Become a Job-Ready Data Scientist
Master Python, Statistics, SQL, Machine Learning, and Data Visualization with live mentorship. Build a real portfolio and break into high-paying data roles.
What You Will Learn?
A complete curriculum that takes you from statistical foundations to production-grade data science workflows.
What Is a Data Scientist?
A Data Scientist is a professional who collects, cleans and analyses data, applies statistics and machine learning to find patterns and make predictions, and communicates insights that help businesses make better decisions.
Companies increasingly hire data scientists who can also deploy models, not just analyze spreadsheets.
- Exploratory data analysis on real datasets
- Building and validating ML models
- Writing production-ready SQL queries
- Communicating insights through visualization
- Feature engineering and model tuning
- Deploying models with basic MLOps practices
Why This Role Is Exploding in 2026?
Data-Driven Decisions
Every business function like marketing, finance, operations now runs on data models, not gut instinct.
Automated Reporting
Businesses want dashboards and forecasts that update themselves, not monthly manual reports.
Predictive Analytics
From churn prediction to demand forecasting, every industry needs predictive models.
Who Is This For?
Skills Covered in This Course
Python, NumPy & Pandas
Clean, analyse and visualise real-world data.
Statistics & Machine Learning
Regression, classification, clustering and model evaluation.
Deep Learning & NLP
Neural networks, transformers and text processing.
Generative AI & LLMs
Prompt engineering, LLM APIs and Hugging Face models.
RAG & Vector Databases
Build document chatbots grounded in your own data.
AI Agents & n8n Automation
Multi-step agents and AI-powered business workflows.
FastAPI, Docker & MLOps
Serve, containerise, track and monitor models.
AWS & CI/CD
Deploy AI applications to the cloud with automated pipelines.
Power BI, MySQL & Excel Analytics
Dashboards, SQL analysis and business reporting.
Mentorship Learning Model
Live Mentorship
Direct interaction with Mehul Prajapati during live classroom sessions at our Borivali West centre.
Hands-on Projects
Work on real-world AI applications, from RAG systems to multi-agent production apps.
Scenario-Based Learning
Solve enterprise-level problems using specific AI case studies and workflows.
1-on-1 Feedback
Get personalized review on your code and architecture designs via Discord.
Career Coaching
Optimize your LinkedIn, resume, and portfolio for the 2026 AI job market.
Exclusive Resources
Access proprietary prompt libraries, deployment scripts, and project templates.
Tools & Technologies You Will Master
Job Roles After Completing
Detailed Course Curriculum
A structured, fully visible syllabus — no hidden content.
Python
- Python fundamentals: data types, loops, functions, OOP
- NumPy arrays and vectorised operations
- Pandas for data cleaning, transformation and analysis
- Data visualisation with Matplotlib and Seaborn
Statistics & Algorithms
- Descriptive statistics, probability and distributions
- Hypothesis testing, confidence intervals and A/B testing
- Correlation, regression analysis and sampling
- Core algorithms and problem-solving for data work
Machine Learning
- Supervised learning: regression and classification
- Unsupervised learning: clustering and dimensionality reduction
- Feature engineering and data pre-processing
- Model evaluation, cross-validation and hyperparameter tuning
Deep Learning & NLP
- Neural networks, activation functions and backpropagation
- CNNs, RNNs and LSTMs with TensorFlow/PyTorch
- NLP basics: text cleaning, tokenisation, embeddings
- Transformers and attention
Foundations of Generative AI
- What generative AI is and how it works
- Text, image and multimodal generation
- Real-world GenAI use cases and limitations
- Responsible AI, safety and hallucinations
LLMs & Model Ecosystem
- How LLMs work: tokens, context windows, parameters
- Closed vs open-source models and choosing the right model
- Using LLM APIs: chat, structured output, function calling
- Fine-tuning vs RAG vs prompting
Prompt Engineering & Context Design
- Prompt patterns: instructions, roles, few-shot examples
- Structured outputs and output validation
- Context design: what to include and how to order it
- Testing, versioning and improving prompts
Hugging Face
- Hugging Face Hub: models, datasets and Spaces
- Transformers pipelines for common NLP tasks
- Running open-source models locally
- Intro to fine-tuning and sharing models
RAG Systems & Vector Databases
- RAG architecture: retrieve, augment, generate
- Document loading, chunking and embeddings
- Vector databases and similarity search
- Hybrid search, re-ranking and retrieval evaluation
Agentic AI (AI Agents & Workflows)
- What AI agents are: tools, memory, planning
- Building agents and workflows with LangChain/LangGraph
- Tool calling and integrating external APIs
- Guardrails, human-in-the-loop and agent evaluation
LLM Application Development
- Designing end-to-end LLM applications
- Chatbots, document Q&A and summarisation apps
- Front-ends for AI apps (Streamlit/Gradio)
- Handling errors, latency and user feedback
AI Coding Assistants
- Using AI coding assistants for faster development
- Generating, explaining and refactoring code
- Debugging and writing tests with AI
- Reviewing and validating AI-written code
n8n Automation with AI
- n8n basics: nodes, triggers and workflows
- Connecting LLMs to email, sheets and CRM tools
- Building AI-powered automation workflows
- Scheduling, error handling and monitoring workflows
API Security
- API keys, secrets and environment management
- Authentication and authorisation basics
- Prompt-injection risks and input validation
- Rate limiting and protecting AI endpoints
Cost Optimization & Monitoring
- Understanding token usage and LLM costs
- Caching, model selection and prompt optimisation
- Logging, tracing and monitoring AI apps
- Tracking quality, latency and cost metrics
Production RAG System
- Building a production-ready RAG pipeline end to end
- Metadata filtering, citations and access control
- Evaluating faithfulness and answer quality
- Scaling, updating and maintaining the knowledge base
FastAPI
- Building REST APIs with FastAPI
- Request and response models with Pydantic
- Serving ML models and LLM apps as APIs
- Async endpoints, testing and API documentation
Docker
- Containers, images and Dockerfiles
- Containerising ML and LLM applications
- Docker Compose for multi-service apps
- Image optimisation and best practices
MLOps
- ML lifecycle and MLOps principles
- Experiment tracking and model registry (MLflow)
- Data and model versioning
- Model monitoring, drift detection and retraining
AWS Deployment on Cloud
- AWS essentials: IAM, EC2, S3
- Deploying APIs and containers on AWS
- Managed AI/ML services overview
- Security, scaling and cost basics on cloud
CI/CD Pipelines
- Git and GitHub workflows
- Automated testing and linting
- CI/CD with GitHub Actions
- Automated build and deployment of AI apps
Power BI Analytics
- Data import and transformation with Power Query
- Data modelling and relationships
- DAX measures and calculations
- Interactive dashboards and reports
MySQL Analytics
- SQL basics: SELECT, filtering and sorting
- Joins, GROUP BY and aggregations
- Subqueries, CTEs and window functions
- Business analysis with SQL
Advanced Excel
- Lookup (XLOOKUP/VLOOKUP), logical and text functions
- Pivot tables and pivot charts
- Data cleaning and validation
- Dashboards and reporting in Excel
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