| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 6 Months |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python R TensorFlow NumPy Pandas Apache Beam Spark |
About the Data Analysis for AI Course
Data Analysis – Use in AI Course dives deep into Data Analysis – Use In Ai.
Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Comprehensive coverage of Data Analysis for AI 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
• Practical experience with tools: Python, R, TensorFlow, NumPy
• Career-oriented training for academic and professional growth in Data Science
Course Curriculum
Module 1: AI Fundamentals, Mathematics, and Data Analysis Foundations
- Analyze the fundamentals of artificial intelligence and its applications in data analysis
- Develop a deep understanding of mathematical concepts such as linear algebra, calculus, and probability theory
- Design a data analysis pipeline using Python and relevant libraries such as NumPy and Pandas
Module 2: Data Engineering, Preprocessing, and Feature Pipelines
- Configure data engineering workflows using tools such as Apache Beam and Spark
- Implement data preprocessing techniques such as handling missing values and data normalization
- Evaluate the effectiveness of feature engineering techniques such as feature scaling and encoding
Module 3: Model Architecture, Algorithm Design, and Data Analysis Methods
- Design and implement machine learning models using algorithms such as regression, classification, and clustering
- Develop a deep understanding of model architecture and hyperparameter tuning
- Analyze the performance of different models using metrics such as accuracy, precision, and recall
Module 4: Training, Hyperparameter Optimization, and Evaluation
- Train machine learning models using techniques such as cross-validation and grid search
- Optimize hyperparameters using tools such as Hyperopt and Optuna
- Evaluate the performance of models using metrics such as mean squared error and R-squared
Module 5: Deployment, MLOps, and Production Workflows
- Deploy machine learning models using tools such as Docker and Kubernetes
- Implement MLOps workflows using tools such as TensorFlow Extended and MLflow
- Configure production workflows using tools such as Apache Airflow and AWS Step Functions
Module 6: Ethics, Bias Mitigation, and Responsible AI Practices
- Analyze the ethical implications of AI systems and develop strategies for bias mitigation
- Develop a deep understanding of responsible AI practices such as transparency, accountability, and fairness
- Implement techniques for detecting and mitigating bias in AI systems
Module 7: Industry Integration, Business Applications, and Case Studies
- Develop a deep understanding of industry applications of AI and data analysis
- Analyze case studies of successful AI implementations in various industries
- Design and implement AI solutions for real-world business problems
Tools, Techniques, or Platforms Covered
Python R TensorFlow NumPy Pandas Apache Beam Spark
Real-World Applications
- Apply Data Analysis for AI skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Data Science competencies
- Solve industry-relevant problems using Data Analysis for AI methodologies and tools
- Contribute to open-source projects and collaborative research in Data Science
- Prepare for competitive examinations, interviews, and professional certifications in Data Science
Who Should Attend & Prerequisites
- Designed for Professionals.
- Designed for Students.
- Foundational knowledge of artificial intelligence and familiarity with core concepts recommended.
- Mentorship by industry experts and NSTC faculty.
Certification
