01
Demand forecasting model
Forecast weekly demand for a retail category, then explain honestly where the model breaks and what it would cost the business.
- Tools
- Python, scikit-learn, pandas
- Dataset
- Public retail sales history
9 months · ₹89,999 · Next batch 6 September 2026
Data Science Course in Nagpur — Machine Learning, Deep Learning and Generative AI
Nine months from Python foundations through machine learning, deep learning, NLP, computer vision and Generative AI/LLMs, taught through models you build and deploy, plus a month of corporate grooming. Classroom, online and weekend batches in Nagpur.
Quick facts about the Data Science course
- Duration
- 9 months — 8 months training + 1 month grooming
- Mode
- Classroom / Online / Weekend
- Next batch
- 6 September 2026
- Fee
- ₹89,999 all-inclusive · EMI available
- Prerequisites
- Comfort with school-level mathematics helps. Python is taught from scratch.
- Certification
- Techtonic Lab completion certificate, plus guidance on vendor certification
- Placement support
- Yes — assistance, not a guarantee
- Language
- English, with Hindi and Marathi explanation on request
- Campuses
- Somalwada and Jaitala Road, Nagpur
Who it is for
Who is the Data Science course for?
Three groups enrol in every batch. If you do not see yourself here, say so on the call and we will tell you honestly whether this is the right route.
Engineering and science graduates
You have the mathematical grounding and want to convert it into a role. This course spends its time on modelling judgement — what to try, what to discard, how to know a model is worse than it looks — through machine learning, deep learning and Generative AI.
Analysts moving up
Already comfortable with SQL and dashboards, and want to move from describing what happened to predicting what will. The statistics, machine-learning and deep-learning modules are built for exactly this transition.
Working professionals
The weekend batch is the same syllabus and the same faculty, run so you never have to leave your job to retrain. Expect eight to ten hours of practice a week outside class across the nine months.
Curriculum
What you will actually build, module by module
8 modules across 9 months. Every module lists the topics, the tools and the thing you finish with.
01Python, NumPy and Pandas
Months 1–2
Python, NumPy and Pandas
Months 1–2
Topics covered
- Python fundamentals, data structures, OOP and file handling
- Exception handling, modules and packages
- NumPy arrays, broadcasting and linear algebra
- pandas — DataFrames, cleaning, GroupBy, merge and join
- Version control with Git and GitHub
Tools
- Python
- NumPy
- pandas
- Git
You finish with
A reusable data-loading and cleaning module of your own
02SQL, Advanced Excel and visualisation
Month 3
SQL, Advanced Excel and visualisation
Month 3
Topics covered
- SQL — joins, subqueries, CTEs, window functions, stored procedures
- Data modelling and normalisation
- Advanced Excel — Power Query, Power Pivot, VBA and macros
- Matplotlib, Seaborn and Plotly
- Statistical and interactive visualisations
Tools
- MySQL
- Excel
- Matplotlib
- Seaborn
- Plotly
You finish with
An interactive dashboard combining SQL-sourced data and Python plots
03Statistics and mathematics
Month 4 (first half)
Statistics and mathematics
Month 4 (first half)
Topics covered
- Descriptive and inferential statistics, probability and sampling
- Hypothesis testing — T-test, ANOVA, Chi-square
- Correlation and covariance
- Linear algebra, matrices and vectors
- Calculus basics, gradient descent and optimisation
Tools
- Python
- SciPy
You finish with
A written statistical analysis with stated assumptions and limits
04EDA and machine learning
Months 4–5
EDA and machine learning
Months 4–5
Topics covered
- Data profiling, outlier detection and feature engineering
- Supervised learning — regression, decision trees, random forest, SVM, XGBoost
- Unsupervised learning — K-Means, hierarchical, DBSCAN, PCA
- Model evaluation — precision, recall, F1, ROC-AUC
