What is the difference between a data analyst and a data scientist?

Analysts answer "what happened and why" using SQL, dashboards and statistics. Data scientists answer "what will happen" using machine learning. The salary gap reflects the complexity — analyst offers in India start near ₹5 LPA, data scientist offers near ₹8 LPA, going up to ₹18 LPA for production-grade ML candidates.

What is the average data scientist salary in India in 2026?

Our 2025 cohort's offers ranged from ₹6 LPA (fresher at a service company) to ₹18 LPA (mid-senior at Razorpay / Fractal). Median was ₹9.5 LPA. With 3 years of experience, ₹22–28 LPA is reachable in Indian product companies.

Is a B.Tech in CS required to become a data scientist?

No, but a math-heavy degree (Stats, Math, Physics, Economics, Engineering of any branch) makes Module 1 easier. We have placed students from B.Sc and M.Sc backgrounds at Goldman Sachs and Bank of America.

Will I learn machine learning AND deep learning?

Yes — both. Module 2 covers classical ML (regression, trees, boosting), Module 3 covers deep learning in PyTorch (CNNs, RNNs, transformers). We deliberately do both because most Indian DS roles still hire on tabular ML.

How rigorous is the math?

Heavy first two weeks (linear algebra, probability, calculus intuition), then applied throughout. We assume class 12 mathematics. You will not need to derive backprop on paper, but you will understand it.

Do you teach Generative AI / LLMs?

Module 3 covers transformer fundamentals and HuggingFace fine-tuning, but for deep agentic AI and RAG systems, see our Gen AI & Agentic AI program. Many graduates take both.

Will I get an AWS or Azure certification?

The course aligns with AWS Machine Learning Specialty content, but the certification itself is a separate ₹15,000 exam paid to AWS. We prep you well enough to pass on the first attempt — about 70% of our students who take it do.

Can I do this course while working full-time?

Yes. Live sessions are 3 evenings a week (7–9pm IST) plus weekend office hours. Plan for 10–12 hours per week. About 60% of our DS cohort are working professionals.

What is the placement support like after the course?

90-day intensive support — weekly check-ins, mock interviews, recruiter introductions to our 50+ hiring partners, and resume + LinkedIn rewrites. If you are still searching after 90 days, we extend support at no cost.

How does this compare with Scaler or upGrad data science courses?

We are honest about the differences. Scaler and upGrad have huge brand recognition and big batches; we are deliberately small (15 per cohort) and India-product-company focused. Our placement rate of 85% is comparable or higher, and our 90-day support is unconditional.

High Salary

Data Science

Name: Data Science
Price: 45000 INR
Availability: InStock

Four months of production-grade ML. Math foundations to PyTorch to AWS MLOps. End-to-end capstone reviewed by hiring managers.

PyTorchAWSMLOpsXGBoostPython

4 months ₹6–18 LPA Max 15 students

Program at a glance

Duration: 4 months
Cohort size: Max 15 students
Placement rate: 85%
Salary range: ₹6–18 LPA
Format: Live online + Hyderabad lab
Certificate: Verifiable + GST

Try a free masterclass firstSee enrolment options ↓

Next cohort: 30 May 202615 seatsLimited to 15 students 4.9 rating

Why this program, for the Indian market

Data science in India in 2026 is no longer a "learn pandas + sklearn" job. The bar has moved. Hiring teams at Razorpay, Fractal, Tredence, Mu Sigma and global capability centres in Hyderabad now expect candidates who can train, deploy and monitor models — not just train and forget. They expect MLOps hygiene, AWS or GCP fluency, and the judgement to choose XGBoost over a deep network when the data calls for it.

The Nettms Data Science program is built for that bar. Four months. Math foundations, classical ML, deep learning, and full production MLOps — taught by ML engineers who currently ship models in Indian product companies. Cohort capped at 15. Each student builds an end-to-end capstone (data ingestion → training → deployment → monitoring) that lives in their GitHub and gets reviewed by our hiring panel before recruiter calls start.

