Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Data Engineering for AI covers the unglamorous foundation that every model and dashboard quietly relies on: trustworthy, timely data. You will design ingestion from files, APIs and streams; build transformation pipelines; and model data for warehouses and lakes. The course covers batch and streaming patterns, workflow orchestration, data quality and schema management, and the trade-offs between them. Throughout, the lens is machine learning: shaping feature-ready datasets and keeping pipelines reproducible. You leave able to design and operate a pipeline that delivers clean data on schedule. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Data Engineering for AI teaches the design and operation of data pipelines — ingestion, transformation, warehousing and orchestration — that feed analytics and machine learning.
📋 Course Objectives
1. Design ingestion from files, APIs and streaming sources.
2. Build batch and streaming transformation pipelines.
3. Model data for warehouses and lakes.
4. Orchestrate workflows and manage data quality and schemas.
5. Prepare feature-ready, reproducible datasets for ML.
👥 Who Should Enroll?
• Developers and analysts moving into data engineering
• ML practitioners who need dependable data pipelines
• Backend engineers building data platforms
• Students specialising in data infrastructure
🚀 Key Learning Outcomes
• The ability to design and operate a production data pipeline.
• A data-engineering project demonstrating the full flow.
• Skills that underpin reliable analytics and ML.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Data Engineering Foundations
Develop a comprehensive understanding of AI fundamentals, including machine learning and deep learning concepts • Analyze mathematical prerequisites for data engineering, such as linear algebra, calculus, and probability theory • Design a data engineering framework for AI applications, incorporating data ingestion, processing, and storage
Data Engineering, Preprocessing, and Feature Pipelines
Implement data preprocessing techniques, including data cleaning, feature scaling, and normalization • Configure data pipelines using Apache Beam, Apache Spark, or other data processing frameworks • Evaluate the effectiveness of feature engineering techniques, such as feature selection and dimensionality reduction
Model Architecture, Algorithm Design, and Data Engineering for AI Methods
Design and implement neural network architectures using TensorFlow, PyTorch, or Keras • Develop and evaluate machine learning algorithms, including supervised, unsupervised, and reinforcement learning • Optimize model performance using hyperparameter tuning and model selection techniques
Training, Hyperparameter Optimization, and Evaluation
Train machine learning models using various optimization algorithms, such as stochastic gradient descent and Adam • Implement hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization • Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score
Deployment, MLOps, and Production Workflows
Deploy machine learning models using containerization techniques, such as Docker and Kubernetes • Implement MLOps practices, including model monitoring, logging, and version control • Design and manage production workflows using Apache Airflow, Apache NiFi, or other workflow management tools
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI systems, including fairness, transparency, and accountability • Implement bias mitigation techniques, such as data preprocessing and model regularization • Develop and evaluate responsible AI practices, including model interpretability and explainability
Industry Integration, Business Applications, and Case Studies
Integrate data engineering and AI concepts into various industries, such as healthcare, finance, and retail • Develop and evaluate business applications of AI, including recommender systems and natural language processing • Analyze case studies of successful AI implementations, including challenges, opportunities, and best practices
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Apache Spark |
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