AI & Data
Science
From data wrangling to deep learning — build real-world AI models using cutting edge frameworks and OpenAI APIs.
Technologies You'll Master
Learning Outcomes
Master the Curriculum
A carefully structured syllabus designed by industry experts — progressive mastery from ground zero to production-level readiness.
Month 1
Foundations
- Introduction to Data Science and AI
- Applications and industry use-cases
- Python setup (Anaconda, Jupyter)
- Basic Python syntax
- Weekly Test 1
Real-World Project Execution
As part of the AI & Data Science Internship Program, project-based learning serves as a critical component in enabling students to translate theoretical knowledge into practical application. The following project allocation framework has been designed to provide structured exposure across varying complexity levels, ensuring progressive skill development.
Learning Stages
- Set 1 & Set 2: Foundational (Basic Level)
- Set 3 & Set 4: Intermediate (Medium Level)
- Set 5: Advanced (Industry-Level Capstone)
Implementation Guidelines
- Problem Definition and Requirement Analysis
- Data Collection and Preprocessing
- Exploratory Data Analysis and Feature Engineering
- Model Development and Evaluation
- Optimization and Validation
- Deployment (where applicable)
- Documentation and Presentation
- Projects in advanced stages must include cloud deployment using AWS or Microsoft Azure, ensuring real-world exposure.
Expected Outcomes
- Demonstrate practical implementation of AI and Data Science concepts
- Develop end-to-end solutions using structured workflows
- Gain experience in real-world datasets and problem-solving
- Build professional portfolios aligned with industry expectations
- Acquire exposure to deployment and cloud-based environments
Project Set 1
This set focuses on fundamental data handling, visualization, and introductory analysis.
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Started?
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