Full-Stack Data Science & AI Course 2026: Syllabus, Fees, Duration, Eligibility, Salary & Placement
The world is moving fast, and the demand for professionals who can make sense of data and apply artificial intelligence (AI) to solve business problems has never been higher. If you’re thinking about enrolling in a “Full-Stack Data Science & AI” course in 2025, you’re asking a smart question. But to get the most out of it whether you’re a fresh graduate, a career-changer, or someone aiming for better placement outcomes you’ll want clarity on the syllabus, investment (fees + time), eligibility, likely salary, and how realistic placement becomes.
Here’s the full, humanised breakdown suited for India (Hyderabad-centric), aligned with placement-outcome-focused training (just like NareshIT does).
Why this course matters now (2025 context)
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Data and AI are no longer niche. Organisations across industries (IT services, product companies, e-commerce, healthcare, BFSI) are leveraging data science, machine learning (ML) and AI for real business value. For example, the salary article shows AI data scientists in India earning around ₹9.5 LPA and above for early roles.
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The term “full-stack data science & AI” implies end-to-end ability: from data ingestion to model building to deployment, not just “analysis”. This breadth increases your employability and aligns with placement-ready outcomes.
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For learners in India who are placement-oriented (i.e., you want a job, not just a certificate), the course needs to cover practical projects, portfolio building, real tools and placement support.
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With so many “data science” courses out there, you must pick a programme with clarity on value, stack, tools, projects and hiring outcomes.
What does “Full-Stack Data Science & AI” typically include?
“Stack” here means covering multiple layers of the data science & AI pipeline: data acquisition/cleaning → analytics → machine learning/AI → deployment (production) → sometimes cloud/devops/ML-ops. Based on syllabus outlines from Indian programmes:
Sample syllabus components
From various sources, we can synthesise a full-stack data science & AI syllabus. For example:
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Programming + fundamentals: Python, SQL, statistics.
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Data manipulation & visualization: Pandas, NumPy, Tableau/PowerBI, EDA.
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Machine Learning & Deep Learning: Regression, classification, clustering, neural networks, computer vision, NLP.
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Big Data / Cloud / Deployment / MLOps: Hadoop/Spark, AWS/Azure/GCP, Docker/Kubernetes, CI/CD for data pipelines.
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Generative AI / Agentic AI (in newer 2025 versions) – e.g., syllabus that include Gen AI + Agentic AI modules.
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Capstone Projects / Portfolio Building / Interview Prep: Real-world case studies, hands-on projects, placement readiness.
Example Syllabus Layout
Here’s a sample breakdown you might see in a “Full-Stack Data Science & AI” course:
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Module 1: Orientation & Essentials – Python fundamentals, Git/GitHub, Excel/SQL basics
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Module 2: Data Manipulation & Visualisation – Pandas, NumPy, Tableau/PowerBI, EDA
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Module 3: Statistics & Probability – Descriptive stats, inferential stats, hypothesis testing
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Module 4: Machine Learning – Supervised/Unsupervised algorithms, model evaluation, feature engineering
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Module 5: Deep Learning & AI – Neural networks, CNNs, RNNs, NLP, Transformer models
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Module 6: Big Data & Cloud – Hadoop, Spark, working with large datasets, AWS/Azure basics
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Module 7: Deployment & MLOps – Docker, Kubernetes, CI/CD pipelines, monitoring, model deployment
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Module 8: Generative AI & Agentic AI – Prompt engineering, LLMs, ethical AI, real-world applications
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Module 9: Industry Projects & Portfolio – Multiple projects across domains (e.g., healthcare, e-commerce, finance), deploy apps, GitHub + live demo
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Module 10: Placement Readiness – Resume/LinkedIn optimisation, mock interviews, hiring partner prep, salary negotiation
Many programmes show a similar curriculum. For example, one full stack data science syllabus mentions Cloud Platforms + Big Data + DevOps modules.
Fees & Duration – What to expect in India (2025)
Duration
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Typical durations for full-stack courses range from 6 to 12 months in India depending on depth and whether it's part-time or full-time.
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Some accelerated bootcamps may be shorter (e.g., 4-6 months) but may demand intense pace and fewer modules.
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If you want full stack + AI + deployment + job-market readiness, expect something closer to 9-12 months for high quality. (For example, a full-stack data scientist master programme in India indicates 9 months.
Fees
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Fees vary widely: For Data Science & AI courses in 2025 India the fee range could be ₹50,000 to ₹3,00,000+ depending on brand, placement support, duration.
