Salary Comparison Data Analytics and Data Science After Online MBA

Online MBA learners usually search this because they want a clear picture of earning potential before choosing a specialisation. The truth is simple. An Online MBA can help you move faster into business facing roles. Your salary still depends more on skills, projects, and prior experience than the online MBA program on your resume.

Quick Salary Snapshot in India (Role-wise Benchmarks)

These are market benchmarks for the roles people typically target after an Online MBA with an analytics or data focus. They are not “MBA only” salaries.

Role	Track	Typical pay range	Central benchmark
Data Analyst	Data Analytics	₹4.32L to ₹10L	Avg ₹6.6L
Business Analyst	Data Analytics	₹5.5L to ₹13.72L	Avg ₹9L
Data Scientist	Data Science	₹9.88L to ₹23L	Avg ₹15L
Machine Learning Engineer	Data Science	₹8L to ₹23L	Avg ₹15L
Analytics Manager	Analytics leadership	₹15.63L to ₹35L	Avg ₹25L
Data Science Manager	Data Science leadership	₹27.05L to ₹43L	Avg ₹34.9L

Data Analytics vs Data Science After an Online MBA

Data Analytics: Data Analytics roles are closer to business reporting and decision making. Companies hire for SQL, dashboards, and stakeholder handling. An Online MBA helps because you can talk about metrics, ROI, and business outcomes.

A common landing path looks like this
• Data Analyst
• Business Analyst
• Product Analyst
• BI Analyst

Data Science: Data Science roles can pay more, but companies test deeper. They look for Python, statistics, modelling, and real projects. Many candidates get filtered out if they only have theory.

Salary Comparison by Career Stage

Online MBA learners usually fall into two buckets. Freshers trying to enter analytics, and working professionals trying to switch or grow. Here is a practical way to read the numbers.

Early career

If you are starting out, Data Analytics roles are more accessible. The India level averages sit around ₹6L to ₹9L for analyst and BA roles.
Data Science offers exist at this stage, but the bar is higher and many roles are mislabeled as “data science” while the work is mostly analytics.

Mid career

With a few years of experience plus an Online MBA, you often shift into business owning roles. Analytics Manager ranges reported by Glassdoor go up to ₹35L in the typical band.
In Data Science, mid career growth depends heavily on production level work and shipping models, not just notebooks.

Senior and leadership

Leadership salaries become more about team impact, revenue influence, and ownership. Data Science Manager benchmarks in India show a typical band that starts near ₹27L.

Why the pay gap happens

Data Analytics pay is tied to decision velocity

Analytics teams drive business decisions, but the work is often easier to scale. Many companies can hire larger analytics teams. That keeps averages moderate.

Data Science pay is tied to specialised depth

Data Science and ML roles often need stronger coding, maths, experimentation, and sometimes deployment skills. Supply is lower for people who can do end to end work. This pushes the upper band higher.

What actually moves your salary up

These factors usually matter more than the specialisation name.

• Company type: Product companies and strong GCCs often pay more than mass service roles.
• Your proof of work: Projects with clear business impact beat generic course certificates
• Tools depth: Advanced SQL and a strong dashboard portfolio can beat weak Python
• Domain knowledge: BFSI, retail, supply chain, marketing analytics all reward context
• Location and role scope: Metro hiring bands are usually higher, and hybrid roles often pay more

Planning to pursue an online master's in data science?
Explore our complete guide to Online MSc in Data Science from Amity University

Skills Checklist for Better Shortlisting

If you choose Data Analytics

• Advanced SQL and joins, windows, performance thinking
• One BI tool at a strong level, Power BI or Tableau
• Metrics design, funnels, cohorts, retention, CAC and LTV basics
• Excel speed, but not Excel dependence
• Clear communication and stakeholder updates

If you choose Data Science

• Python for data and modelling
• Statistics for inference, not just formulas
• ML foundations and model evaluation
• Feature engineering and experimentation mindset
• Ability to explain model choices in simple business terms

Which One Should You Choose

A simple decision table

What you want	Better fit
Faster entry and quicker interviews	Data Analytics
More business facing work	Data Analytics
Higher long term ceiling if you build depth	Data Science
Enjoy coding and modelling daily	Data Science
Prefer dashboards and decision making	Data Analytics
Want leadership track in analytics teams	Either, based on role scope

Conclusion

Data Analytics and Data Science both offer strong growth after an Online MBA, but they work like two different career tracks. Data Analytics is usually a faster entry path. It suits learners who want business facing roles and enjoy working with metrics, dashboards, and decision making.

Data Science can offer a higher salary ceiling, but the screening is tougher. It suits learners who are comfortable with coding, statistics, and building models through real projects. The best choice is the one that matches your background, learning style, and the kind of work you want long term. If you pick the right track and build the right proof of work alongside your Online MBA, your chances of landing a better role improve significantly.

Related Articles

FAQs