ZhangSirM/big-data-mapreduce-course

Big Data, MapReduce, Spark, PySpark, Java @ Santa Clara University, SPRING 2017

Santa Clara University

Big-Data-MapReduce-Course

Course Information

• Fall 2017: Graduate Business, Leavey School of Business
• Course MSIS 2641: Big Data Modeling & Analytics
• Big-Data-MapReduce Course @ Santa Clara University
• Class duration: September 18 - December 7, 2017
• Class hours:
  • Monday: 5:45pm - 7:00pm PST
  • Wednesday: 5:45pm - 7:00pm PST
• Class room: Lucas Hall 210
• Office: 321 T, Lucas Hall
• Required books and papers (all resources are online):
  • 1. A
 Very
Brief 
Introduction
 to 
MapReduce by Diana 
MacLean
  • 2. Introduction to MapReduce by Mahmoud Parsian
  • 3. Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer
  • 4. Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman

Syllabus

Exam Dates

• Midterm Exam: October 2017 (possibly end of October), from 5:45pm to 7:00pm PST
• Final Exam: December 4-7, 2017 from 5:45pm-7:45pm PST

Course Description

The main focus of this class is to cover the following concepts:

• Concepts of Big Data
• Distributed File Systems
• Distributed Computing
• Distributed and Parallel Algorithms
• MapReduce Paradigm
• MapReduce Algorithms
• Scale-out Architectures (using Hadoop, Spark, PySpark)
• Apache Spark: http://spark.apache.org/
• Use Spark, Py-Spark, Hadoop, and Java to teach MapReduce and distributed computing
• SQL for NoSQL Data, How?

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My latest book:

Data Algorithms Book

Data Algorithms: Recipes for Scaling up with Hadoop and Spark