Academic, Engineering, Software & Research Verticals
Click Tabs for other Verticals and Contact us for more details.
Quantum Technology Curriculum
- Foundations of Quantum Information
- Advanced Quantum Algorithms
- Quantum Machine Learning (QML) Core
- Quantum Optimization & NISQ Era Applications
- Practical Engineering, MLOps, & Toolkits
- Mastering QuantumComputing using Python, Qiskit, and PennyLane.
- Foundations & Gate Construction - IBM Qiskit Implementation ; Xanadu PennyLane Implementation
- Advanced Quantum Algorithms in Python
- Variational Algorithms & QML (PennyLane Specialization) - Data Embedding Techniques; Ansatz Design & Parameterization; Quantum Gradients & Optimization; Quantum Neural Networks (QNN)
- Hybrid Quantum Optimization (VQE & QAOA)
- Hardware Deployment & Hybrid Integration - Qiskit Runtime & IBM Quantum; PennyLane Plugins; Torch & TensorFlow Integration
Advanced Mathematics Curriculum
- Engineering Mathematics-I
- Engineering Mathematics-II
- Engineering Mathematics-III
- Engineering Mathematics-IV
- Probability & Statistics
- Probability & Statistics using MiniTab
Mathematics - IIT/JEE
Mechanical Engineering Curriculum
- Engineering Mechanics (Statics & Dynamics)
- Engineering Graphics & CAD Basics
- Thermal Engineering
- Fluid Mechanics
- Strength of Materials / Mechanics of Materials
- Elements of Mechanical Engineering
- Hydraulics & Hydraulic Machines
- Design of Machine Elements-I
- Design of Machine Elements-II
- Applied Thermodynamics (IC Engines, Gas Turbines)
- Turbo Machinery (Pumps, Fans, Compressors)
- Refrigeration and Air Conditioning (HVAC)
- Industrial Engineering & Operations Research
- Computer-Aided Design & Manufacturing (CAD/CAM)
- Finite Element Analysis (FEA/FEM)
- Automatic Control Systems
- Control Engineering
- Theory of Machines
- Solid Mechanics
- Computer Aided Machine Drawing
- Basic Thermodynamics
- Advanced Themodynamics
- Basic Mechanical Engineering
- Kinematics of Machines
- Engineering Drawing
- Machine Drawing
- Production Drawing
- Dimensions, Fits & Tolerances
- Dynamics of Machines
- Mechanical Vibrations
- Machine Design-I
- Machine Design-II
- Experimental Stress Analysis
- Heat and Mass Transfer
- Operations Research
AI / ML & Deep Learning Curriculum
- Mathematics (Linear Algebra, Calculus, Probability & Statistics)
- Introduction to programming in Python
- Supervised & Unsupervised Learning
- Deep Learning (Aritificial Neural Networks)
- Data Processing
- Natural Language Processing (NLP) and Computer Vision.
- Reinforcement learning, Generative AI, and Large Language Models (LLMs)
- Operations (MLOps)
- AI/ML Projects
- Python Programming (For IT & Allied)
- Introduction to AI & Applications
- Introduction to Prompt Engineering
- Industrial Applications of AI / Robotics, Drones using AI
- No Code / Low Code AI
- Agentic AI
Data Analytics & DS Curriculum
- Mathematics & Statistics (Linear algebra, multivariable calculus, probability theory, hypothesis testing, and statistical inference)
- Programming Languages: Python, R, and Java or Scala for coding and data manipulation.
- Databases & SQL: Relational databases, NoSQL, and Structured Query Language (SQL) for data extraction.
- Data Visualization: Matplotlib, Seaborn, Tableau, and Power BI for graphical data storytelling.
- Machine Learning: Supervised and unsupervised learning, regression, classification, and clustering algorithms.
- Big Data & Cloud Computing: Hadoop, Apache Spark, AWS, and Google Cloud for handling large datasets.
- Advanced Topics: Deep learning, Natural Language Processing (NLP), and computer vision.