AI is transforming careers, with Applied Data Science focused on insights and decision-making, and Applied AI & ML focused on building intelligent systems and machine learning solutions.
Choose Applied Data Science if you enjoy finding business insights, working with data, creating visualizations, and using SQL or statistics. Choose Applied AI & ML if you prefer coding, building intelligent systems, and developing machine learning models.
Applied Data Science is ideal for professionals interested in statistics, business analytics, experimentation, and data-driven decision-making across functions.
Applied AI & ML suits professionals who enjoy building models, working with deep learning and generative AI, and engineering AI systems end to end.
Similarities: Both programs build strong foundations in statistics, programming, machine learning, and applied problem-solving, with growing overlap in generative and agentic AI.
Top recruiters for both fields include Deloitte, EY, Accenture, TCS, Amazon, HDFC Bank, ICICI Bank, Infosys, Tata Group, and Reliance.
Data Science's future scope is growing through causal AI, business experimentation, AI copilots, and agentic analytics.
AI & ML's future scope is expanding through generative AI, agentic AI, RAG systems, and enterprise-grade AI deployment.
Both specializations build strong analytical, technical, and problem-solving skills applicable across industries.
Not sure whether Applied Data Science or Applied AI & ML is right for you? Let's compare both and help you decide.
Applied Data Science vs Applied AI & ML: Which Program Is Right for You?
Confused between Applied Data Science and Applied AI & ML? Here's a quick breakdown of both before we get into the details.
Applied Data Science
The subject, Applied Data Science, has been designed for you to learn how to turn business data into decisions via the use of statistics, experiments, visualization, and modeling. In effect, this subject gives you the required skills to formulate the right questions, to test the right hypotheses, and to draw the correct inferences from the results.
Applied AI & ML
Applied Artificial Intelligence & Machine Learning prepares you for designing building, training and deploying AI applications through Machine Learning, Deep Learning, Natural Language Processing and Generative AI.
Which One Should You Choose?
Select EPGC in Applied Data Science if you like analyzing data and making decisions based on your analysis, and if you like dealing with numbers and finding patterns in data.
Select EPGC in Applied AI & ML if you like building and designing systems, and if you like getting deep into models, deploying them, and understanding AI infrastructure.
Both tracks have great job opportunities, and more and more they converge due to generative and agentic AI. The point is to know which one you prefer - working with decision-making using data or building smart systems.
In Detail: What Is Applied Data Science?
Curious what an Applied Data Science classroom actually looks like week to week? Here's the subject-by-subject breakdown.
Course Overview
Applied Data Science teaches you how to turn data into decisions using statistics, experimentation, and AI-native analytics tools. You learn to frame business problems, run the right experiments, identify true causality, and direct AI systems to scale up your judgment.
This program suits professionals who think analytically and want to lead data-driven initiatives across business functions.
Core Subjects
The Applied Data Science program establishes its base with modules such as:
Machine Learning and Statistics, Data Engineering and SQL, AI Data Stack, Data Visualization & Business Analytics, AI Storytelling, Experimentation & Causal Inference, Predictive Modeling, Generative AI & AI Copilots, Analytics Automation, Agentic AI & RAG, Responsible AI
Skills Developed
By the completion of this specialization, you will have developed job-ready skills including:
Statistical thinking, experimentation & A/B testing, predictive modeling, data storytelling, AI copilots, agentic analytics workflow, business decisions.
Are you someone who cannot resist asking “why” about a move in a metric? This is for you!
In Detail: What Is Applied AI & ML?
Wondering what's actually behind building and deploying an AI system? Here's the subject-by-subject breakdown.
Course Overview
Applied AI & ML provides training on how to go from the basics of AI to its practical applications, including machine learning, deep learning, NLP, Generative AI, etc. You will learn how to design and deploy AI models confidently.
Applied AI & ML is perfect for those professionals who want to apply their knowledge on modeling, deployment, and AI infrastructure.
Core Subjects
Applied AI & ML track has laid its foundation on modules such as:
AI Foundation Bridge (Python, SQL, Statistics, Linear Algebra, Probability, Calculus), Machine Learning Foundations, Deep Learning & Computer Vision, Natural Language Processing (NLP), Transformers & Speech AI, Generative AI & LLM applications, RAG systems & LLM orchestration, Agentic AI, MLOps & deployment, AI governance
Skills Developed
By the end of this specialization, you will have gained practical skills, ready to apply on the job, such as:
AI Model Building using ML/Deep Learning, Natural Language Processing & Transformers, Generative AI Application Development, RAG Systems Design, Agentic AI Workflows, and Production Deployment/MLOps
If creating and launching an AI system really gets you excited, this is your starting point.
Applied Data Science vs Applied AI & ML: Key Differences
Both programs seem interesting, but which of them should be your choice? We need to find out more about the difference between them.
