lpyuan/Big-Data-Course-Material

Python Project

Santa Clara University

Big-Data-MapReduce-Course

Course Information: Spring 2018

• Graduate Business, Leavey School of Business
• Course MSIS 2641: Big Data Modeling & Analytics
• Big-Data-MapReduce Course @ Santa Clara University
• Class Meeting dates: 04/03/2018 - 06/08/2018
• Class hours: Class Number 68044: TTh 5:45PM - 7:00PM PST
• Class hours: Class Number 68041: TTh 7:35PM - 8:50PM PST
• Class room: Lucas Hall 310
• Office: 321 T, Lucas Hall

Required Books and Papers

• 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

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Midterm Exam

• Midterm Exam: TBDL, @ 5:45pm to 7:00pm PST
• Midterm Exam: TBDL, @ 7:35pm to 8:50pm PST

Final Exam

Final Exam Class Number 68044

• Class hours: TTh 5:45PM - 7:00PM PST
• Final Exam Date: Tuesday, June 12, 2018
• Final Exam Time: 5:45PM - 7:00PM PST

Final Exam Class Number 68041

• Class hours: TTh 7:35PM - 8:50PM PST
• Final Exam Date: Thursday, June 14, 2018
• Final Exam Time: 5:45PM - 7:00PM PST

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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