Simon Fraser University
Engaging the World

Course Outlines

Fall 2019 - CMPT 732 G100

Programming for Big Data 1 (6)

Class Number: 9020

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2019: Mon, 12:30–2:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

This course is one of two lab courses that are part of the Professional Master’s Program in Big Data in the School of Computing Science. This lab course aims to provide students with the hands-on experience needed for a successful career in Big Data in the information technology industry. Many of the assignments will be completed on massive publically available data sets giving them appropriate experience with cloud computing and the algorithms and software tools needed to master programming for Big Data. Over 13 weeks of lab work and 12 hours per week of lab time, the students will obtain a solid background in programming for Big Data.

COURSE DETAILS:

Many companies today collect massive amounts of data that cannot be managed without proper programming techniques. This lab courses focuses on the practical aspects of dealing with such data. It will provide insight on MapReduce, Spark, NoSQL databases, cloud computing, and data analytics for large data sets. Instructor's Objectives: ------------------------ The objective of this class is to ensure that students will be able to: - Use a distributed file system such as (or similar to) HDFS (Hadoop Distributed File System). - Write software that can interact with a distributed file system using programming tools that are part of Apache Hadoop. - Write simple distributed software using common tools. - Be able to formulate and implement queries on large data sets. - Write software that can interact with at least one non-relational database.

Topics

  • Big data storage and analysis
  • Hadoop ecosystem
  • MapReduce
  • NoSQL database (HBase, Cassandra)
  • Cloud computing
  • Data analytics
  • Spark
  • Data Ingestion (Kafka)

Grading

NOTES:

To be discussed in the first week of class. Will include regular lab assignments, and a final project.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS