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
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.
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.).
end of the course, you should be able to:
1)Explain and illustrate fundamental
conceptsand building blocks in IoT and Data Science, and review and critique technical
articles in terms of novelty, viability, and overall quality.
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
3)Communicate your proposal and accomplishments in a professional context effectively.
Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data
Mining, Inference, and Prediction (2ndEdition), 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
○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
●Lecture slides / notes: will be uploaded
to course website or Canvas.
●Other resources: software kits, tools, equipment, etc.
○No phones (unless you are expecting an emergency call);
○Avoid laptop use as much as possible (unless required by a particular
●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.
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·Introduction to the Course
·Introduction to the Internet of Things and Data Science
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