+91 86907 86907
Contact Info
City Centre, “C” Wing, 2nd Floor, Near ST Depot, Station Road, Bharuch, Gujarat – 392001
About Program
The Data Science & Generative AI Program is a 6, 8 or 10-month journey from your first line of Python to building and deploying machine learning, deep learning, NLP, and Generative AI applications. It starts from absolute basics, so no coding, statistics, or data background is needed.
The program is designed to be taught slowly and thoroughly rather than rushed. Python is split into a dedicated foundations phase and a separate advanced phase, so students build real fluency before moving on. Every subsequent topic — SQL, math, machine learning, and deep learning — is also taught step by step, from the underlying basics up to practical, job-ready skills, with enough time built in to practice before advancing.
You move through the complete data science and AI pipeline: Python foundations, advanced Python, data wrangling, SQL and databases, math and statistics, machine learning, advanced machine learning, deep learning, computer vision, NLP, Generative AI fundamentals, and practical deployment. Every phase is project-based, with progressively larger projects, a mid-course project, multiple portfolio projects, and an end-to-end capstone.
The program focuses on building strong Data Science + AI application skills without turning the final phase into a separate full-scale MLOps or DevOps specialization.
You graduate with a Kanan certificate.
Classes are held offline, in person, in our classroom at Kanan.co Bharuch, City Centre, Station Road, Bharuch, and online classes are also available.
Program Highlights
6, 8 or 10-month program
5 days a week, offline in Bharuch or online
Starts from zero
Python programming, statistics, and data science fundamentals are taught from scratch
Slow, step-by-step pacing
Python is split into a foundations phase and an advanced phase
Complete pipeline
Python, advanced Python, SQL, math and statistics, machine learning, deep learning, NLP, Generative AI fundamentals, and practical deployment
18+ hands-on projects and exercises
Including a mid-course project and an end-to-end capstone
Industry tools
Python, NumPy, Pandas, SQL, scikit-learn, XGBoost, LightGBM, PyTorch, Hugging Face, Streamlit, FastAPI, Docker, MLflow, and Git
Generative AI fundamentals included
LLM basics, prompt engineering, LLM APIs, and an introduction to embeddings, RAG, and AI agents
Practical deployment
Learn to turn ML and AI projects into usable APIs and applications
Career readiness
Portfolio, GitHub, resume, LinkedIn, technical interviews, case studies, and mock interviews
Program Eligibility
Who can join
- Final-year students and graduates from any stream who want a career in data science and AI
- Working professionals looking to move into data science, machine learning, or applied AI
- Data analysts who want to add machine learning and AI skills
- Career switchers and freelancers who want to build data- and AI-driven products
Prerequisites
- No prior programming or data science experience required
- Basic mathematics up to school level (Class 10–12) is helpful, and higher math concepts are taught intuition-first
- Curiosity, consistency, and willingness to practice regularly
System requirements
- Laptop or desktop with at least 8 GB RAM (16 GB recommended) and a stable internet connection
- Python, Jupyter, VS Code, Git, and Google Colab for GPU-based deep learning work
After the Course
By the end of the program, you will be able to:
- Write clean, well-structured Python programs, including object-oriented code, and use Git/GitHub for version control
- Collect, clean, transform, analyze, and visualize real-world datasets with NumPy and Pandas
- Query relational databases using SQL, including joins, CTEs, window functions, and analytical queries
- Apply probability, statistics, hypothesis testing, and A/B testing concepts to real data problems
- Build, evaluate, tune, and interpret supervised and unsupervised machine learning models
- Use ensemble methods such as Random Forest, XGBoost, LightGBM, and CatBoost on tabular data
- Build time-series forecasting and recommendation-system prototypes
- Build neural networks and computer-vision models using PyTorch and transfer learning
- Work with NLP pipelines, embeddings, transformers, and Hugging Face models
- Understand LLM fundamentals, prompt engineering, and how to work with LLM APIs
- Understand the basics of embeddings, RAG, and AI agent concepts
- Deploy ML and AI applications using Streamlit and FastAPI, with practical Docker and cloud-deployment basics
- Track experiments and understand basic model monitoring concepts with MLflow
- Present a portfolio of substantial projects, including an end-to-end capstone
- Prepare for entry-level Data Science, ML, Analytics, and Applied AI roles
Skills You Will Learn
Python Foundations
Variables, data types, control flow, functions, data structures, file handling, debugging
Advanced Python
Object-oriented programming, exception handling, modules and packages, virtual environments, clean code, Git and GitHub
Data Handling
NumPy, Pandas, APIs, web scraping basics, data cleaning, transformation, feature preparation
Databases
