How to Become a Data Scientist in India: Guide 2026
Learn how to become a Data Scientist in India by 2026. Explore the complete roadmap, required skills, top colleges, and salary trends. Read the guide now!
- Starting salary
- ₹6.5-80 LPA a window, not a promise
- Entrance exams
- 4 JEE Main, JEE Advanced, CUET (UG)
- Stream
- Engineering from Class 11
What Does a Data Scientist Actually Do?
A Data Scientist combines statistics, programming, machine learning, and business knowledge to collect, analyze, and interpret large volumes of data. They help companies uncover hidden patterns, solve complex problems, and predict future trends to make smart business decisions.
Your day starts around 9:30 AM by discussing a business problem with stakeholders. By 10:30 AM, you are writing SQL queries to extract data from multiple corporate databases. At 11:30 AM, you move to data preparation using Python, cleaning missing values and fixing errors. Data is rarely clean in its raw form, so this data cleaning takes up a huge part of your working hours. After lunch at 1:00 PM, you run statistical tests and perform exploratory data analysis (EDA) using libraries like Pandas and NumPy to find initial trends.
At 2:30 PM, you build, train, and evaluate machine learning models using Scikit-learn. Around 4:00 PM, you create visual graphs using Matplotlib or Seaborn. At 5:00 PM, you present these visual findings to product managers and business teams, translating complex mathematical outcomes into clear recommendations. Before wrapping up, you spend time learning about new AI papers, Python libraries, or MLOps practices, and collaborating with data analysts or subject matter experts.
A good week means your model trains cleanly, yields high predictive accuracy, and helps a business team launch a feature. A bad week means dealing with corrupted raw data, broken pipelines, biased model predictions, and long meetings trying to explain technical errors to non-technical managers.
Hour by hour
- 9:30 AMUnderstand business problems and define the scope of a project.
- 10:30 AMCollect and query data from various sources.
- 11:30 AMClean and prepare raw data, which often takes a significant amount of time.
- 1:00 PMApply statistical methods and perform exploratory data analysis (EDA) to find trends and patterns.
- 2:30 PMBuild and evaluate machine learning models.
- 4:00 PMCreate data visualizations to present findings clearly.
- 5:00 PMExplain results and insights to stakeholders, translating complex technical findings into business recommendations.
- 6:00 PMEngage in continuous learning by reading research papers and staying updated with new technologies.
- 7:00 PMCollaborate with other professionals like data analysts and subject matter experts.
Who Should Choose This?
Data science suits people who enjoy solving practical puzzles using numbers and computer code. Check how many of these statements sound like you:
You should NOT choose this career if you dislike mathematics, struggle with basic statistics, or hate writing code. It is also not a good fit if you prefer working entirely alone without presenting your work to non-technical business teams.
The Roadmap
- Build Strong Foundations in School Focus on mathematics, probability, and basic computer science during Class 11 and 12. Strong numerical skills form the base of all data algorithms.
- Clear Entrance Exams and Enter College Take entrance tests like JEE Main, JEE Advanced, or CUET to get into an undergraduate program in Computer Science, Statistics, Mathematics, or Data Science.
- Master Core Programming and Databases Learn Python as your primary language during your degree, along with SQL for querying relational databases. Practice data manipulation using Pandas and NumPy.
- Learn Statistics and Machine Learning Study probability, linear algebra, calculus, and core machine learning algorithms using Scikit-learn. Learn how to test hypotheses and evaluate model performance.
- Build a Practical Project Portfolio Apply your skills to public or Indian datasets. Create 3 to 4 real-world projects that perform data cleaning, exploratory data analysis, and predictive modeling, then host them on GitHub.
- Gain Practical Industry Experience Apply for data analyst, research, or junior data scientist internships during your final years of college to understand real business workflows.
- Land an Entry-Level Role Apply for entry-level Data Scientist, Data Analyst, or Associate ML Engineer positions to start your professional career.
- Learn Edge Skills and Advanced Frameworks Expand into Deep Learning, MLOps, model deployment, Natural Language Processing (NLP), and Generative AI workflows to move into mid-level and senior roles.
Start from where you are
The road is different from Class 9 than from a job. Pick your line; the page remembers it.
- Class 11-12Choose the Science stream with Mathematics as a core subject.
This provides the necessary academic background for undergraduate degrees in data science or related fields.
- After Class 12Prepare for entrance exams like JEE Main, JEE Advanced, or CUET.
