Spring 2020

 Instructor Information


  Tony T. Luo (homepage)


  307 Computer Science Building

  Office Hours

  Tuesday 4:30-6:00 PM or by appointment


  (573) 341-4788


   tluo <at> mst <dot> edu


Course Information

Course Name

Internet of Things with Applied Data Science

Course ID & Section


Credit Hours



Spring 2020


205 Computer Science Building

Course Structure

F2F (transitioned to online since March 16 due to COVID-19)

Course Website



Course Description

Internet of Things (IoT) is no longer a fiction but has permeated into our daily lives. IoT devices such as smartphones, wearable gadgets, household appliances, and connected vehicles measure physical parameters, interact with human beings, exchange information with each other, and produce data in an unprecedented scale.


In the meantime, Data Science has been gaining remarkable momentum and is pervasively transforming the way we work, live, and think. It derives deep insights from a plethora of data and is tremendously helping businesses, individuals, and machines make smarter decisions and take swifter actions.


This course attempts to synergize these two thrilling fields for innovative value creation. IoT is the forerunner of large-scale data generation and Data Science provides excellent tools to transform data into actionable wisdom.


But what are the building blocks of IoT? What are the underlying technologies that drive the IoT revolution? What are the powerful Data Science techniques? How to apply Data Science techniques to IoT and solve real problems?


This course will equip you with necessary knowledge and skills in the field of Internet of Things and that of Data Science, and elaborate on the intersection between these two fields. This course also aims to develop your ability of analytical and critical thinking and groom you to conduct innovative research on an interesting topic of your choice.


Prerequisites: COMP SCI 2500 and 3800; STAT 3113 / 3115 / 3117 / 5643 (any). General understanding of computer and wireless networks (e.g., you should be familiar with the OSI model and TCP/IP protocol stack; types of networks such as LAN, MAN, WAN, and PAN; fundamentals of wireless communications such as RF bands, channels, interference, CSMA, etc.).

Course Outcomes

By the end of the course, you should be able to:

1)    Explain and illustrate fundamental concepts and building blocks in IoT and Data Science, and review and critique technical articles in terms of novelty, viability, and overall quality.

2a) Apply data science to solve a problem that you identify in an existing IoT system, OR

2b) Build an IoT system with data science capabilities to solve a real-world problem you identify;

3)  Communicate your proposal and accomplishments in a professional context effectively.

Course Materials

      Textbooks (optional):

      Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd  Edition), 2019 [Free eBook available]

      Simone Cirani; Gianluigi Ferrari; Marco Picone; Luca Veltri, Internet of Things: Architectures, Protocols and Standards, Wiley, November 12, 2018. [Free eBook accessible from library]

      Timothy Chou, Precision: Principles, Practices and Solutions for the Internet of Things, 2016

      Joel Grus, Data Science from Scratch, 2nd Edition, O'Reilly, May 3, 2019. ISBN: 978-1-4920-4113-9. [Free eBook accessible from library]

      Amita Kapoor, Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems, Packt Publishing, January 31, 2019. [Free eBook accessible from library]

      The above texts are not compulsory, because in this course we are dealing with a fast-evolving interdisciplinary field which tends to make any textbook somewhat stale by the time you read it. On the other hand, I did spend some time screening through these books. So, read them at your discretion.

      Compulsory Reading: I will hand-pick a list of recent and closely relevant technical articles and research papers for you to read, and you are supposed to write something about what you learn from reading them. Should time permit, we will also discuss some readings in class. The goal is to keep you abreast of the latest developments in this vibrant field and to develop your ability of analytical and critical thinking.

      Lecture slides / notes: will be uploaded to course website or Canvas.

      Other resources: software kits, tools, equipment, etc.

Course Policies

      Classroom expectations:

      No phones (unless you are expecting an emergency call);

      Avoid laptop use as much as possible (unless required by a particular class).

      Assignment expectations: All due dates shall be adhered to. There will be a limited time window for accepting slightly late submissions, yet down-scaled by 50%; any subsequent submissions will not be entertained. On the other hand, I will allocate a “Grace Quota” to waive your lowest score(s) (Bam!). So, your life won’t be that tough.

Course Schedule

Sign up here if you are auditing the class, in order to receive course announcements and download course materials.




