IS 452 Big Data and the Internet of Things (Iot)

Marco Chou

Fall 2020-2021
Class number: 10299
Section number: 701
OLASY NCH00 Online Campus


This course surveys system design concepts, techniques, and algorithms in Machine to Machine (M2M), Internet of Things (IoT), and Internet of Everything (IoE). Topics covered include system architecture for big data, sensors and embedded technologies, IoT architecture, consumer vs. industrial IoT, wearable and mobile systems, tracking systems, IoT and big data analytics, market dynamics and entrepreneurial opportunities. Special emphasis is placed on identifying best practices in using big data and IoT through case studies and hands-on exercises.


Internet of Things (A Hands-on-Approach) by Vijay Madisetti, Arshdeep Bahga


Grading Lab 60%, Homework 20%, Midterm 10%, Final 10%. No late homework will be accepted after one week from the due date. Any late homework will lose 20% credit. If you can not attend the test, you must present certain document to show that you have a valid reason. Exam material will be drawn from lecture notes and homework assignments. Grading Scale: A : 93-100 A-: 90- 92 B+: 87- 89 B : 83- 86 B-: 80- 82 C+: 77- 79 C : 73- 76 C-: 70- 72 D : 60- 69 F : 0- 59



Learning Objectives

Students will be able to ? Explain the value of Big Data and IoT for organizations across many industry sectors ? Describe how IoT is interacting with and impacting Big Data implementations ? Define IoT ecosphere including technologies, business verticals, regulations and business opportunities ? Identify entrepreneurs opportunity to get started with building their own IoT devices and solutions ? Experiment with different hardware and software products and technologies and apply the appropriate ones to provide end-to-end solutions for the business ? Use data visualization to present time-series data gathered from IoT

Office Hours

Mon-Thur 10AM-5PM (appointment request by email -

Introduction, Technology Trends (Cloud Computing, Big Data Analytics, Mobile and Social)

Consumer IoT: Smart home, Healthcare, etc. Industrial IoT - Smart Car, Smart Factory, etc. Internet, Fog Computing/Edge Computing midterm IBM Bluemix and IoT Microsoft Azure and IoT Smart Building, Smart City Wearable Device Raspberry Pi, Summary

School policies:

Changes to Syllabus

This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.

Online Course Evaluations

Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation. Students complete the evaluation online in CampusConnect.

Academic Integrity and Plagiarism

This course will be subject to the university's academic integrity policy. More information can be found at If you have any questions be sure to consult with your professor.

Academic Policies

All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at

Students with Disabilities

Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002
Fax: (312)362-6544
TTY: (773)325.7296