Course Description
The course develops the ability to design highly scalable systems that can process, store and analyze large amounts of unstructured/structured data. Course covers topics such as Hadoop architecture, Hadoop Distributed File system, Map-Reduce programming. SQL-like access to unstructured data with Pig and Hive. NoSQL storage solution HBase. Statistical analysis with Mahout and R.
Course Outcomes
- Demonstrate a robust understanding of big data concepts, challenges, and opportunities, including their relevance to AI-driven and cybersecurity analytics.
- Demonstrate a solid understanding of the role of distributed systems, particularly the Hadoop ecosystem including HDFS, MapReduce, YARN, and associated technologies in managing and processing large datasets.
- Apply scalable data processing techniques using Hadoop technologies to support advanced analytics and AI-assisted assessments.
- Utilize advanced statistical and machine learning methods adapted for the Hadoop ecosystem.
- Effectively visualize and interpret insights derived from large datasets, integrating appropriate visualization tools.
- Recognize and address ethical and legal considerations in big data analytics.
- Apply big data analytics to practical scenarios using the Hadoop ecosystem and other tools in diverse domains such as business intelligence, cybersecurity, healthcare, finance, and social media analytics.
