Big Data Analytics and Applications
( 25 Modules )

Module #1
Introduction to Big Data
Overview of big data, its significance, and importance in various industries

Module #2
Big Data Characteristics
Understanding volume, velocity, variety, veracity, and value of big data

Module #3
Big Data Technology Stack
Overview of Hadoop, Spark, NoSQL databases, and other big data technologies

Module #4
Hadoop Fundamentals
Introduction to Hadoop ecosystem, HDFS, and MapReduce

Module #5
Spark Fundamentals
Introduction to Apache Spark, Spark Core, and Spark SQL

Module #6
NoSQL Database Fundamentals
Introduction to NoSQL databases, key-value stores, and document-based databases

Module #7
Data Ingestion and Processing
Ingesting and processing big data using Sqoop, Flume, and Kafka

Module #8
Data Warehousing and ETL
Designing and implementing data warehouses, ETL processes, and data pipelines

Module #9
Big Data Analytics Tools
Introduction to big data analytics tools such as Hive, Pig, and Impala

Module #10
Data Visualization
Visualizing big data using Tableau, Power BI, and D3.js

Module #11
Machine Learning Fundamentals
Introduction to machine learning, supervised and unsupervised learning

Module #12
Machine Learning with Spark
Implementing machine learning algorithms using Spark MLlib

Module #13
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and TensorFlow

Module #14
Big Data Applications in Industry
Applications of big data in healthcare, finance, and retail

Module #15
Big Data in IoT
Handling and analyzing IoT data using big data technologies

Module #16
Big Data Security and Governance
Ensuring security and governance in big data environments

Module #17
Big Data and Cloud Computing
Deploying big data solutions on cloud platforms such as AWS and Azure

Module #18
Big Data Case Studies
Analyzing real-world big data case studies and applications

Module #19
Predictive Analytics
Building predictive models using big data and machine learning

Module #20
Real-time Analytics
Implementing real-time analytics using Spark Streaming and Kafka

Module #21
Graph Analytics
Analyzing graph data using big data technologies and graph databases

Module #22
Text Analytics
Analyzing unstructured data using text analytics and natural language processing

Module #23
Big Data and AI
Applications of artificial intelligence in big data analytics

Module #24
Big Data and Ethics
Ethical considerations in big data analytics and applications

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Big Data Analytics and Applications career

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Big Data Analytics and Applications ( 25 Modules )

Big Data Analytics and Applications
( 25 Modules )