You leave with a portfolio that opens doors: a trained PyTorch model deployed to SageMaker, a Streamlit demo, a write-up that frames the business problem, and the LinkedIn story that converts a recruiter glance into an interview. We focus on Indian hiring realities — the live coding rounds, the system-design questions for ML, and the case studies that separate a senior offer from a fresher one.

Graduates have been placed at

$fractal$
$fractal$

· and 50+ more partners across India, UAE and the UK

Who this is for

Built for these candidates

B.Tech / B.E. / M.Sc graduates targeting a ₹8 LPA+ first DS role.
Software engineers (Java, .NET, support) wanting to switch into ML within 6 months.
Analysts ready to level up from dashboards to predictive modelling.
PhD / research candidates who can do the math but have never deployed a model.

Prerequisites

What you need on day one

Class 12 maths required. Linear algebra and probability are covered in Module 1.
Basic Python is preferred but not required — Module 1 has a fast-track Python primer.
Comfort with a laptop with 8 GB RAM (16 GB recommended for deep learning weeks).
Willingness to invest 10–12 hours per week for 4 months.

Live cohorts · 15 students max

Built around projects, not lectures.

What you'll learn

Every outcome here is a thing hiring managers test for. Not a slide you'll see once and forget — a skill you'll build in a project.

Train and deploy XGBoost models to production on AWS SageMaker.
Build deep learning models in PyTorch — CNNs, RNNs, transformers from first principles.
Set up MLOps pipelines with MLflow, DVC and GitHub Actions.
Write unit + integration tests for ML code, not just notebooks.
Use AWS S3, Lambda, ECR and SageMaker confidently in a production setting.
Run hyperparameter tuning at scale with Optuna or Ray Tune.
Frame an ML problem business-first — when not to use ML at all.
Master feature engineering for tabular data (the skill XGBoost interviews test).
Build a Streamlit demo for every model — recruiters love clickable proof.
Pass the live coding + system design rounds at Razorpay, Fractal, Tredence and 50+ partners.
Read and reproduce a recent NeurIPS or ICML paper.
Write a project README that hiring managers actually finish.

Free · 90 minutes · live

See how we teach before you enrol

Join our free "Cracking the DS Interview" masterclass — 90 minutes with a mentor who currently ships ML at Razorpay. No upsell pitch; just real interview problems worked end-to-end.

Reserve a free seat →

Curriculum · Program • DS-ML

A complete program, module by module.

12 modules · 4 Months · 16 Weeks · 120+ Hours · Beginner to Advanced.

A rigorous, project-driven program that takes you from the mathematical foundations of machine learning through to advanced deep learning, reinforcement learning and transformer-based NLP. You will build real systems across healthcare, finance and e-commerce, and graduate ready to operate as a Data Scientist or ML Engineer at any modern organisation.

Duration
4 Months · 16 Weeks
Hours
120+ Hours
Level
Beginner to Advanced
Tools & stack
Python, TensorFlow, PyTorch, scikit-learn, Hugging Face

Linear algebra, calculus, probability theory and statistics — the language of every ML and NLP algorithm. Build the intuition that lets you read papers and debug models, not just call libraries.

Prefer to read the full brochure offline? Use the Download curriculum button above — we'll send a one-time code to your phone and unlock the PDF instantly.

Who should enroll

Designed for ambitious learners.

Students and graduates aiming for data science and ML engineering roles
Working professionals upskilling into AI-first roles
Software engineers transitioning into machine learning
Researchers and academics applying ML to their domain
Anyone serious about building a career in modern AI

Real outcomes, not just hours

Why this program.

