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
Big Data Analytics with AI is about extracting insight when data no longer fits on one machine. You will learn the distributed-computing model behind Apache Spark, work with resilient dataframes, and build ETL and analysis pipelines over large datasets. The course then layers machine learning on top — training and scoring models at scale with Spark MLlib — and covers the practical architecture around it: storage formats, partitioning and pipeline orchestration. You finish able to design and run an analytics workflow that scales from a laptop sample to a full cluster. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Big Data Analytics with AI teaches distributed processing with Spark and the modern data stack, then applies machine learning to datasets too large for a single machine.
📋 Course Objectives
1. Explain the distributed-computing model behind Spark.
2. Build ETL and analysis pipelines over large datasets.
3. Work with Spark dataframes and SQL at scale.
4. Train and score ML models with Spark MLlib.
5. Design storage, partitioning and orchestration for scale.
👥 Who Should Enroll?
• Data engineers and analysts working with large data
• Data scientists scaling models beyond one machine
• Backend engineers moving into data platforms
• Students specialising in big-data systems
🚀 Key Learning Outcomes
• The ability to build a scalable analytics pipeline.
• Hands-on experience with Spark and big-data tooling.
• A distributed data project for your portfolio.
• 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 Big Data Analytics Foundations
Apply linear algebra and calculus concepts to optimize AI model performance • Develop probabilistic models using Bayesian inference and statistical reasoning • Analyze big data sets using data visualization techniques and dimensionality reduction methods
Data Engineering, Preprocessing, and Feature Pipelines
Design scalable data pipelines using Apache Beam and Google Cloud Dataflow • Implement data preprocessing techniques such as tokenization, stemming, and lemmatization • Configure data quality checks and data validation using Apache Airflow and Great Expectations
Model Architecture, Algorithm Design, and Big Data Analytics Methods
Evaluate the performance of different deep learning architectures such as CNNs and RNNs • Develop recommender systems using collaborative filtering and matrix factorization • Optimize model hyperparameters using grid search, random search, and Bayesian optimization
Training, Hyperparameter Optimization, and Evaluation
Train neural networks using stochastic gradient descent and Adam optimizer • Implement hyperparameter tuning using Optuna and Hyperopt • Evaluate model performance using metrics such as accuracy, precision, and F1-score
Deployment, MLOps, and Production Workflows
Deploy models using TensorFlow Serving and AWS SageMaker • Configure continuous integration and continuous deployment (CI/CD) pipelines using Jenkins and GitLab • Implement model monitoring and logging using Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze bias in AI models using fairness metrics and bias detection tools • Develop strategies for mitigating bias and ensuring fairness in AI systems • Evaluate the ethical implications of AI systems using case studies and scenario planning
Industry Integration, Business Applications, and Case Studies
Apply AI and big data analytics to real-world business problems such as customer segmentation and churn prediction • Develop business cases for AI adoption using cost-benefit analysis and ROI calculation • Evaluate the impact of AI on business operations using case studies and industry reports
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Spark |
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