Course Materials

Assignments / Projects


Jan 27

·       Introduction to the Course

·       Introduction to the Internet of Things and Data Science

Lecture slides





Feb 3

IoT Wireless Technologies: Architectures, Protocols and Standards

Lecture slides



(#1 Due: Feb 9, 11AM)


Feb 10

Reading Week / Briefing of Capstone Project



Details in Email


Feb 17

·       Feedback on Assignments

·       Discussions on Readings

·       More on Capstone Project


#2 Due: Feb 22, 11AM


Feb 24

·       IoT Security

·       IoT Development Kits

·       Feedback on Assignments

·       Security slides

·       Dev-kit slides

·       Infographic

·       Comparison table


#3 Due: Feb 29, 11AM


Mar 2

·       Capstone Project Proposal Preview

·       Data Science with Python & Numpy

·       Lecture slides 1

·       Lecture slides 2

·       Jupyter notebook – Python & Numpy

·       Quiz

·       Solutions



Mar 9

·       Feedback on Assignments

·       Capstone Project Proposal Presentation

·       Data Science with Pandas (1)

Jupyter notebook (see below)



Mar 16

·       Data Science with Pandas (2)

·       Lecture slides

·       Jupyter notebook

·       CSV files

·       Additional video (75 MB) and notebook

Programming Assignments:

2, 3, 4

Due: Mar 28, 11AM (note format; see slides)

Data files


Mar 23

Spring Break: Sharpen Your Saw

·       Data Science with Matplotlib (1)

Video & Jupyter notebook (on Canvas)



Mar 30

·       Visualization using Matplotlib (2)

Jupyter notebook (on Canvas)



Apr 6

·       Capstone Project Checkpoint

·       Introduction to Machine Learning

·       Machine Learning with Scikit-learn

·       Lecture slides

·       Jupyter notebook

(both on Canvas)



Apr 13

Machine Learning with Scikit-learn: Supervised Learning (1A)

·       Lecture slides

·       Jupyter notebook

·       Data files

(all on Canvas)

Programming Assignments 1, 2A (on Canvas)

Due: Apr 18, 11 AM


Apr 20

Machine Learning with Scikit-learn: Supervised Learning (1B)

·       Lecture slides

·       Jupyter notebook

Programming Assignments

Due: Apr 25, 11 AM


Apr 27

Machine Learning with Scikit-learn: Evaluation

·       Lecture slides

·       Jupyter notebook

Programming Assignments

Due: May 3, 1 PM


May 4

·       Machine Learning with Scikit-learn: Supervised Learning (2)

·       Machine Learning with Scikit-learn: Unsupervised Learning

·       Lecture slides



May 14

(7:30 -9:30pm)


Capstone Project Assessment


Package Submission: May 16, 6 PM.

Grading Policies

·       Rubric for Writing Assignments

·       Rubric for Research Poster (see Canvas)

·       Rubric for Research Paper (see Canvas)



Percentage of Grade

Writing Assignments


Programming Assignments


Capstone Project





Grading Scale Information

Letter Grade



85 – 100 points


75 – 84 points


65 – 74 points


55 – 64 points


54 and below

©2020 Tony T. Luo





Institutional Policies & Student Information



Student Honor Code and Academic Integrity

Please take a few minutes to stress the importance of academic integrity in class. Discuss why it should matter to the student, why it matters to you and your discipline, why it matters to Missouri S&T, and why it matters to future employers. Include a statement on your syllabus about the Honor Code developed and endorsed by the Missouri S&T Student Council: the Honor Code can be found at this link: http://stuco.mst.edu/honor-code/. Encourage students to read and reflect upon the Honor code and its emphasis on HONESTY and RESPECT.


Page 30 of the Student Academic Regulations handbook describes the student standard of conduct relative to the University of Missouri System's Collected Rules and Regulations section 200.010, and offers descriptions of academic dishonesty including cheating, plagiarism or sabotage (http://registrar.mst.edu/academicregs/index.html ). Additional guidance for faculty, including the University’s Academic Dishonesty Procedures, is available on-line at http://academicsupport.mst.edu. Other informational resources for students regarding ethics and integrity can be found online at http://academicsupport.mst.edu/academicintegrity/studentresources-ai.



