Name: Certified Big Data Analytics (Hadoop / Spark)
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Course Title: Certified Big Data Analytics (Hadoop / Spark)
Instructor: Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
Target Audience:
- Aspiring Big Data engineers and data analysts
- IT professionals transitioning to distributed data processing
- Software developers seeking Hadoop/Spark expertise
- Data scientists needing scalable analytics skills
- Students pursuing Big Data certifications
Prerequisites:
- Basic knowledge of Linux commands
- Familiarity with Python or Java (for Spark modules)
- Understanding of database concepts (recommended but not mandatory)

Key Topics Covered:
- Hadoop Ecosystem Fundamentals
  - HDFS (Hadoop Distributed File System) architecture and operations
  - YARN (Yet Another Resource Negotiator) for cluster management
  - MapReduce principles and limitations
- Apache Spark Core Concepts
  - RDDs (Resilient Distributed Datasets) and transformations
  - PySpark for data manipulation (Python-based)
  - Spark SQL and DataFrames for structured data
- Performance Optimization
  - Data serialization (Parquet, ORC formats)
  - Cluster tuning for large-scale workloads
  - Fault tolerance and job scheduling
- Real-World Applications
  - ETL (Extract, Transform, Load) pipelines
  - Batch and stream processing use cases
  - Enterprise scaling challenges and solutions
Key Skills Learned:
- Setting up and managing Hadoop/Spark clusters
- Writing efficient Spark scripts in PySpark
- Optimizing distributed data storage (HDFS)
- Designing scalable data pipelines
- Troubleshooting performance bottlenecks in Big Data systems
- Preparing for industry-recognized Big Data certifications

Duration:
- 3 practice tests (simulated certification exams)
- Self-paced (lifetime access to materials)
Format:
- On-demand video lectures
- Mobile and TV access
- Practical exercises with real-world datasets
Resources:
- Downloadable scripts and configuration templates
- Quizzes for knowledge reinforcement
- Project simulations for hands-on experience