SQL, joins, CTEs, window functions, analytical queries, Python-SQL connectivity, NoSQL concepts
Visualization
Matplotlib, Seaborn, Plotly, exploratory analysis, dashboard basics, data storytelling
Math & Statistics
Probability, distributions, sampling, confidence intervals, hypothesis testing, A/B testing, linear algebra intuition, calculus and gradient descent intuition
Machine Learning
Regression, classification, clustering, ensembles, feature engineering, hyperparameter tuning, model evaluation, interpretability, anomaly detection
Advanced ML
XGBoost, LightGBM, CatBoost, SHAP, Optuna, recommendation systems, time-series forecasting
Deep Learning
Neural networks, PyTorch, training loops, CNNs, transfer learning, sequence models overview
NLP & Transformers
Text preprocessing, TF-IDF, sentiment analysis, embeddings, transformers, Hugging Face
Generative AI Fundamentals
LLM basics, prompt engineering, LLM APIs, structured outputs, tool calling, intro to embeddings, intro to RAG, intro to AI agents
Deployment & MLOps Basics
FastAPI, Streamlit, model serialization, Docker basics, MLflow, monitoring concepts, practical cloud deployment overview
Professional
Portfolio, GitHub, Kaggle-style workflow, resume, LinkedIn, case studies, technical interview preparation
Career Outcome
Roles you can apply for
- Junior Data Scientist
- Machine Learning Associate / Engineer (entry level)
- AI / Generative AI Associate
- NLP or Applied AI Analyst
- Advanced Data Analyst (with ML skills)
- Data Science Associate in consulting and analytics teams
Industries hiring
IT services and product companies, BFSI and fintech, e-commerce and retail, healthcare and life sciences, telecom, EdTech, consulting, and AI startups.
Why Choose This Program
- Starts from zero: Python, statistics, and machine learning foundations are taught from the basics
- Slow and thorough pacing: Python is split into foundations and advanced phases, and every topic is built up step by step rather than rushed
- Up to 10 months of depth: choose a 6, 8 or 10-month plan, with enough time to practice instead of rushing through advanced topics
- End-to-end: moves from raw data to machine learning, deep learning, and Generative AI fundamentals, all the way to deployed applications
- Strong ML foundation: regression, classification, ensembles, feature engineering, clustering, forecasting, and interpretability
- Generative AI fundamentals: LLMs, prompt engineering, LLM APIs, and an introduction to embeddings, RAG, and AI agents
- Project-based: multiple projects build a portfolio throughout the program rather than relying on one final project
- Practical deployment: students learn to turn models and AI applications into usable APIs and web applications
- Industry-oriented stack: Python, Pandas, SQL, scikit-learn, XGBoost, PyTorch, Hugging Face, FastAPI, Streamlit, Docker, MLflow, and Git
- Career preparation: GitHub, portfolio, resume, LinkedIn, technical interviews, case studies, and mock interviews
- Focused scope: advanced MLOps, Kubernetes, infrastructure engineering, and full DevOps are intentionally outside the core curriculum so students can develop stronger Data Science and AI fundamentals
Data Science & Generative AI Course in Bharuch: FAQs
Classes are held at Kanan.co Bharuch, City Centre, “C” Wing, 2nd Floor, Near ST Depot, Station Road, Bharuch, Gujarat – 392001. Call +91 86907 86907 or email info@kananbharuch.co to book your seat.
The Data Science & Generative AI course is taught offline, in person, in our classroom at Kanan.co Bharuch on Station Road, so you learn face to face with your trainer and work on projects alongside other students. Online classes are also available if travelling to the centre is difficult.
You can choose a 6, 8 or 10-month plan, with classes 5 days a week.
The complete data science and AI pipeline: Python foundations and advanced Python, data wrangling, SQL, math and statistics, machine learning, deep learning, computer vision, NLP, Generative AI fundamentals (LLMs, prompt engineering, RAG and AI agents) and practical deployment.
No prior programming or data science experience is required; everything starts from absolute basics. School-level mathematics (Class 10–12) is helpful, and higher maths concepts are taught intuition-first.
Entry-level roles such as Junior Data Scientist, Machine Learning Associate, AI / Generative AI Associate, NLP or Applied AI Analyst and Advanced Data Analyst. You build 18+ projects and exercises, including an end-to-end capstone, and graduate with a Kanan certificate.
Fees and batch timings depend on the batch and duration you choose. Call +91 86907 86907, visit our Bharuch centre, or fill in the free counselling form on this page and our team will share the current batches.
Choose AI For Everyone (3 months, no coding required) if you want to use AI tools such as ChatGPT, Canva and automation tools for work, content or business. Choose Data Analytics (4, 6 or 8 months) to build analyst skills in Excel, SQL, Python and Power BI. Choose Data Science & Generative AI (6, 8 or 10 months) to learn Python, machine learning, deep learning and Generative AI, starting from the basics. Not sure which suits you? Fill in the free counselling form on this page and our team at Kanan.co Bharuch will help you decide.
