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TAs: Ruben (Head TA), Dan, Eason, Jennifer, John, Shuo, Wilson, Yachen
TA Office Hours:
MON 11am-1pm (Jennifer and Ruben) in Jolley 431 TUE 6:30-8:30pm (Eason) in Lopata 229 WED 12-2pm (Wilson) in Jolley 224 THU 11:30am-1:30pm (Dan) in Jolley 431 FRI 11am-1pm (Shou and John) in Jolley 431 SAT 10am-12pm (Yachen) in Lopata 229
This course provides a comprehensive introduction to applied parallel computing using the MapReduce programming model facilitating large scale data management and processing. There will be an emphasis on hands-on experience working with the Hadoop architecture, an open-source software framework written in Java for distributed storage and processing of very large data sets on computer clusters. Further, we will derive and discuss various algorithms to tackle big data applications and make use of related big data analysis tools from the Hadoop ecosystem, such as Pig, Hive, Impala, and Apache Spark to solve problems faced by enterprises today. Check the Roadmap for more detailed information.
Prerequisites: CSE 131 (solid background in programming with Java), CSE 247, and CSE 330 (basic knowledge in relational databases (RDMS), SQL, and AWS).
This class counts towards the Certificate in Data Mining and Machine Learning as applications course.
This class uses materials from the Cloudera Developer Training for MapReduce, the Cloudera Data Analyst Training: Using Pig, Hive, and Impala with Hadoop, and the Cloudera Developer Training for Apache Spark, which are made available to Washington University through the Cloudera Academic Parntership program. Further contents are based on the “Mining of Massive Data Sets” book and class taught at Stanford by Jure Leskovec.
Instructor: Marion Neumann
Office: Jolley Hall Room 222
Contact: Please use Piazza!
Office Hours: TUE 3-4pm (or individual appointment* – avoid drop ins w/o appointment outside the office hours)
*request individual appointments via email and allow for 2-3 days reply and scheduling time
Section 1: 1-2:30pm
Section 2: 4-5:30pm
Grades on BB
Piazza (sign-up using your wustl email address)
Please ask any questions related to the course materials and homework problems on Piazza. Other students might have the same questions or are able to provide a quick answer.
Any postings of (partial) solutions to problems (written or in form of source or pseudo code) will result in a grade of zero for that particular problem for ALL students.