BIG DATA MASTER
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Course sections
Section 1
Introduction to Big Data
1
Overview of Big Data
2
Key characteristics of Big Data
3
Understanding the challenges of Big Data
Section 2
Data Storage and Processing
1
Data storage systems (HDFS, NoSQL databases)
2
Data processing frameworks (MapReduce, Spark)
3
Distributed computing architecture
Section 3
Data Analysis
1
Data mining techniques
2
Data visualization and exploration tools
3
Machine learning algorithms for Big Data analysis
Section 4
Data Integration and Transformation
1
Extracting data from various sources
2
Data transformation and cleaning
3
Data integration techniques (ETL)
Section 5
Big Data Technologies
1
Hadoop ecosystem (Hive, Pig, HBase, Mahout)
2
Spark ecosystem (Spark SQL, MLlib, GraphX)
3
Real-time data processing systems (Kafka, Storm)
Section 6
Cloud Computing for Big Data
1
Cloud computing architecture (AWS, Azure, Google Cloud)
2
Cloud-based data storage and processing
Section 7
Section 8
1
Data security and privacy concerns in cloud-based environments
Section 9
Big Data Applications
1
Big Data in healthcare, finance, retail, and other industries
2
IoT and Big Data
3
Big Data for predictive analytics and decision-making
Section 10
Big Data Management
1
Big Data project management
2
Agile methodologies for Big Data projects
3
Team management and collaboration
Section 11
Emerging Trends in Big Data
1
Edge computing and Big Data
2
Blockchain and Big Data
3
Quantum computing and Big Data
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Data Storage and Processing
Data storage systems (HDFS, NoSQL databases)
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Introduction to Big Data
Understanding the challenges of Big Data
Complete
Data Storage and Processing
Data processing frameworks (MapReduce, Spark)
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