These exams are crucial for admission to top engineering and science programs that lead to data science.
- 1-2 years after Class 12Pursue a Bachelor's degree in Computer Science, Statistics, Mathematics, or a related engineering field.
This builds the foundational technical and analytical skills required for data science.
- During Bachelor's degreeLearn programming languages like Python and SQL, and develop skills in statistics, machine learning, and data visualization.
These are core skills for any data scientist.
- Final year of Bachelor's or afterBuild a portfolio of real-world projects and consider internships.
Practical experience and a strong portfolio are essential for securing your first job.
Your main task right now is to build a rock-solid foundation in Mathematics and basic Science. Pay close attention to algebra, coordinate geometry, and basic statistics in school. If your school offers computer applications, start learning basic programming concepts early. Your immediate move after Class 10 is to choose the Science stream with Mathematics as a core subject for Class 11 and 12.
You will need about 4 to 5 years after Class 12 to land your first job, which includes completing a 3 or 4-year undergraduate degree. The first major exam you will target is JEE Main or CUET after Class 12. The biggest trap to avoid at this stage is ignoring math to focus only on simple coding tutorials. Data science requires strong mathematical reasoning, so do not skip your school math classes.
- After Class 12Prepare for and appear in entrance exams like JEE Main, JEE Advanced, or CUET.
These exams are gateways to reputable undergraduate programs in relevant fields.
- 1-2 years after Class 12Enroll in a Bachelor's degree program in Computer Science, Statistics, Mathematics, or a related engineering discipline.
This provides the academic foundation and technical skills needed for data science.
- During Bachelor's degreeFocus on developing strong programming skills in Python and SQL, understanding statistical concepts, and learning machine learning fundamentals.
These are the core technical competencies for data scientists.
- During Bachelor's degreeWork on practical projects and seek internships to gain hands-on experience.
A strong project portfolio and practical exposure are critical for job applications.
- Final year of Bachelor's or afterApply for entry-level Data Scientist, Data Analyst, or Associate ML Engineer roles.
These roles serve as entry points into the data science career path.
Ensure Mathematics is one of your core subjects in Class 11 and 12. Your primary focus must be preparing for national or state undergraduate entrance exams like JEE Main, JEE Advanced, or CUET. Aim for a degree program in Computer Science, Data Science, Statistics, or Mathematics at a reputable institute.
You are looking at 3 to 4 years after Class 12 before entering the workforce. Focus on securing admission to a top college where campus placements occur. Learn basic Python syntax on weekends, but keep your primary attention on school exams and entrance tests. The main trap to avoid is spending all your study time taking unstructured online certification courses at the cost of your Class 12 board marks and entrance exam preparation.
- ImmediatelyLearn Python programming (Pandas, NumPy) and SQL for data manipulation.
These are fundamental tools for any data professional.
- 3-6 monthsStudy core statistics, probability, and linear algebra.
These mathematical concepts are the bedrock of data analysis and machine learning.
- 6-12 monthsLearn machine learning algorithms (Scikit-learn) and data visualization tools (Matplotlib, Seaborn).
These skills are essential for building and interpreting models.
- 12-18 monthsBuild a portfolio of 3-4 real-world data science projects, ideally using Indian or public datasets.
Projects demonstrate your practical skills to potential employers.
- 18-24 monthsConsider a Master's degree in Data Science or a specialized PG Diploma, or apply for Data Analyst/Junior Data Scientist roles.
A Master's can provide in-depth training, while entry-level roles offer practical experience.
If you hold a degree in a different stream, assess your current mathematical and analytical background immediately. Start by learning Python for data manipulation using libraries like Pandas and NumPy, alongside standard SQL. Spend your first 3 to 6 months building a strong grasp of linear algebra, probability, and basic statistics.
Over the next 6 to 12 months, learn standard machine learning algorithms and visualization tools like Matplotlib and Seaborn. Dedicate months 12 to 18 to constructing a practical portfolio of 3 to 4 real projects using real-world public datasets. You can land an entry-level role in 1.5 to 2 years through self-study, or 2 to 3 years if you enroll in a formal Master's or PG Diploma program. Avoid the trap of applying for jobs with just a course completion certificate but no live portfolio code on GitHub.
- ImmediatelyStart with online courses to learn Python (Pandas, NumPy) and SQL.
These are the most in-demand programming and database skills.
- 3-6 monthsFocus on understanding statistics, probability, and machine learning fundamentals.
