Data science vs data analytics: What’s the Difference?

If you are learning Advanced Excel or already working with data in your job, then you must have heard these two terms again and again — Data Science and Data Analytics.
Most people think both are the same, but actually, they are not.

Both work with data, yes, but the way they use data and the goals are very different.
Let’s break it down in a very simple way, without heavy technical language.

What Is Data Analytics?

Data Analytics is mainly about looking at existing data and understanding what is happening.

In simple words, data analytics tries to answer:

“What happened?” and
“Why did it happen?”

Simple Example:

Suppose a company has sales data for the last 1–2 years.
A data analyst will:

• Clean that data

• Use Excel or Power BI

• Make reports and dashboards

• Check which product is selling more

• Find why sales went down in some months

This is data analytics.

Common Work in Data Analytics:

• Cleaning messy data

• Making reports

• Creating dashboards

• Finding trends

• Helping business teams make decisions

Tools Mostly Used:

• Advanced Excel

• SQL

• Power BI / Tableau

• Google Sheets

• Sometimes basic Python

That’s why people say Advanced Excel is the base of Data Analytics, and it’s true.

What Is Data Science?

Data Science is a little more advanced.
Here, the focus is not only on understanding past data but also on predicting future results.

In simple language:

Data Science answers:
“What can happen next?”
“How we can predict it using data?”

Simple Example:

Using past sales data, a data scientist can:

• Predict next month or next year’s sales

• Forecast demand

• Build recommendation systems

• Detect fraud automatically

So here, data is used to build models, not just reports.

Common Work in Data Science:

• Writing code

• Creating prediction models

• Machine learning

• Handling large data

• Automating decisions

Tools Used:

• Python or R

• Machine learning algorithms

• Statistics

• Big data tools

• Cloud platforms

Data Science vs Data Analytics: Main Differences

Skills Required

For Data Analytics:

• Advanced Excel (very important)

• SQL basics

• Business thinking

• Data visualization

• Logical mindset

You don’t need to be very technical here.

For Data Science:

• Python or R

• Strong statistics

• Machine learning concepts

• Problem solving

• Coding comfort

Honestly speaking, Data Science is not beginner-friendly for everyone.

Career Options

Data Analytics Roles:

• Data Analyst

• Business Analyst

• MIS Executive

• Reporting Analyst

• Power BI Developer

Data Science Roles:

• Data Scientist

• Machine Learning Engineer

• AI Engineer

• Data Engineer

Salary Comparison

Yes, data science pays more, but the learning curve is also higher.

Which One Is Better After Advanced Excel?

This question comes from almost every Excel learner.

If you already know Advanced Excel, then Data Analytics makes more sense as the next step.

Why?

• Excel is heavily used in analytics jobs

• Less coding pressure

• Faster job readiness

• Used in almost every industry

Data Science can come later, once you are comfortable with analytics and data thinking.

Who Should Choose Data Analytics?

You should go for Data Analytics if:

• You are from a commerce, management, or non-technical background

• You like working with Excel and reports

• You want a practical career path

• You want quicker entry into data roles

Who Should Choose Data Science?

Choose Data Science if:

• You like coding

• Maths does not scare you

• You want to work with AI or ML

• You are ready to invest time in learning

Final Thoughts

Data Science and Data Analytics both are both good careers, but they are not the same at all.

• Data Analytics is about understanding data

• Data Science is about predicting the future using data

For most people starting with Advanced Excel, Data Analytics is the safer and smarter option.
Later, moving into Data Science becomes much easier.

FAQs

Is coding compulsory for Data Analytics?

No, not at the start. Advanced Excel and SQL are enough to begin.

Can I move from Data Analytics to Data Science later?

Yes, many professionals do that step by step.

Is Advanced Excel still useful today?

Indeed, Excel remains widely used in analytics roles.