Data analysts study historical data. Data scientists find new data. Data analysts answer known questions, while data scientists find the right questions. Choosing between data analytics and data science is one of the most common career decisions for people who are interested in working with data.
Both paths let you solve problems, influence decisions, and build a high-impact career. Both involve working with numbers, tools, and teams. But they are different in tools, skills, learning approach, career opportunities, and scope.
This article will explain the real difference between data analytics and data science, the difference in career opportunities, what professionals in each role actually do, skills and tools needed, and finally, you can choose the right one that aligns with your interests and skills.
What is Data Analytics?
Data Analytics focuses on the interpretation of existing data (historical data) to get meaningful insights and support to make current decisions. Data Analytics works with structured data and applies statistical data to solve problems and answer questions. It involves cleaning, organising, and analysing structured datasets, creating reports or dashboards, visualising trends, and helping stakeholders act on the findings.
At Techolas, our Data Analytics course teaches you the foundational tools, such as SQL, Excel, Power BI, Tableau, and Python, to help you become proficient in turning data into actionable business intelligence.
What Does a Data Analyst Do?
Data Analytics works with the business team to collect, organize, and interpret data to make decisions. They use tools like Excel, SQL, Power BI, and Tableau to analyze trends, prepare reports, and visualize key insights. Data analysts mostly use structured data and work closely with marketing, finance, and operations teams to track performance metrics, forecast outcomes, and optimize business strategies. Their role bridges the gap between raw data and actionable business intelligence.
What is Data Science?
Data Science is a broader term that includes data analytics, data mining, machine learning, artificial intelligence, and other complicated information to predict the future and generate new questions. Data science begins with defining business goals and progresses through data collection, cleaning, and analysis. Using programming, machine learning, and statistical models, data scientists build algorithms to extract insights, which are finally presented through visualizations and dashboards.
At Techolas, our Data Science program trains you in Python and R scripting, machine learning, big data tools, and model deployment so you can handle end-to-end data workflows and create futuristic solutions.
What Does a Data Scientist Do?
A data scientist focuses on deeper data exploration and prediction through machine learning, statistical modeling, and programming using tools like Python, R, and TensorFlow. They build algorithms, train predictive models, and uncover complex patterns hidden within large datasets. Data scientists often collaborate with leadership teams to develop AI-driven solutions, automate decision-making, and create data strategies that drive innovation and growth.
Key Differences Between Data Analytics and Data Science
Features | Data Analytics | Data Science |
Focus | Interprets historical and current data to get actionable insights for decision-making through Data Analytics | Predicts future outcomes, builds models, and discovers new questions in Data Science |
Type of Data | Data Analytics uses primarily structured data with moderate complexity | Data Science mostly deals with unstructured data with higher complexity |
Tools Used | Excel, SQL, Power BI, Tableau, Python, R | Python, R, big-data frameworks like Spark and Hadoop, ML libraries |
Skills Required | Data cleaning, reporting, dashboards, business context | Statistical modelling, machine learning, programming, and domain expertise |
Learning Approach | Data Analytics highlights practical, business-driven projects, with a focus on visualization and reporting tools. | Data Science involves advanced programming, data engineering, and model development with real-world datasets. |
Outcomes | Data Analytics produces reports, dashboards, and insights for business decisions. | Data Science develops models and systems that predict future trends or automate decision-making. |
Career Opportunities | Data Analyst, Business Analyst, BI Analyst, Reporting Analyst. | Data Scientist, ML Engineer, AI Researcher, Data Engineer. |
Scope | The scope of Data Analytics is low compared to Data Science | The scope of Data Science is high |
Who can study | Data Analytics is ideal for beginners, graduates, and professionals with logical or business backgrounds. | Data Science is suitable for those with a strong foundation in mathematics, statistics, and programming. |
Career Opportunities & Growth
Both Data Analytics and Data Science jobs are in high demand in the market, but it is different across industries.
Data Analytics are needed for almost every business sector, including marketing, operations, finance, and retail, to help companies track performance, improve efficiency, and make decisions based on current data.
Common roles include Data Analyst, Business Intelligence (BI) Analyst, Reporting Analyst, and Operations Analyst. In India, entry-level analysts typically earn ₹3.5–6 LPA, and with experience and domain skills, salaries can rise to ₹7–10 LPA+. Techolas’s placement assistance and project-based training ensure you’re job-ready for these roles.
Data Scientists are needed in tech, healthcare, and finance, where organizations depend on AI, machine learning, and advanced modeling to drive innovations as part of long-term strategies.
Typical roles include Data Scientist, Machine Learning Engineer, AI Research Associate, and ML Ops Specialist. Entry-level salaries often begin around ₹6–12 LPA, with potential to grow significantly beyond ₹12–20 LPA+ as experience and expertise increase. At Techolas, our advanced curriculum and live projects prepare you for these high-growth roles.
Core Skills and Tools Needed for Data Analysts and Data Scientists
Data Analyst Skills
- Proficiency in SQL, Excel, and visualisation tools like Power BI or Tableau
- Ability to interpret business metrics and build dashboards
- Strong communication skills to translate analytics into business insights
- Working with historical data and answering known questions
Data Scientist Skills
- Strong programming skills, like Python or R, and experience with libraries like scikit-learn or TensorFlow
- Deep knowledge of statistics, machine learning, and big data technologies
- Ability to ask new questions, build predictive models, and work with unstructured data
- Domain knowledge and ability to translate complex models into business-ready solutions
Which Course Should You Choose – Data Analytics or Data Science?
If you are interested in business insights, dashboards, visualisation, and prefer a quicker entry into the data field, then the Data Analytics course at Techolas is a perfect choice for you. It is suitable for fresh graduates, professionals with a business/commerce background, and anyone planning to move into data with less coding.
On the other hand, if you are passionate about programming, statistics, and machine learning, and want to build predictive models and future-focused skills, then the Data Science course at Techolas is the right path. It is well-suited for engineering graduates, tech-savvy individuals, or those willing to invest time in advanced analytics.
Techolas offers both Data Analytics and Data Science, so you can choose anyone. If you’re confused about which is best suited for you, they will help you to choose the best path that aligns with your background, interests, and goals.
Get Started in Data Analytics or Data Science with Techolas
Choosing between Data Analytics and Data Science doesn’t have to be confusing. Both fields offer excellent career prospects, but the right choice depends on your interests, skill set, and ambitions. With Techolas’s industry-aligned curriculum, mentor-led training, real-world projects, and placement assistance, you will get full support no matter which path you choose.
Enroll in either the Data Analytics or Data Science program at Techolas today and start your data-driven career with confidence.
FAQ – Frequently Asked Questions
- Which is better, data science or data analytics?
It depends entirely on your interests and career goals. Choose Data Analytics if you are interested in business insights and visualisation. If you’re interested in programming, modelling, and building predictive systems, choose Data Science as your career path.
- Which pays more – data science or data analytics?
Generally, Data Science roles have higher salaries because they involve more technical and programming skills. However, salary depends on many factors such as company, location, experience, and skills.
- Can a data analyst become a data scientist?
Yes. Many data scientists begin as analysts and then upskill in programming, machine learning, and big data frameworks to switch into data science.
- Can you work from home as a data analyst?
Yes, many data analyst roles, like querying data, building reports, and creating dashboards, can be done remotely using cloud-based tools and secure systems. However, professionals handling confidential or highly sensitive data may be required to work on-site due to strict data security policies.
- Should I learn data analytics before data science?
Learning Data Analytics first can be beneficial if you’re just starting your journey and want to build foundational skills in data handling and visualisation. You can switch into Data Science once you are comfortable with coding and statistical modelling.
