Algorithms for Big Data Processing
( 25 Modules )

Module #1
Introduction to Big Data
Overview of big data, its characteristics, and importance in modern computing

Module #2
Challenges in Big Data Processing
Discussion of challenges in processing and analyzing big data, including scalability, velocity, and variety

Module #3
Overview of Big Data Processing Tools
Introduction to popular big data processing tools, including Hadoop, Spark, and NoSQL databases

Module #4
MapReduce Programming
Introduction to MapReduce programming, including mapper, reducer, and combiner concepts

Module #5
Hadoop Ecosystem
Overview of the Hadoop ecosystem, including HDFS, YARN, and Hive

Module #6
Spark Fundamentals
Introduction to Apache Spark, including Resilient Distributed Datasets (RDDs) and DataFrames

Module #7
Spark SQL and DataFrames
In-depth look at Spark SQL and DataFrames, including data processing and querying

Module #8
Spark Streaming
Introduction to Spark Streaming, including real-time data processing and event-time processing

Module #9
NoSQL Database Fundamentals
Introduction to NoSQL databases, including key-value, document, and graph databases

Module #10
Designing Scalable Systems
Principles and best practices for designing scalable systems for big data processing

Module #11
Distributed Systems Architecture
Overview of distributed systems architecture, including master-slave, peer-to-peer, and microservices

Module #12
Algorithmic Techniques for Big Data
Introduction to algorithmic techniques for big data, including filter, sampling, and aggregation

Module #13
Data Compression and Encoding
Techniques for data compression and encoding, including Huffman coding and LZW compression

Module #14
Data Sketching and Approximation
Overview of data sketching and approximation techniques, including Bloom filters and Count-Min sketches

Module #15
Clustering and Classification
Introduction to clustering and classification algorithms for big data, including k-means and decision trees

Module #16
Graph Processing and Network Analysis
Overview of graph processing and network analysis, including GraphX and NetworkX

Module #17
Anomaly Detection and Outlier Analysis
Introduction to anomaly detection and outlier analysis, including statistical and machine learning approaches

Module #18
Big Data Visualization
Overview of big data visualization, including visualization tools and best practices

Module #19
Real-time Analytics and IoT
Introduction to real-time analytics and IoT, including stream processing and edge computing

Module #20
Security and Governance in Big Data
Overview of security and governance in big data, including data encryption and access control

Module #21
Big Data Case Studies
Real-world case studies of big data processing and analytics in various industries

Module #22
Scaling Up and Down
Best practices for scaling up and down big data systems, including load balancing and autoscaling

Module #23
Testing and Debugging Big Data Systems
Introduction to testing and debugging big data systems, including unit testing and integration testing

Module #24
Big Data Ethics and Social Responsibility
Discussion of big data ethics and social responsibility, including data privacy and fairness

Module #25
Course Wrap-Up & Conclusion
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Algorithms for Big Data Processing ( 25 Modules )

Algorithms for Big Data Processing
( 25 Modules )