- Cross-validation and hyperparameter tuning
Tools
- scikit-learn
- XGBoost
- Python
You finish with
A tuned, cross-validated classifier with an honest error analysis
05Deep learning
Month 6
Deep learning
Month 6
Topics covered
- Neural networks and the perceptron
- ANN, CNN, RNN and LSTM
- TensorFlow and Keras
- Transfer learning
- Image classification
Tools
- TensorFlow
- Keras
- Python
You finish with
An image classifier trained and evaluated end to end
06NLP and computer vision
Month 7
NLP and computer vision
Month 7
Topics covered
- Text processing, tokenisation, stemming and lemmatisation
- Sentiment analysis and text classification
- Word embeddings, transformers and BERT basics
- Image processing with OpenCV
- Object detection and face recognition
Tools
- OpenCV
- Transformers
- Python
You finish with
A sentiment-analysis model and an OpenCV vision task
07Generative AI and LLMs
Month 8
Generative AI and LLMs
Month 8
Topics covered
- Introduction to AI, GenAI and prompt engineering
- Large Language Models and the OpenAI APIs
- LangChain and Retrieval-Augmented Generation (RAG)
- AI agents and AI automation
- Building a GenAI application
Tools
- OpenAI API
- LangChain
- Python
You finish with
A RAG-based application over your own document set
08Deployment, capstone and corporate grooming
Month 9
Deployment, capstone and corporate grooming
Month 9
Topics covered
- Model deployment — Flask, FastAPI, Streamlit and REST APIs
- Docker basics and an introduction to MLOps
- Cloud fundamentals (AWS / Azure)
- End-to-end industry capstone project
- Resume, LinkedIn, GitHub portfolio, mock interviews and grooming
Tools
- Flask
- FastAPI
- Streamlit
- Docker
You finish with
A deployed model behind a working endpoint, defended in review
Portfolio
Four projects, not forty exercises
Each one is documented, reviewed and defended out loud. A hiring manager reads projects far more closely than a certificate.
02
Credit-risk classifier
Build and tune a classifier, then work through the class-imbalance and fairness problems that make this a hard problem rather than a tutorial.
- Tools
- scikit-learn, XGBoost
- Dataset
- Public lending dataset
03
Sentiment analysis and NLP
Process real text, build a classifier and interpret it, using embeddings and transformer basics rather than a bag-of-words toy.
- Tools
- Transformers, Python
- Dataset
- Public review corpus
04
Deployed GenAI capstone
Take a model or a RAG application all the way to a running endpoint with a simple interface, so you can demonstrate it live in an interview.
- Tools
- LangChain, FastAPI, Docker
- Dataset
- Your own choice from earlier projects
Careers
Where this course leads
Indicative salary bands for Nagpur and the wider Vidarbha region. These are market observations, not offers — and they move with your project quality far more than with your marks.
| Role | Entry level | 2–4 years |
|---|---|---|
| Junior Data Scientist | ₹4.0L – ₹6.5L | ₹9L – ₹15L |
| Machine Learning Engineer | ₹4.5L – ₹7L | ₹10L – ₹18L |
| Data Analyst (advanced) | ₹3.5L – ₹5L | ₹7L – ₹11L |
| AI / GenAI Engineer | ₹5L – ₹8L | ₹12L – ₹22L |
Who teaches it
Faculty for the Data Science course
The people who will actually be in the room, named, with the years behind each of them.
Sudhir Talekar
Faculty — Data Analytics & Data Science · 12+ years
Over a decade across data, business intelligence and corporate strategy. Runs the Data Analytics and Data Science tracks — from SQL, Power BI and statistics through Python, machine learning and applied AI — and mentors every learner through their capstone project.
Vivek Khubalkar
Faculty — Data Science · 5+ years
Five years across analytics, Python and machine learning, plus classroom teaching. Runs the hands-on Data Science modules — Python, data wrangling, model building and deployment — and reviews every portfolio project personally.
Batch schedule
When the next intakes start
Published in advance so you can plan around a notice period or a semester.