These form the analytical core of data science.
- 6-12 monthsApply your new skills to projects relevant to your current industry or a domain you understand well.
This allows you to leverage your existing domain knowledge and build a relevant portfolio.
- 12-18 monthsNetwork with data professionals and look for internal opportunities to transition into data-focused roles within your current company.
Internal transitions can be easier as you already understand the business.
- 18-24 monthsIf internal transition isn't possible, apply for Data Analyst or Junior Data Scientist roles, highlighting your project portfolio and domain expertise.
These roles are common entry points for career changers.
Begin by mapping out how your current domain expertise can transfer into data science. If you work in finance, logistics, retail, or healthcare, your domain understanding is a massive advantage. Start your transition by taking evening or weekend modules in Python and SQL. Move on to core statistics and machine learning over the next 3 to 6 months.
Apply these skills directly to datasets from your current industry. Try finding data-driven business problems in your current company to solve. You can make an internal transfer or transition into a Junior Data Scientist or Data Analyst role within 1.5 to 2 years. The biggest trap to avoid is quitting your full-time job abruptly to join expensive offline bootcamps. Keep your job, build your technical skills steadily, and look for internal opportunities first.
Entrance Exams
Entrance exams are the main gateway to top undergraduate and postgraduate programs in India. For undergraduate admissions, JEE Main and JEE Advanced open doors to top engineering institutes, while CUET handles admissions to central universities. For postgraduate courses, GATE and CAT are the primary national tests used by top public institutes and university analytics programs.
The exams, with your doors
- Admits to
- B.E./B.Tech, B.Arch, B.Plan in NITs, IIITs, CFTIs
- Who can sit
- Passed Class 12 in 2024, 2025, or appearing in 2026 with Physics, Mathematics, and required technical subjects. Minimum 75% in Class 12 or top 20 percentile.
- When
- January and April (likely)
- Attempts
- 6 attempts (twice a year for three consecutive years)
- Admits to
- B.Tech in IITs
- Who can sit
- Top 2,50,000 rank holders in JEE Main. Passed Class 12 for the first time in 2025 or 2026 with Physics, Chemistry, and Mathematics.
- When
- Once a year (likely May/June)
- Attempts
- 2 attempts in consecutive years
- Admits to
- Undergraduate courses in Central Universities and participating institutions
- Who can sit
- Passed Class 12 or appearing in 2026 from a recognized board.
- When
- May-June (likely)
- Attempts
- No limit on attempts (likely)
- Admits to
- M.Tech, M.Sc, and Ph.D. admissions in engineering and science
- Who can sit
- Bachelor's degree in engineering, technology, architecture, science, arts, or commerce.
- When
- February (likely)
- Attempts
- Multiple attempts (likely)
- Admits to
- MBA and postgraduate management programs in data/analytics
- Who can sit
- Bachelor's degree with at least 50% marks (45% for SC/ST/PwD).
- When
- November (likely)
- Attempts
- Multiple attempts (likely)
Who can apply opens the eligibility page when this site has one for the exam. Plan opens your planner with the exam filled in; you set the date the notification gives.
Top Colleges
Top government colleges like the IITs, IISc Bangalore, and ISI Kolkata offer premier instruction, exceptional faculty, and subsidized tuition fees. Private institutions like BITS Pilani and VIT Vellore offer modern computer infrastructure and strong industry corporate networks, though their total tuition costs are substantially higher. Choose based on your entrance ranks, budget, and long-term placement goals.
Colleges and what they cost
- Course
- B.Tech in Computer Science, M.Sc in Data Science
- How to get in
- JEE Advanced for B.Tech, GATE for M.Sc
- Aid
- Various scholarships for economically weaker sections and reserved categories
- Course
- M.Tech in Computational and Data Science
- How to get in
- GATE
- Aid
- Various scholarships for economically weaker sections and reserved categories
- Course
- B.Tech in Computer Science, M.Tech in Data Science
- How to get in
- JEE Advanced for B.Tech, GATE for M.Tech
- Aid
- Various scholarships for economically weaker sections and reserved categories
- Course
- B.Stat (Hons), M.Stat
- How to get in
- ISI Admission Test
- Aid
- Scholarships based on merit and need
- Course
- B.E. in Computer Science, M.Sc in Business Analytics
- How to get in
- BITSAT for B.E., GATE/BITS HD for M.Sc
- Aid
- Merit-based scholarships, need-based financial aid
- Course
- B.Tech in Computer Science, M.Tech in Data Science
- How to get in
- VITEEE for B.Tech, VITMEE for M.Tech
- Aid
- Merit scholarships
Fees are for the whole course, as published by the college; hostel and mess come on top.
