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 Planning next steps in Algorithms for Big Data Processing career
