- July 21, 2025
- BTech Data Science
Are you curious to know the difference between data science and data analytics? They both look at the data but serve different purposes: one predicts, while the other describes. To pursue careers in any of these fields, it is important to understand the terms and choose one yourself.
This guide will explain to you what is data science and what is data analytics; how they differ, and what criteria you need to take into account when choosing between them. Also, we will discuss the B.Tech in Data Science course at Universal AI University, which is an AI-based degree program in Mumbai.
What is Data Science?
Understanding the question of what Data science is has to be your priority. Data Science is a domain that integrates elements from a multitude of different academic fields. It is made up of statistics, computer science, algorithms, and, of course, domain expertise used to reveal the hidden knowledge from huge and complex datasets, which can be both structured and unstructured.
Machine learning, natural language processing, and big data frameworks are the ones that data scientists employ to generate models capable of making predictions. They articulate the data, whether it be from a billboard about the number of people driving cars or an increase in traffic accidents, to what solutions are available for the health sector to improve its financial situation.
What is Data Analytics?
Data analytics is a slightly different process from data science. The major focus of a data analyst is to look after existing, structured data to get actionable insights and support the decision-making process.
To visualize and summarize the performance, data analysts use different tools, including Dashboards, SQL, Excel, and Power BI. The major goal of an analyst is to describe the past. This includes answering questions like “what happened?” and “why did it happen?” by looking at the trend of previously taken data.
Difference Between Data Science and Data Analytics
The data science and data analytics difference is primarily defined by their goals, analysis, and predictions, alongside the tools and data involved. To understand the difference between Data Science and Data Analytics, below is a brief table discussing the factors like scope, career, purpose, and more.
| Feature | Data Science | Data Analytics |
| Scope | Broad, exploratory, and predictive | Focused, descriptive, action-oriented |
| Purpose | Ask new questions, build models, and forecast trends | Analyze what happened, explain the causes, and guide decisions |
| Data Types | Structured and unstructured (text, images) | Primarily structured databases, logs, spreadsheets |
| Tools & Skills | Python/R, machine learning, AI, statistical modeling | SQL, Excel, BI tools, basic scripting |
| Outcome | Predictive models, algorithms, and automation | Reports, dashboards, insights, trend summaries |
| Career Path | Data Scientist, ML Engineer, Research Scientist | Data Analyst, BI Analyst, Reporting Analyst |
How To Choose a Career Between Data Science and Data Analytics
1. Understand Your Interests
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- Data science is a great choice for those who like to build and play with models based on AI and machine learning technologies.
- Data visualizations and dashboards, along with data sequence summarization, are the tools that can help you fit into data analytics.
2. Evaluate Required Skills & Learning Curve
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- As a data scientist, the demand for strong programming, statistics, and algorithmic thinking is very high.
- Data analytics requires high-end skills in SQL, Excel, and BI tools, and a basic knowledge of statistics.
3. Consider Career Path and Salary
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- Data scientists are generally paid higher, around ₹12–20 LPA+.
- Any fresher data analyst can get up to ₹6–12 LPA.
4. Explore Hybrid or Incremental Paths
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- Starting as a data analyst and switching to data science via the training is a classic route that many of the experts prefer. The starting point based on this idea is to acquire skills from practical experience.
5. Look at Educational Programs
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- Opt for programs in which one can have a firm base in the two fields or the possibility to tailor their course, for example, a B. Tech in Data Science Studies, from the top university in Mumbai which is a cross of a bachelor’s and master’s degree program with analytics, machine learning, and AI.
Why a B.Tech in Data Science at Universal AI is Ideal
With the graduates being proficient in the necessary skills, it is very easy for them to move down the career path as data analysts and even reach the next step in their journey by stepping into advanced data science roles in machine intelligence, big data, or corporate analytics.
Universal AI’s 4‑year B.Tech in Data Science in Mumbai provides state-of-the-art instruction in these subjects. It is a dual focus on data science and analytics. The study plan consists of:
- A curriculum that spans multiple disciplines is characterized by the integration of computer science, statistics, AI, ML, and data analytics into a single program.
- To gain practical knowledge, the institutions focus on students doing internships, projects in labs, and industrial placements with companies.
- The firm has a solid backing with certification from AICTE and UGC, respectively. It has been mentioned in the list of the best universities in Mumbai for AI/DS courses.
Conclusion
To sum it up, the main difference between data science and data analytics is the degree of concentration of each domain. The analytics discipline interprets the historical data, and the data science field predicts what will come in the future.
Both data analytics and data science are important and can be used together very easily.
Before choosing your career option, ask whether you are the kind of person who wishes to build predictive models with the aid of AI and machine learning, or you would rather learn ways to analyze data in driving business decisions.
Whatever your response may be, it will be of great value towards pursuing a successful career in an environment driven by data.
Eager to develop autonomous systems with AI? The B.Tech in Data Science might be the degree for you. On the other hand, if you would rather be the one to aid decision-making with dashboards and reports, the path of data analytics might be the right choice for you.
FAQs
Which is better: data science or data analytics?
Depends on your preference: data science is ideal for those who love modeling and forecasting; data analytics suits those who enjoy summarizing insights and reporting.
Who gets paid more, a data analyst or a data scientist?
Data scientists usually earn more due to advanced skills in machine learning and modeling, though their salaries vary by experience and industry.
Will AI replace data analysts?
AI automates routine analysis, but human involvement is essential for extracting context, setting goals, and validating results. Analysts will evolve, not disappear.
Which field of data science is best?
Specialties in AI/machine learning, NLP, deep learning, or big data engineering are highly sought-after, with strong job growth and salary potential.
Does data analytics require coding?
Basic scripting (e.g., SQL, Python, R) is useful. However, visualization tools like Power BI and Tableau often reduce the need for extensive coding.
