What is the Difference Between Data Science and Data Analytics?

Data science is a multidisciplinary field that uses statistical methods, programming, machine learning, and domain expertise to extract insights and build predictive models from structured and unstructured data. It involves everything from collecting raw data to developing algorithms and deploying data products. A data scientist doesn't just analyse data; they model complex scenarios, make predictions, and create tools that can automate data-driven decision-making. Their work often leads to the creation of machine learning models, recommendation engines, and forecasting systems.

Key responsibilities:

Tools used: Python, R, SQL, TensorFlow, PyTorch, Jupyter, Apache Spark, Git

The lifecycle of a data science project involves several distinct phases:

Data science emphasises innovation, automation, and future forecasting based on historical patterns.

Data analytics, on the other hand, focuses more on processing and performing statistical analysis on existing datasets to find actionable insights. It is typically retrospective, helping organisations understand what happened and why it happened. Analytics is more concerned with interpreting existing data to identify trends, summarise insights, and support business decision-making. It’s less focused on predictions and more on optimisation and insight generation.

Key responsibilities:

Tools used: Excel, Tableau, Power BI, SQL, Python (Pandas), R, Google Analytics

Data analytics can be broken into four main categories:

Data scientists may operate across all four levels, but data analysts usually focus on descriptive and diagnostic analytics. When building complex data processing systems, understanding Python inheritance concepts becomes crucial for creating modular, reusable code that can be extended and maintained efficiently.

The skill sets for both domains overlap but diverge in-depth and focus.

For Data Scientists:

For Data Analysts:

While data scientists build systems that predict and learn, data analysts explain, monitor, and optimise performance.

Data Scientist Roles:

Data Analyst Roles:

The data scientist role is generally more technical, requires stronger programming and modelling skills, and commands a higher salary. However, data analytics is more approachable and essential in nearly every business sector. According to the U.S. Bureau of Labor Statistics, the demand for data science roles is expected to grow by 36% from 2023 to 2033, much faster than the average for all occupations.

Choose Data Science if:

Choose Data Analytics if:

Still unsure? Start with data analytics, build a strong foundation in data handling, and gradually progress into data science training as your skills evolve.

So, are data science and data analytics the same? No, while they are related fields within the data domain, they serve different purposes and require different skill sets. Understanding the difference between data science and data analytics can help organisations build stronger teams and help individuals make more informed career choices. Whether you're exploring data science or data analytics, which is better depends on your interests and long-term goals. Analytics can be a great entry point, while data science offers broader challenges and higher impact through automation and AI.

For aspiring professionals seeking to establish a strong foundation, consider enrolling in structured learning programs that offer hands-on projects and practical experience. At Bhrighu Academy, our expert-led curriculum is tailored to help you master both disciplines through real-world data science projects, analytics case studies, and industry tools. Join Bhrighu Academy today and chart your career in data with confidence.

Do data scientists and data analysts use the same tools?

Data scientists and data analysts share some standard tools, such as SQL, Python, and Excel, but differ in their depth of usage and application. Analysts often rely on visualization platforms like Tableau and Power BI, while data scientists use machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn. The overlap exists, but data scientists typically work with more advanced programming, modeling, and big data tools than analysts.

What kind of data do these fields work with?

Data analysts primarily work with structured data from relational databases, spreadsheets, or BI tools to identify trends and support business decisions. Data scientists handle both structured and unstructured data, including text, images, and sensor data, using advanced techniques for prediction, classification, and automation. Their work often involves handling larger, more complex datasets across diverse sources.

Is data analytics a subset of data science?

Yes, data analytics is often considered a subset of data science. While data science encompasses a broader spectrum, including machine learning, data engineering, and predictive modeling, data analytics focuses on interpreting historical data to uncover patterns and insights. Analysts support business decisions through analysis, whereas data scientists may develop algorithms and systems for forecasting or automation.

Which career has higher earning potential?

Data science typically offers higher earning potential due to its advanced technical requirements, including machine learning, programming, and big data handling. According to Glassdoor, Data scientists in the US often have average salaries between $100,000 and $150,000. However, salaries depend on experience, industry, location, and skill set, with both fields offering lucrative and growing career paths.

Can data analytics lead to a career in data science?

Yes, a career in data analytics can serve as a strong foundation for transitioning into data science. Analysts gain essential skills in data cleaning, interpretation, and visualization, which are fundamental to data science. By learning programming, statistics, and machine learning, professionals can expand into data science roles. Many data scientists begin their careers as analysts and upskill over time.