DSC 333 Introduction to Big Data Processing
Summary
This is a graduate course in large scale data mining applications. The map-reduce programming paradigm is a fundamental tool used in processing large data sets and is supported in tools such as Hadoop. In this course, you will learn the basics of map-reduce and its related technologies for big data processing.
Specific topics to be covered include:
- Fundamentals of distributed file systems and MapReduce (MR) technology.
- Advantages of an MR-based system compared to a relational database.
- Tuning MR algorithm performance and tools for mining massive data sets.
- Applications in clustering, similarity search, classification, data warehousing (e.g., Hive), machine learning (e.g., Mahout).
- Modern Big Data frameworks such as Spark, Kafka, Flink, etc.
Texts
1. Mining of Massive Datasets, by Anand Rajaraman and Jeffrey D. Ullman
2. Hadoop: The Definitive Guide, by Tom White, O'Reilly Media, 4th edition 2015.
Prerequisites
If you are not sure that you have satisfied the prerequisites, speak to the instructor before the second lecture.
- Prerequisite Courses: (CSC 453 or DSC 450) and (DSC 441 or CSC 480) or (MAT 491 and MAT 449).
- Prior experience with Python.
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
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