What to Compare | Applied Data Science | Applied AI & ML |
Focus | Using data and experimentation to guide business decisions | Building and deploying intelligent AI systems |
Curriculum | ML & statistical foundations, data engineering, business analytics, causal AI, GenAI copilots, agentic analytics | AI foundations, ML & deep learning, NLP & transformers, generative AI, RAG systems, agentic AI & MLOps |
Core Skills | Statistical reasoning, experimentation, forecasting, business storytelling | Model building, deep learning, system design, AI deployment |
Tools & Technologies | SQL, Python, BI and visualization tools, AI copilots, RAG-based BI systems | Python, deep learning frameworks, NLP/transformer tools, deployment and MLOps stacks |
Programming Requirements | Working knowledge of Python and SQL is helpful; strong statistical thinking matters more | Comfort with programming logic; foundational math (algebra, probability, statistics) required |
Applications | Business forecasting, A/B testing, causal analysis, AI-powered reporting | Computer vision, NLP, generative AI applications, deployed AI APIs |
Career Paths | Data Analyst, Data Scientist, Analytics Manager, Analytics Director | ML Engineer, AI Engineer, Deep Learning Engineer, AI Architect |
Ideal Learner Profile | You like data, curiosity, and connecting insights to business outcomes | You like building systems and want to engineer AI end to end |
Once you have found the key differences between these two courses, it will not be difficult for you to choose between Applied Data Science and Applied AI & ML.
Similarities Between Applied Data Science and Applied AI & ML
Before you pick a side, know this: the two paths aren't as far apart as the comparison table makes them look.
Both ensure a strong foundation in machine learning, statistics, and programming basics.
Both are offered by IIT Kharagpur’s Department of Artificial Intelligence and are 100 percent live online programs.
Both will make you think logically and solve real-world problems.
Both now consider generative AI and agentic AI as a must-have skill set.
Both have a capstone project and portfolio building integrated into them.
Both are aimed at working professionals and ensure career progression.
If you specialize in either, you'll walk away with the same strong technical foundation. The specialization simply determines where you choose to apply it.
Applied Data Science vs Applied AI & ML: Career & Salary Comparison
Are you wondering which of the two is more Potential? Check out the growth of salaries for Applied Data Science and Applied AI & ML here.
Applied Data Science:
Junior Data Analyst / Data Scientist: The annual salary of a Junior Data Analyst / Data Scientist in India can vary anywhere between ₹5-8 lakhs per year.
Data Scientist: The annual income of a Data Scientist with around 3-6 years of experience can vary anywhere between ₹10-22 lakhs per year.
Senior Data Scientist: The annual salary of a Senior Data Scientist with more than 7 years of experience can vary between ₹20-35 lakhs.
Applied AI & ML:
AI / ML Engineer / Junior AI Engineer: The expected salary range for junior ML engineers or AI engineers is generally ₹6-9 lakhs annually in India depending upon their technical skills and organization.
ML Engineer: For mid-level ML Engineers having 3-6 years' experience in the field, the average salary may be ₹11-17 lakhs annually, and above that in some cases depending on their specialization.
Senior ML Engineer: For senior ML Engineers having at least 7 years' experience, the expected salary may be ₹20-32 lakhs annually depending upon their specialization, industry, and responsibilities.
Leadership Positions: For leadership positions such as Analytics Director, Chief Data Officer, AI Architect, and Head of AI, the salary range may vary from ₹40 lakhs to ₹1 crore+ annually, depending on the organization.
In the end, both Applied Data Science and Applied AI & ML can offer strong earning opportunities. What matters most is how you build your skills and grow within your chosen field.
(Source : Glassdoor)
Applied Data Science vs Applied AI & ML: Career Opportunities & Top Recruiters
Having a number is fine, but this is your job title, which will show up on your business card. This is how the hiring process will look for both tracks.
Career Roles in Applied Data Science
Applied Data Science is a strong fit for people who enjoy analyzing data, running experiments, and connecting insights to business strategy.
Role | What You'll Do |
Data Analyst | Clean, analyze, and visualize data to support day-to-day business decisions. |
Data Scientist | Build predictive models and translate data into actionable business recommendations. |
Business Analytics Consultant | Design experiments and forecasting models to solve business problems. |
Senior Data Scientist | Lead analysis on complex problems and mentor junior analysts. |
Analytics Manager | Oversee analytics teams and align data initiatives with business goals. |
AI-Powered Analytics Lead | Build and manage AI copilots and agentic analytics workflows. |
Chief Data Officer | Set the data and analytics strategy at an organizational level. |
All of these are the positions that require making the data and models work for your company or product. No matter what position you start in, whether it is data analyst or machine learning engineer, you might influence the AI strategy of your entire company someday.
Career Roles in Applied AI & ML
For individuals drawn to building and deploying intelligent systems, Applied AI & ML opens doors to a wide and fast-growing range of engineering careers.