Skills to Build
Foundation
Foundation skills give you the essential mathematical and analytical framework needed to understand how data algorithms work under the hood.
- Mathematics (Statistics, Probability, Linear Algebra, Calculus): Practice core concepts through free online modules on Khan Academy.
- Python Programming Basics: Learn syntax, data structures, and control flow using NPTEL's Python for Data Science course.
- SQL Fundamentals: Learn relational database querying, joins, and aggregations using W3Schools SQL Tutorial.
- Data Cleaning & EDA: Master basic exploratory data analysis techniques using Kaggle Learn data cleaning tutorials.
Core
Core skills are the tools you will use every single day in an entry-level data science job to process data, train models, and visualize trends.
- Python Data Science Libraries (Pandas, NumPy, Scikit-learn): Learn data manipulation and modeling through NPTEL courses.
- Machine Learning Algorithms: Study supervised and unsupervised algorithms by auditing Andrew Ng's Machine Learning course on Coursera.
- Data Visualization (Matplotlib, Seaborn, Tableau): Learn to build visual charts using Tableau Public free tier or Kaggle Learn visualization guides.
- Data Engineering Basics (Git, APIs, Cloud Concepts): Learn code version control and basic APIs using the official Git Handbook.
- Business Framing & Communication: Practice translating raw technical metrics into business case studies through self-study articles.
Edge
Edge skills set you apart from standard applicants, opening up higher-paying roles in specialized artificial intelligence, deep learning, and MLOps deployment.
- Deep Learning (Neural Networks, TensorFlow, PyTorch): Learn deep learning frameworks through official PyTorch and TensorFlow online documentation.
- Natural Language Processing (NLP): Practice text processing using free documentation and guides on spaCy and NLTK.
- Generative AI Workflows (LLMs, RAG, Prompt Engineering): Learn practical AI integration through tutorials on Hugging Face.
- MLOps & Model Deployment (Docker, FastAPI, Streamlit): Learn how to deploy models into application endpoints using FastAPI documentation and Docker guides.
- Big Data Technologies (Apache Spark): Learn distributed data processing using Apache Spark documentation and Databricks Community Edition.
The skills ladder
Tick a skill you already have. The ladder keeps your ticks on this device.
Salary Reality
Data science offers strong earning potential in India, but initial packages depend heavily on your college tier, practical coding skills, and geographic location. The average salary for a Data Scientist in India is approximately ₹33.7 lakhs per year across all experience levels, ranging typically from ₹20.6 lakhs to ₹131.9 lakhs per year based on profile data. High earners in the top 10% draw over ₹61.2 lakhs per year.
| Experience Level | Years of Experience | Average Salary (LPA) | Salary Range (LPA) |
|---|---|---|---|
| Entry Level | 0 - 2 Years | ₹6.5 - 11.5 LPA | ₹4.5 - 12 LPA |
| Mid Level | 4 - 9 Years | ₹12 - 14 LPA | ₹10 - 18 LPA |
| Senior Level | 10+ Years | ₹20+ LPA | ₹18 - 45+ LPA |
Salaries vary noticeably across major Indian tech hubs. Bangalore pays the highest average data scientist salary in India at ₹13 LPA, followed by Mumbai at ₹11.5 LPA, and Chennai and Hyderabad at ₹10.5 LPA.
The salary ladder, in lakh a year
Government institutions and research labs generally follow structured pay scales with moderate starting figures.
Private sector product firms, FAANG labs, and tech startups offer salaries ranging from 14 LPA up to 80 LPA for senior roles.
Average remote data scientist salary ranges from 10.26 LPA to 19.4 LPA, with entry-level median salary around 11.25 LPA.
What It Costs
Understanding the full financial cost of training is critical before committing to a degree or certification program. Here is what you should expect to spend at different stages of your education:
- Undergraduate Degree (B.Tech / B.Sc): Tuition at government colleges (like IITs or central universities) ranges from ₹10,000 to ₹10 Lakhs in total. Private colleges (like BITS or VIT) range from ₹8 Lakhs to ₹20 Lakhs for 4 years. Hostels and living expenses add around ₹60,000 to ₹1.5 Lakhs per year.