Strong mathematical core
We teach the math, not just the API calls.
Cutting-edge curriculum
Transformer-era content, not 2018 leftovers.
Multi-domain projects
Healthcare, finance and e-commerce case studies.
Industry-grade tooling
TensorFlow, PyTorch and the modern MLOps stack.
Research-quality capstone
A portfolio piece you can defend in any interview.
Career coaching & referrals
Direct lines into our hiring partner network.

Indicative salary by role · India 2026

From classroom to offer letter.

Junior Data Scientist₹6.0 – 10.0 LPA
ML Engineer₹8.0 – 15.0 LPA
NLP Engineer₹9.0 – 18.0 LPA
Data Scientist (Mid)₹12.0 – 22.0 LPA
Senior ML Engineer₹18.0 – 30.0 LPA
Lead / Principal Scientist₹25.0 – 45.0 LPA

Placement support

Dedicated placement cell with 100% assistance
Resume, LinkedIn and GitHub portfolio coaching
Mock technical interviews on ML theory and coding
Direct referrals across 200+ hiring partners
Domain interview tracks (healthcare, finance, e-commerce)
Research paper reading and case-study practice

Indicative annual compensation, India market • 2026

Tools you'll master

The exact stack, with the why behind each choice

Python
numpy, pandas, scikit-learn — the daily-driver stack.
PyTorch
Deep learning framework Indian product companies have standardised on.
XGBoost
Still the most asked-about ML library in Indian DS interviews.
AWS SageMaker
Train and deploy at scale — covered the way hiring teams test.
MLflow
Experiment tracking and model registry that production teams use.
HuggingFace
Transformers, fine-tuning, datasets, evaluators.

Capstone projects

Real projects, real datasets, public portfolio

01Credit-risk scoring model deployed as a SageMaker endpoint with monitoring.
02Churn prediction for a Tier-2 SaaS company, with a SHAP-explained dashboard.
03Demand forecasting for a D2C brand using time-series transformers.
04NLP fine-tune for Indian-language customer support classification.

Where the work continues

From cohort to production team in 60–90 days.

Career outcomes

Where graduates work — and what they earn

Salary range (India)

₹6–18 LPA

Placement rate

85%

Avg. time to offer

75 days after graduation

Roles you can target

Data Scientist
Machine Learning Engineer
Applied Scientist
ML Ops Engineer
Research Engineer
AI Engineer
Decision Scientist

Cities hiring now

Bengaluru
Hyderabad
Pune
Gurgaon
Mumbai
Chennai

Hiring partners

Razorpay
Fractal
Tredence
Goldman Sachs
Bank of America
Accenture
PwC
Deloitte
TCS
Infosys
GlobalLogic
LatentView

Why students pick Nettms for Data Science

01
MLOps is taught the way production teams use it — not the textbook way.
02
Cohort capped at 15 — your capstone gets weekly mentor reviews.
03
Math primer is calibrated — heavy enough to understand, light enough to finish.
04
Hiring panel includes engineers from Razorpay, Fractal and Tredence.
05
90-day placement support continues after graduation, no extra fee.

Upcoming cohorts

Batch	Starts	Mode	Seats left
Data-Science-Batch-2	30 May 2026	hybrid	7 of 15	Enrol

Mentors

Nettms Admin

Mentor

Frequently asked questions

The questions students from Hyderabad, Bengaluru, Pune, Mumbai and 30+ cities ask before enrolling.

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Investment & enrolment

Three flexible ways to join the next cohort

No-cost EMI, instant referral discount, and a 7-day no-questions-asked refund. Every payment includes a GST invoice and lifetime access to recordings.

Option 1 · One-time

₹45,000

Pay once, lock the seat. Save 5% with a referral code at checkout.

Enrol with one-time payment

No-cost EMI

Option 2 · 3-month EMI

₹15,000/mo

Zero interest. Auto-debited monthly. Major Indian banks supported via Razorpay.

Start no-cost EMI

Option 3 · Referral

Save ₹15,000

Any current Nettms student can refer you. The discount applies instantly at checkout — no codes to chase.