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Example: A full stack AI-powered full stack development course by upGrad: ₹1,83,000 for 9 months.
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Another online model: “Data Science Course with Placement Guarantee” – ₹28,000 for fewer months.
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So for a comprehensive full-stack + AI + placement course you might expect anywhere between ₹75,000 to ₹2,00,000+, depending on institution, scope, live/online/hybrid, inclusion of cloud tools, internships and placement assistance.
What to budget
If you're planning for Hyderabad/India market:
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Budget: Aim for ₹70,000-₹1,50,000 as a decent mid-tier investment for full stack data science & AI programme with placement support.
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Time: 9 months (roughly) to 12 months to complete and reach placement readiness.
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Investment pay-back: Depending on your salary uplift, placement assistance, your ROI should be considered.
Eligibility – Who can enrol?
Eligibility criteria tend to vary but for full stack data science & AI courses you’ll commonly find the following:
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Bachelor’s degree (any discipline) or final year student. Some courses may accept 12th + proven programming aptitude.
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Basic mathematics/statistics comfort (linear algebra, probability) is often required or taught as part of the programme.
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Basic programming skills: many courses expect Python/SQL knowledge or provide a foundation module.
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Some courses may require qualifying aptitude test or interview for batch selection. For example, one programme noted: fill application form → eligibility test → offer letter.
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For freshers or career-changers, having willingness to commit time & work on projects is important.
In your context (Hyderabad, placement-oriented), make sure your learners have access to the lower-level modules (foundation Python/math) and good support to bridge any gaps.
Salary & Placement – What you can realistically expect
One of the most important metrics for you and your learners is: what salary can one expect after such a programme? And how does placement support matter?
Salary expectations (India, 2025)
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For entry-level data science roles: According to Simplilearn, entry-level data scientists earn between ₹3,00,000 to ₹7,00,000 per annum.
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More recent upGrad article: Data scientist salaries in India for freshers 0-2 yr experience ~ ₹4-7 LPA; mid-level ~ ₹9-15 LPA; senior (5+ yrs) ~ ₹20-30 LPA+.
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For full stack data scientist with AI skillset: One source: Full Stack data scientists salary approx ₹6,98,412 per annum in India (for early levels).
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For AI Data Scientists roles: Average salary ~ ₹13.3 LPA/year in India according to upGrad.
Placement and role outcomes
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The actual salary depends on stack, domain (product vs service), location (Hyderabad vs bigger metro), experience, project work, and placement support.
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If your training provider offers placement tie-ups with hiring partners (Infosys, Deloitte, Capgemini, Tech Mahindra, etc) and helps with portfolio + interview prep, then you stand better chance at higher salary bands.
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For freshers in Hyderabad, good placement could mean salaries in range ₹4.5-8 LPA with strong portfolio, skills and good company.
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As you gain 2-3 years experience with full stack + AI skillset, you could target ₹12-20+ LPA depending on employer.
What helps you improve salary placement outcomes
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Strong hands-on portfolio projects (end-to-end full stack + AI)
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Real domain exposure (e.g., e-commerce, healthcare analytics, FinTech)
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Deployment & production skills (not just model building)
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Communication/interview readiness + resume/LinkedIn optimization
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Placement assistance and hiring partner network in your training institute
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Learning newer trending modules (Generative AI, Agentic AI, MLOps) which increases value
Placement-Oriented Use-Case (For a Training Provider Like NareshIT)
Let’s illustrate how you could design your programme or position this course to your learners (freshers, Hyderabad market) for maximum outcome:
Use-case: A student completes the Full-Stack Data Science & AI course, builds 3 projects:
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E-commerce customer segmentation + recommendation engine (Python, SQL, Tableau)
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Health-care predictive model for readmission risk (Python, Scikit-learn, TensorFlow)
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Deployed web app with backend + AI model (Flask + AWS + Docker + React front-end)
Outcome:
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Student includes these projects in their portfolio, with GitHub links + live demo
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In resume they highlight measurable outcome: “Reduced churn by 18% in model trial”, “Built REST API for model deployment with AWS Lambda”
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Placement team arranges interviews with hiring partners (Infosys, Deloitte etc) – owing to the full stack + AI skillset they secure an entry-level role at ₹5.2 LPA in Hyderabad.
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Within 2-3 years they progress to ₹12 LPA by adding product domain experience, cloud skills and generative AI modules.