6 September 2026
Open- Mode
- Online
- Timing
- Sat–Sun, 9:00 am – 12:00 pm
- Campus
- Live online
- Seats left
- 10
6 October 2026
Open- Mode
- Classroom
- Timing
- Mon–Fri, 6:30 pm – 8:30 pm
- Campus
- Somalwada
- Seats left
- 13
1 November 2026
Open- Mode
- Online
- Timing
- Sat–Sun, 9:00 am – 12:00 pm
- Campus
- Live online
- Seats left
- 16
2 November 2026
Open- Mode
- Weekend
- Timing
- Sat–Sun, 2:00 pm – 5:00 pm
- Campus
- Somalwada
- Seats left
- 15
Fees
₹89,999, itemised
One price for the whole 9 months. No registration fee, no examination fee, no certificate fee, and no tier above this one.
₹89,999all-inclusive
or ₹15,000 × 6 months on EMI · no hidden charges
- 8 months of core training
- All learning material and datasets
- End-to-End industry Capstone projects
- 1 month corporate grooming
- 3 recorded mock interviews
- Placement preparation, GitHub portfolio setup, and referrals
FAQ
Data Science — questions people actually ask
If yours is not here, WhatsApp it to us. We answer within the hour on working days.
How long is the data science course at Techtonic Lab?
The Data Science course runs for 9 months, covering Python, statistics, machine learning, deep learning, NLP, computer vision and Generative AI/LLMs, ending with model deployment, an end-to-end capstone and a month of corporate grooming. Classroom, online and weekend batches all follow the same syllabus.
Do I need a maths or engineering background for data science?
Comfort with school-level mathematics genuinely helps, more so than for the Data Analytics course. You do not need a mathematics degree. Python is taught from scratch, and the statistics and mathematics module builds from distributions and linear algebra upward rather than assuming prior study.
What is the fee for the data science course in Nagpur?
The fee is ₹89,999 for the complete nine-month programme, reflecting the additional deep learning, NLP, computer vision and Generative AI content. A no-cost EMI is available at roughly ₹15,000 per month over six months, and there are no separate registration or certificate charges.
What is the difference between the data analytics and data science courses?
Data Analytics is about describing what happened — SQL, Excel, Power BI and reporting, over 6 months. Data Science is about predicting and generating — machine learning, deep learning, NLP, computer vision and Generative AI, over 9 months. Analytics is the faster route into a first job; Data Science pays more but expects more mathematical comfort and more time.
Does the course cover Generative AI and LLMs?
Yes. A dedicated module covers prompt engineering, Large Language Models, the OpenAI APIs, LangChain, Retrieval-Augmented Generation and AI agents, and you build a GenAI application as part of it. It sits after the machine-learning, deep-learning and NLP modules so you reach it with real foundations.
Will I actually deploy a model, or only build one in a notebook?
You deploy one. The final module takes a model or a RAG application you built earlier all the way to a running endpoint using Flask, FastAPI or Streamlit, because being able to demonstrate a live system in an interview separates you from candidates who only have notebooks.
Does Techtonic Lab guarantee a data science job?
No. Techtonic Lab provides placement assistance, not a placement guarantee. That means resume and LinkedIn rebuilds, recorded mock interviews, aptitude practice and referrals. Any institute promising a guaranteed job should be treated with caution.
When does the next data science batch start?
The next Data Science batch begins on 25 August 2026, with classroom, online and weekend options. Later dates are listed on the batches page.
Enquire
Ask about the Data Science course
We call within four business hours, Monday to Saturday. No sales script — if a different route suits your background better, we will say so.
Next batch starts 6 September 2026 · Classroom / Online / Weekend
Explore other courses
The other two tracks
The same all-inclusive shape and grooming month, each with its own length and fee. Different destination — pick where you want to end up.
Ready to start the Data Science course?
Book a free 20-minute consultation, or come and sit in on a live session before you decide. Both are free and neither commits you to anything.