MSME-Approved EdTech Platform

Data Science
& Artificial Intelligence Program

An intensive 6-week, 100% offline classroom training program designed to transform beginners into industry-ready Data Science professionals. Master Predictive Modeling, RAG pipelines, and build Autonomous AI Agents.

6 Weeks
Duration
100%
Offline Classes
Mon-Fri
Daily Sessions
Yes
Internships
Enrollment Details
Program Fee
₹7,999
Enroll Now
  • 120+ Hrs Total Training
  • 70% Practical Lab Ratio
  • 4 Major Industry Projects
  • Daily + Weekly Evaluations

Program Objective & Pedagogy

The NP TechCrafters Data Science program is meticulously structured to bridge the gap between academic theory and industry requirements. This curriculum has been vetted by industry experts to ensure complete compliance with modern technological standards.

Delivery Mechanism: Delivered entirely offline, the program mandates rigorous Monday-to-Friday attendance. The pedagogy revolves around an "Implementation-First" approach, where theoretical concepts are immediately followed by practical coding labs.

Key Assessment Metrics (Audit-Ready)

  • Daily Lab Assessments: Mandatory code commits and daily assignments evaluated for logic and syntax.
  • Weekly Proficiency Tests: Objective and subjective evaluations at the end of each week to ensure knowledge retention.
  • Capstone Evaluation: A major industry-grade project assessed on deployment architecture, accuracy, and business impact.
  • Attendance & Conduct: Strict adherence to offline classroom schedules is required for certification eligibility.

Why NP TechCrafters?

MSME Certified

Government of India recognized certification validating your skills.

Internship Opportunities

Top performers receive direct placement into virtual/offline internships.

Mock Interviews

Intensive HR and Technical interview prep tailored to current market demands.

Tools & Technologies Mastered

Python
Pandas
Scikit-Learn
Neural Nets
LangChain
Vector DBs

6-Week Master Curriculum

A sequential, rigorous learning path advancing from fundamental programming to production-ready predictive modeling and Agentic AI.

Objective: Re-establish foundational math and transition learners from basic logic to advanced Python data structures required for scalable Data Science operations.

Day 1: Setup & Syntax
  • Anaconda & Jupyter setup
  • Git & GitHub basics
  • Variables, Operators, I/O
  • Control Flow
Day 2: Data Structures
  • Lists, Tuples, Sets
  • Dictionary operations
  • List Comprehensions
  • String manipulation
Day 3: Adv. Python
  • Functions & Lambda args
  • Object-Oriented Prog (OOP)
  • Error Handling
  • File I/O operations
Day 4: Math Essentials
  • Linear Algebra Basics
  • Central Tendency
  • Variance & Std Dev
  • Probability Dist.
Day 5: Assessment
Lab Exam: Algorithmic problem-solving using core Python.
Deliverable: CLI-based data processing script pushed to GitHub.

Objective: Master the processing of raw, messy datasets using industry-standard libraries to engineer features and extract actionable insights.

Day 1: NumPy Power
  • Arrays & Matrix ops
  • Vectorization techniques
  • Broadcasting & Indexing
Day 2: Pandas Core
  • Series & DataFrames
  • Filtering & Sorting
  • Handling Missing Values
Day 3: Adv. Pandas
  • Groupby & Aggregations
  • Merging & Joining
  • Datetime processing
Day 4: Visualization
  • Matplotlib Architecture
  • Seaborn Statistical Plots
  • Exploratory Data Analysis
Day 5: Assessment
Lab Exam: Data Cleaning Pipeline.
Deliverable: Comprehensive EDA Report on a provided real-world messy dataset.

Objective: Implement core Machine Learning algorithms. Understand how machines learn from historical data to forecast outcomes.