This is aligned with the kind of placement narratives you emphasise (alumni success, salary benchmarks, partner companies) in your brand.
FAQs
Q1. What exactly is a “Full-Stack Data Science & AI” course and how is it different from “Data Science” alone?
A “Full-Stack Data Science & AI” course covers the entire pipeline not just data cleaning + modelling, but also front-end (sometimes), deployment, cloud/Big Data, MLOps, and often AI (deep learning, NLP, generative models). A plain “Data Science” course might focus more on analytics, ML and less on deployment/production integration. If you want a job in which you can build end-to-end solutions (not just analyse data), full stack is more valuable.
Q2. Do I need prior programming or maths experience?
You don’t always need to be an expert when you start, but you should be comfortable with basics of mathematics (algebra, probability) and willing to learn programming (often Python). Many programmes include foundation modules for beginners. If you start zero, expect extra effort to catch up.
Q3. Is the investment (fees/time) worth it?
Yes, if you treat the course as investment and ensure: high-quality curriculum (stack + AI + deployment), multiple hands-on projects, placement assistance, effective learning. If just theory + no projects + no job support, ROI will be weak. Given salary bands and demand, the investment can pay off in 1-2 years if you land a good job.
Q4. What kind of jobs will I get after completing this course?
Potential roles include: Junior Data Scientist, Full Stack Data Scientist, ML/AI Engineer, Data Engineer, Analytics Consultant, Business Intelligence Analyst (with full-stack mix). The “stack + AI” skillset opens routes into more advanced roles than pure analytics.
Q5. What salary can I expect as a fresher?
In India in 2025: entry-level roles for data scientists may range ~ ₹4-7 LPA (for good institutes) and for full stack data science & AI roles maybe slightly higher if you bring deployment + cloud skills. With a strong programme + portfolio you could aim ~ ₹5-8 LPA+ in Hyderabad/India. As experience builds, you can move into ₹12-20+ LPA range. See salary sources.
Q6. How should I choose the right course/institution?
Key criteria:
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Curriculum covering full stack + AI + deployment + MLOps
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Hands-on projects (stack + real-world)
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Placement support (hiring partners, mock interviews, resume help)
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Credible institution or brand with alumni outcomes
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Cost and duration within your budget/time
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Tools/technologies up-to-date (e.g., generative AI, cloud, big data)
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Flexibility (online/hybrid) if you’re working or switching career
Q7. How can I make sure I get placed?
Placement readiness tips:
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Build at least 2-3 end-to-end projects you can talk about
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Use GitHub + live demo links in your resume
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Tailor your resume & LinkedIn profile to highlight your stack + AI skills + results
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Practice mock interviews (technical + behavioural)
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Target hiring partners actively recruiting data/AI talent
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Leverage the training institute’s placement support and network
Q8. After completing the course, what’s the next step for career growth?
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Gain 1-2 years experience in the job, ideally deployment/data product role
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Up-skill in trending areas: generative AI, agentic AI, MLOps, cloud native, domain specialisation (e.g., health, FinTech)
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Work on larger, impactful projects (e.g., production-level ML systems)
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Focus on leadership skills or domain expertise for salary jump
Q9. Can someone without a CS background do this course?
Yes, many programmes are designed for non-CS/engineering graduates too, provided you’re willing to learn programming + maths and put in the effort. Look for courses that include foundational modules or bridging content.
Q10. Are online courses as good as classroom ones for this field?
They can be, provided they offer live sessions, hands-on labs/projects, mentorship, placement support and interaction. In many cases online has the advantage of flexibility. The key is to verify they deliver the “full-stack + AI + deployment + placement” promise not just theory. For a structured path, explore our comprehensive Data Science with AI course.
Final Thoughts
If you’re serious about stepping into data & AI in 2025, a Full-Stack Data Science & AI course can be a powerful enabler but it’s only as good as how you use it. The certificate alone won’t guarantee a job. What will matter more is:
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Strong practical skills (stack + AI + deployment)
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Projects you can show (portfolio)
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Ability to articulate your work and value in interviews
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Placement support and alignment with hiring partner expectations
With the right programme, duration, investment and mindset, you’re positioning yourself for one of the high-growth tech lanes especially in India. From a training-and-placement perspective (like NareshIT), ensure your programme is tightly aligned to the job market: specify the syllabus, emphasise deployment/real-world use-cases, tie it to placement benchmarks (salary, companies), and build modules on portfolio + interview readiness.
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