Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course provides an in-depth exploration of Apache Spark, its core concepts, and its integration with AI technologies to enhance machine learning capabilities and handle big data with ease. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course provides an in-depth exploration of Apache Spark, its core concepts, and its integration with AI technologies to enhance machine learning capabilities and handle big data with ease.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
Introduction to Apache Spark
Understand Spark ecosystem and advantages over traditional frameworks • Explore core components and architecture • Set up Spark environment for hands‑on labs
Spark Core Concepts
Manipulate data using RDDs and DataFrames • Write efficient transformations and actions • Optimize performance with caching and partitioning
Spark for AI Applications
Apply MLlib algorithms to build machine‑learning models • Integrate Spark with TensorFlow and PyTorch • Deploy scalable AI pipelines on Spark clusters
Advanced Data Processing
Implement real‑time stream processing with Structured Streaming • Handle massive datasets using Spark SQL and Catalyst optimizer • Tune jobs for high‑throughput AI workloads
Real‑World Applications & Case Studies
Explore Spark use‑cases in healthcare, finance, and e‑commerce • Analyze performance tuning techniques • Adopt best practices for production AI systems
Capstone Project
Design an end‑to‑end Spark AI solution • Implement data ingestion, model training, and deployment • Present findings and receive expert feedback
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Apache Spark |
| Covered Tool / Platform | MLlib |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Structured Streaming |
| Covered Tool / Platform | Hadoop |
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