Role | What You'll Do |
ML Engineer | Build, train, and evaluate machine learning models for real-world use cases. |
AI Engineer | Design and integrate AI applications, including generative and agentic AI features. |
Deep Learning Engineer | Develop computer vision or NLP models using deep learning architectures. |
NLP/GenAI Engineer | Build applications using transformers, LLMs, and RAG-based systems. |
MLOps Engineer | Deploy, monitor, and maintain AI systems in production environments. |
AI Architect | Design end-to-end AI system architecture across an organization. |
Head of AI / AI Consultant | Lead AI strategy, governance, and enterprise-wide adoption. |
All these career paths involve the usage of data and model application leading to business results. You may start from the position of a data analyst or machine learning engineer, but in the end, you can affect the way a whole company uses artificial intelligence technology.
H3 Top Recruiters
Both specializations open doors to leading employers across banking, consulting, IT services, e-commerce, and technology sectors.
Sector | Leading Recruiters |
Banking & Financial Services | HDFC Bank, ICICI Bank, Kotak Mahindra Bank, Axis Bank |
Consulting & IT Services | Deloitte, EY, KPMG, PwC, Accenture, TCS |
Technology & Product Companies | Amazon, Flipkart, Microsoft, Google |
Analytics & IT Services | Capgemini, Infosys, HCLTech, Cognizant |
Large Corporates | Tata Group, Reliance Industries, Aditya Birla Group, Mahindra Group |
Whether you go for any of the specializations, you will be meeting with similar recruiters. It is not about the companies that you will be applying to; it is more about how you can perform the job.
Applied Data Science vs Applied AI & ML: Future Scope
When choosing one of these two subjects, you should keep in mind not just your current interests, but also the future prospects for each of these areas. The reason is that Applied Data Science and Applied AI & ML are very promising areas indeed.
Future Scope in Applied Data Science
The world of business analytics is changing fast, creating opportunities for people who combine data expertise with AI fluency.
Development of AI co-pilots and agentic analytics
The rise in demand for causal AI and experiment skills
Increasing use of generative AI for business reporting and forecasting
Data governance and responsible AI becoming more relevant
Recruitment in BFSI, consulting, IT, and e-commerce strong
In other words, data science is no longer just about analyzing numbers. It's about directing AI systems to make smarter, faster business decisions.
Future Scope in Applied AI & ML
Applied AI & ML sits at the center of how modern companies build products and systems, and demand for this skill set is growing across nearly every industry.
Usage of generative AI and large language models has increased
More engineers are required to develop and deploy AI agents
Higher adoption of RAG and LLM orchestration systems within enterprises
Higher focus on MLOps, deployment, and AI governance
High hiring within the domains of technology, IT and consulting, BFSI, and products
The engineers who stand out won't just know how to train a model. They'll be the ones who can design, deploy, and govern AI systems that move a business forward.
Looking Ahead
At the end of the day, both Applied Data Science and Applied AI & ML have a lot to offer in today's changing business world.
The best choice depends on your interests, but building strong technical and analytical skills will help you succeed in either field.
So, choose the path that excites you and keep upgrading your skills to stay ready for the future
Why Choose IIT Kharagpur Online for Applied Data Science and Applied AI & ML?
The course is only half the equation. Where you learn changes everything else. Both programs are designed and delivered by the Department of Artificial Intelligence, IIT Kharagpur, India's first IIT, ranked 5th in Engineering by NIRF 2025.
100% live online teaching: All classes are taught live by IIT Kharagpur professors, guaranteeing depth, clarity, and academic excellence.
Capstone project in tune with industry: Solve a practical business or engineering problem, from its formulation all the way to deployment of the solution.
Graduate with an Executive Certificate and on-campus ceremony: The programme culminates with an on-campus graduation ceremony held at IIT Kharagpur, where the participants graduate with their certificates from the Programme Director and Institute of leadership.
Superior academic quality: Taught by faculty from the Department of Artificial Intelligence and Department of Computer Science and Engineering, IIT Kharagpur.
Tailored for working professionals: IIT Kharagpur’s Applied AI & Machine Learning is a 8-month-long programme, while IIT Kharagpur’s Applied Data Science & Agentic AI is a 7-month-long programme.
Lean towards Data Science, lean towards AI & ML, or lean towards none. IIT Kharagpur programmes provide a solid foundation for the age of agentic AI.
Conclusion
Are you someone who enjoys uncovering insights from data or building intelligent systems with it? If experimenting with data, business analytics, forecasting, and data-driven decision-making interest you, Applied Data Science can lead you toward roles like Data Scientist, Analytics Manager, and future leadership positions in data. But if you enjoy building models, deploying AI systems, and working with machine learning and generative AI, Applied AI & ML can open doors to roles like ML Engineer, AI Engineer, and AI Architect.
The best choice depends on what excites you and matches your career goals and existing strengths. Choose Applied Data Science if you enjoy solving business problems through data and turning insights into actionable decisions or choose Applied AI & ML if you like developing and deploying intelligent systems from end to end.