- Postgraduate Degree (M.Tech / M.Sc): Government programs cost between ₹50,000 and ₹3 Lakhs total, while private master's degrees range from ₹4 Lakhs to ₹12 Lakhs.
- Certifications & Bootcamps: Professional online certifications (like IBM Data Science Certificate or Google Cloud Engineer) cost between ₹3,000 and ₹30,000. Full-time offline bootcamps range from ₹50,000 to ₹3 Lakhs.
- Tools & Hardware: A decent laptop with a modern multi-core processor and at least 16GB RAM costs ₹50,000 to ₹80,000. Cloud platform free tiers (AWS, GCP, Kaggle) are usually sufficient for basic learning without extra cost.
What the road costs
| Undergraduate Degree (B.Tech/B.Sc) | ₹4 lakh to ₹20 lakh |
| Postgraduate Degree (M.Tech/M.Sc) | ₹2 lakh to ₹10 lakh |
| Online Courses/Certifications | ₹10,000 to ₹2 lakh |
| Coaching for Entrance Exams (JEE/CUET) | ₹50,000 to ₹2 lakh |
| Books and Study Materials | ₹10,000 to ₹50,000 |
| Laptop/Computer | ₹50,000 to ₹1.5 lakh |
| Whole road | ₹7.2 lakh to ₹36 lakh |
Hostel and living expenses typically range from 1,00,000 to 3,00,000 per year during full-time studies.
The Years Ahead
The demand for qualified data science and AI professionals in India continues to grow due to rapid digital transformation across industries like fintech, healthcare, e-commerce, and IT services. India currently faces a demand-supply gap, with a shortfall of qualified data science and AI professionals estimated between 200,000 and 500,000. However, the nature of the entry-level job market has shifted dramatically.
Generative AI and automated machine learning (AutoML) platforms now handle routine data cleaning and basic model construction. This means companies are hiring fewer generalist data entry analysts and demanding more candidates with specialized skills. Future job growth will favor professionals who understand MLOps, model deployment, cloud infrastructures, and generative AI integrations. Moving from entry-level positions to senior roles will require continuous learning and domain specialization.
Could a machine take this job?
Core problem-solving, domain understanding, and critical thinking cannot be automated easily, even as basic tasks get streamlined.
Generative AI integration, prompt engineering, LLM fine-tuning, MLOps, model deployment, NLP, and computer vision.
Basic manual data cleaning, simple reporting, and basic spreadsheet tasks.
Pros and Cons
Pros
- High Earnings Potential: Competitive salary packages across experience levels, with senior roles commanding premium compensation.
- High Industry Demand: Strong hiring across finance, healthcare, e-commerce, manufacturing, and IT services.
- Flexible Work Culture: High availability of remote and hybrid work options compared to traditional engineering jobs.
- Continuous Growth: Opportunity to work on cutting-edge technologies like Generative AI, computer vision, and predictive analytics.
Cons
- High Pressure on Data Prep: Cleaning corrupt, missing, or poor-quality datasets consumes up to 80% of project time.
- Relentless Learning Curve: Technology frameworks change rapidly, requiring continuous self-study outside regular working hours.
- Unrealistic Expectations: Companies often expect data scientists to handle full data engineering and software pipelines single-handedly.
- High Cost of Training: Private university degrees and commercial bootcamps can require significant financial investment.
Mistakes Students Make
- Focusing on Coding while Ignoring Math: Jumping straight to Python code without understanding linear algebra, calculus, and basic probability leads to poor model building and failing technical interviews.
- Relying Only on Course Certificates: Collecting course certificates without building a public GitHub portfolio of custom projects will not convince recruiters.
- Using Overused Kaggle Datasets: Building portfolios using generic datasets like Titanic or Iris dataset shows a lack of originality. Work on custom scraped or real-world open datasets instead.
- Ignoring Database Skills: Neglecting SQL and data pipeline fundamentals makes it difficult to extract data from enterprise systems in actual work environments.
- Quitting Full-Time Jobs Too Early: Working professionals often leave stable jobs prematurely for expensive bootcamps instead of transitioning gradually through internal projects.
The mistakes, and the fix
✓ Build a strong portfolio of real-world projects using public or Indian datasets.
✓ Master SQL and Python data cleaning tools first, as real-world data is rarely clean.
✓ Practice framing business questions and communicating findings clearly to non-technical stakeholders.