Day 1: Regression
  • Scikit-Learn framework
  • Linear/Polynomial Reg.
  • RMSE, R² Score, MAE
Day 2: Classification
  • Logistic Regression
  • Support Vector Machines
  • Confusion Matrix, ROC-AUC
Day 3: Tree Models
  • Decision Trees
  • Random Forests
  • Hyperparameter Tuning
Day 4: Unsupervised
  • K-Means Clustering
  • PCA (Dimensionality)
  • Feature Scaling
Day 5: Assessment
Lab Exam: Model Training & Tuning.
Deliverable: Binary classification model predicting Loan Default Risk.

Objective: Transition into the architecture of modern AI. Build Neural Networks from scratch and utilize them for complex data like images.

Day 1: Intro to ANNs
  • Perceptrons
  • Activation Functions
  • Forward/Backpropagation
Day 2: Keras & TF
  • Building Sequential Models
  • Optimizers (Adam)
  • Handling Overfitting
Day 3: Convolutional Nets
  • Image representation
  • Filters, Strides, Pooling
  • CNN Architectures
Day 4: Transfer Learning
  • Pre-trained Models
  • Fine-tuning weights
  • CV Applications
Day 5: Assessment
Lab Exam: Neural Network architecture design.
Deliverable: Medical Image Classification using custom CNN.

Objective: Process natural language data and explore the mechanics of Large Language Models (LLMs) to build modern RAG architectures.

Day 1: NLP & LLMs
  • Tokenization
  • Transformers
  • Prompt Engineering
Day 2: LangChain
  • LangChain architecture
  • Model I/O (Parsers)
  • Connecting to OpenAI API
Day 3: Vector Databases
  • Word Embeddings
  • Document Chunking
  • ChromaDB & Pinecone
Day 4: RAG Flow
  • Retrievers & Chains
  • Contextual Querying
  • Handling Hallucinations
Day 5: Assessment
Lab Challenge: Build scalable RAG.
Deliverable: Custom Document Q&A Bot querying multiple PDFs.

Objective: Transition from static LLMs to autonomous Agentic AI workflows. Master tool-calling and synthesize all learning into a production-grade Capstone project.

Day 1: Agentic AI
  • Agentic Reasoning vs Static RAG
  • Tool-Calling & Memory
  • Model Context Protocol (MCP)
Day 2: Multi-Agent Systems
  • Orchestration with CrewAI/LangGraph
  • Defining Agent Roles
  • Agentic RAG
Day 3: UI Deployment
  • Web UIs with Streamlit
  • Integrating Agents into UI
  • Basic Cloud Deployment
Days 4-5: Capstone Defense & Career Prep
Final Presentation: Defend architecture before instructors simulating stakeholders.
Mock Interviews: Technical rounds & HR prep.
MSME Certification Award: Granted upon clearing evaluation criteria.

Industry-Grade Capstone Projects

Candidates select one major capstone to build from scratch. These projects simulate real enterprise requirements.

Predicting Loan Default Risk

Apply classification algorithms (Random Forest, XGBoost) to real-world financial data to detect fraud and evaluate borrower credit risk profiles.

Scikit-Learn Pandas
Corporate Document Q&A (RAG)

Develop a Retrieval-Augmented Generation pipeline that securely ingests dense corporate PDFs into a Vector DB, allowing users to query them via an LLM.

LangChain Vector DB
FLAGSHIP
Autonomous Agentic Assistant

Design a multi-agent system that autonomously orchestrates workflows, searches knowledge bases, executes tool API calls, and synthesizes reports.

Agentic AI MCP

Transition from Learner to Professional

Our commitment extends beyond classroom training. Week 6 encompasses a dedicated career readiness protocol designed to secure internships and entry-level positions.

MSME Validated Certificate

Receive a globally recognized, MSME-approved certificate upon successful completion of assessments.

Guaranteed Internship Opportunity

Candidates maintaining an 'A' grade are directly channeled into virtual or offline internship roles.

Interview Mastery

1-on-1 mock interviews covering Machine Learning algorithms and business case studies.

NP TechCrafters MSME Certificate