Data Analysis - Use in AI
Data Analysis - Use in AI is a advanced-level, 8 Weeks online course by NSTC. Master key concepts and practical skills in Data Science through hands-on projects, real-world case studies, and expert mentorship.
- ModeOnline (e-LMS)
- LevelAdvanced
- Duration8 Weeks
- Fee₹10,749
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 8 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $145 |
| Tools | Python R Pandas NumPy Matplotlib Seaborn Tableau SQL |
About the Data Analysis - Use in AI Course
This course offers a comprehensive exploration into data analysis with a focus on its pivotal role in artificial intelligence.
It covers statistical methods, data management, and predictive analytics, enhancing skills in handling, analyzing, and interpreting data to drive AI innovations.
Program Highlights
• Comprehensive coverage of Data Analysis from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Data Science
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Data Science
• Career-oriented training for academic and professional growth in Data Science
Course Curriculum
Module 1: Introduction to Data Analysis
- Overview and historical evolution of Data Analysis
- Key terminology, definitions, and core concepts in Data Science
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of Data Analysis
- Mathematical and analytical frameworks relevant to Data Science
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Use in AI
- Core concepts and techniques in Use in AI
- Practical implementation and hands-on exercises
- Integration of Use in AI with Data Analysis workflows
- Case study: Real-world application of Use in AI
Module 4: EDA
- Introduction to EDA concepts and methodologies
- Step-by-step practical implementation of EDA techniques
- Tools and platforms commonly used for EDA
- Troubleshooting, optimization, and best practices
Module 5: Statistical Analysis
- Introduction to Statistical Analysis concepts and methodologies
- Step-by-step practical implementation of Statistical Analysis techniques
- Tools and platforms commonly used for Statistical Analysis
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Data Science
- Cutting-edge research and innovations in Data Analysis
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Data Science
Module 7: Capstone Project and Assessment
- End-to-end project implementation using Data Analysis skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
