moderate – mainly SQL and visualization tools |
| Career Roles | Data Scientist, Machine Learning Engineer, AI Specialist | Data Analyst, Business Analyst, BI Analyst |
| Industry Applications | AI product development, recommendation systems, fraud detection | Sales analysis, marketing reports, operational efficiency |
FAQs (Data Analytics vs Data Science)
What is the main difference between data science and data analytics?
- Data Analytics focuses on interpreting and analyzing historical data to provide actionable insights. It often involves tasks like data cleaning, reporting, and descriptive analytics.
- Data Science, on the other hand, uses advanced algorithms, statistical models, and machine learning techniques.
Which field has more job opportunities data science vs data analytics?
Data Science tends to have more high-demand job opportunities due to its advanced techniques. The growing need for predictive insights in businesses across various sectors is required.
Can I transition from data analytics to data science?
Yes, many professionals transform their career from Data Analytics to Data Science. All depends upon skills for the transitions and skills required like Python, ML, DL to start your career in this field.