Plan B
If a direct entry into a Data Scientist role does not happen immediately, several adjacent career options allow you to build relevant industry experience while transitioning:
- Data Analyst: Focuses on SQL, Excel, and visualization tools like Tableau to analyze business performance. This is the most common stepping stone to becoming a Data Scientist.
- Business Analyst: Focuses on understanding business metrics, requirements gathering, and translating domain needs into technical specifications.
- Data Engineer: Focuses on building database architectures, data pipelines, and infrastructure needed for data storage and processing.
- Database Administrator (DBA): Manages corporate relational databases, ensuring performance, data security, and smooth backup execution.
- Associate Software Engineer: Focuses on core software development, providing strong backend programming foundations that simplify a later shift into machine learning engineering.
FAQs
Is Data Science hard for beginners?
Data science can be challenging because it requires learning three distinct disciplines: mathematics/statistics, programming, and business domain knowledge. However, following a structured step-by-step roadmap starting with basic Python and SQL makes the learning process manageable.
Can non-CS students become Data Scientists in India?
Yes. Students from mathematics, statistics, economics, or general engineering backgrounds can become Data Scientists. What matters most to hiring managers is your practical coding skills, understanding of statistics, and live project portfolio on GitHub.
Is Python compulsory for Data Science?
Python is considered the primary language for data science due to its rich ecosystem of libraries like Pandas, NumPy, and Scikit-learn. SQL is equally essential for retrieving data from relational databases.
How long does it take to become a Data Scientist from scratch?
For high school students, it takes 3 to 4 years while completing an undergraduate degree. For college graduates or working professionals switching fields, it typically takes 12 to 24 months of consistent study and portfolio construction.
Are certifications enough to get a job as a Data Scientist?
No. Certifications show foundational learning, but recruiters prioritize demonstrated practical ability. You need a portfolio of real-world projects showing data cleaning, exploratory data analysis, and predictive modeling hosted on GitHub.
⚖️ Compare Entry Pathways: Tech Degree vs Non-Tech Transition
| Tech/STEM Degree Pathway | Non-Tech Transition Pathway | |
|---|---|---|
| Educational Background | B.Tech, B.Sc, M.Sc, or M.Tech in Computer Science, Statistics, or Math. | Bachelor's degree in Commerce, Arts, Business, or Non-STEM fields. |
| Foundational Math & Python | Covered during formal academic degree coursework. | Requires 3-6 months of dedicated self-study or specialized bootcamps. |
| Entry-Level Salary Range | ₹6.5 LPA to ₹11.5 LPA for 0-2 years experience. | ₹4.5 LPA to ₹8.0 LPA depending on non-tech domain experience. |
| Domain Knowledge Advantage | Strong technical, algorithmic, and software engineering basics. | Strong business context, problem framing, and industry process expertise. |
| Key Preparation Barrier | Mastering real-world business communication and practical project delivery. | Building deep math, statistical modeling, and hands-on coding rigor. |
🎯 Tech graduates enter faster via technical roles, while non-tech candidates succeed by pairing strong business domain knowledge with foundational Python and SQL.
🕐 Step-by-Step Learning and Skills Roadmap to 2026
- Step 1: Mathematics & Statistics FundamentalsMonths 1-2
Build core mathematical foundations including linear algebra, probability, basic calculus, and statistical hypothesis testing.
- Step 2: Python Programming & SQL CoreMonths 3-4
Master Python programming syntax and SQL queries to extract and manage data from relational databases at scale.
- Step 3: Data Cleaning & Exploratory Data AnalysisMonths 5-6
Learn Pandas and NumPy for data manipulation, cleaning messy data, and creating charts with visualization libraries.
- Step 4: Machine Learning FoundationsMonths 7-9
Train supervised and unsupervised algorithms using Scikit-Learn and evaluate model performance metrics.
- Step 5: MLOps, Deployment & Generative AIMonths 10-12
Deploy models using APIs, learn Git and cloud basics, and gain practical exposure to LLM fine-tuning and AI-assisted workflows.
📊 Data Scientist Salary Breakdown by City and Experience
Highest average data science compensation among tech hubs in India.
Driven heavily by financial services, banking, and fintech headquarters.
Strong growth powered by expanding enterprise IT and cloud centers.
Typical starting salary band for fresh graduates and early career entrants.
Top 10% of practitioners in India earn over ₹61.2 LPA.
Your decision notebook
Two lines now save you a wrong year later. Everything stays on this device.
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