This course may contain copyright protected materials such as audio or video clips, images, text materials, etc. These items are being used with regard to the Fair Use doctrine in order to enhance the learning environment. Please do not copy, duplicate, download or distribute these items. The use of these materials is strictly reserved for this course and your use only. All copyright materials are credited to the copyright holder.

Accessibility and Accommodations

It is the university’s goal that learning experiences be as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please contact Student Disability Support at (573) 341- 6655, sdsmst@mst.edu, visit http://dss.mst.edu for information, or go to http://mineraccess.mst.edu  to initiate the accommodation process.

 *Please be aware that any accessible tables and chairs in this room should remain available for students who find that standard classroom seating is not usable.


Student Counseling Center

Any of us may experience strained relationships, increased anxiety, feeling down, alcohol/drug misuse, decreased motivation, challenges with housing and food insecurity, etc. When your mental well-being is negatively impacted, you may struggle academically and personally. If you feel overwhelmed or need support, please make use of S&T’s confidential mental health services at no charge.

Learn more at https://counseling.mst.edu/resources/.

S&T Connect

(S&T Connect icon on left toolbar) Office of Academic Support • 105 Norwood Hall • 320 West 12th Street • Rolla, MO 65409-1520 Phone: 573-341-7276 • Email: ugs@mst.edu • Web: http://academicsupport.mst.edu/  An equal opportunity institution S&T Connect provides an enhanced system that allows students to request appointments with their instructors and advisors via the S&T Connect calendar, which syncs with the faculty or staff member’s Outlook Exchange calendar. S&T Connect will also facilitate better communication overall to help build student academic success and increase student retention. S&T Connect Early Alert has replaced the Academic Alert system used by Missouri S&T. If training is needed, please contact Rachel Morris at rachelm@mst.edu or 341-7600.


LEAD Learning Assistance

The Learning Enhancement Across Disciplines Program (LEAD) sponsors free learning assistance in a wide range of courses for students who wish to increase their understanding, improve their skills, and validate their mastery of concepts and content in order to achieve their full potential. LEAD assistance starts no later than the third week of classes. Check the schedule at http://lead.mst.edu/assist , using zoom buttons to enlarge the view. Look to see what courses you are taking have collaborative LEAD learning centers (bottom half of schedule) and/or Individualized LEAD tutoring (top half of the schedule). For more information, contact the LEAD office at 341-7276 or email lead@mst.edu.


The Burns & McDonnell Student Success Center

The Student Success Center is a centralized location designed for students to visit and feel comfortable about utilizing the campus resources available. The Student Success Center was developed as a campus wide initiative to foster a sense of responsibility and self-directedness to all S&T students by providing peer mentors, caring staff, and approachable faculty and administrators who are student centered and supportive of student success. Visit the B&MSSC at 198 Toomey Hall; 573-341-7596; success@mst.edu; facebook: www.facebook.com/SandTssc ; web: http://studentsuccess.mst.edu.

Title IX

Missouri University of Science and Technology is committed to the safety and well-being of all members of its community. US Federal Law Title IX states that no member of the university community shall, on the basis of sex, be excluded from participation in, or be denied benefits of, or be subjected to discrimination under any education program or activity. Furthermore, in accordance with Title IX guidelines from the US Office of Civil Rights, Missouri S&T requires that all faculty and staff members report, to the Missouri S&T Title IX Coordinator, any notice of sexual harassment, abuse, and/or violence (including personal relational abuse, relational/domestic violence, and stalking) disclosed through communication including but not limited to direct conversation, email, social media, classroom papers and homework exercises.


Missouri S&T’s Title IX Coordinator is interim chief diversity officer Neil Outar. Contact him (naoutar@mst.edu; (573) 341-6038; Temporary Facility A-1200 N. Pine Street) to report Title IX violations. To learn more about Title IX resources and reporting options (confidential and non-confidential) available to Missouri S&T students, staff, and faculty, please visit http://titleix.mst.edu .


Classroom Egress Maps

Please note where the classroom emergency exits are located. Please familiarize yourselves with the classroom egress maps posted online at  http://designconstruction.mst.edu/floorplan.