44-517 Course Syllabus

Area

School of Computer Science and Information Systems

Course Title

44-517 Big Data

Course Credit

3 hours

Placement in Curriculum

This course is typically offered in the later years of an undergraduate degree or after the first semester of a graduate program.

Prerequisites

Undergraduate prerequisites: MATH 17230 or MATH 17316 with a grade of C or better and CSIS 44242 with a grade of C or better. Graduate prerequisites: CSIS 44542 with a grade of B or better, or concurrent enrollment in CSIS 44542, or consent of instructor.

Section Details

Spring 2021
Sec 01 - MWF 1-1:50pm CH 3300
Sec 02 - MWF 2-2:50pm CH 3300

Course Description

An introduction to the design of data-intensive, reliable, scalable, and maintainable systems. This may include concepts such as parallel programming, distributed computing, distributed file systems, MapReduce, regular expressions, and the ingesting and processing of data at-rest and data in motion. Tools used may include Hadoop, HDFS, Pig, Hive, Spark, Storm, Kafka, Mahout, MLlib, etc.

Course Rationale

This course involves an overview the design and implementation of big data solutions covering common approaches to processing big data at rest and data in motion.

Student Learning Outcomes

Compentency	BS Data Science Program Outcome	Assessment
Managing Information	DSI students will access, generate, and reorganize information using contemporary technologies.	Selected assignment(s)
Teamwork	DSI Students will work as a team to design, implement, and deliver solutions to problems using best practices with contemporary technologies.	Selected assignment(s)

Additional student learning outcomes include:

Materials

Recommended references

• Mining of Massive Datasets by Leskovec, Rajaraman, and Ullman, Third Edition, 2019.
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann, 2017 (preview).
• Big Data by Nathan Marz with James Warren, 2015.
• Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer, 2010.
• MapReduce: Simplified Data Processing on Large Clusters by Jeffrey Dean and Sanjay Ghemawat, 2004.
• Cornell Java reference (online book) by Professor David Gries.
• MIT Software Construction Reading 25: Map, Filter, Reduce.

Stanford CS 102: Working with Data - Data Sets.

Required

Students must have access to the following at every course meeting:

Instructional Methods and Techniques

Instructional methods include lectures, class discussions, individual work, small group work, learner presentations, discussions, guest speakers, and collaborative development.