INFO 3200: DATA MINING AND VISUALIZATION
Term and Credits:
Spring 2021
4 Credits

CRN:
Section 1: CRN: 2376
Section 2: CRN: 2320
Section 3: CRN: 2665
Location:
Section 1:
Tues 10:00 am – 11:50 am, DCB 125
Section 2:
Tues 12:00 pm – 1:50 pm, DCB 305
Section 3:
Tues 4:00 pm – 5:50 pm, by Zoom
Note: Thursdays of all sections are online Async.
Instructor:

Name: Dr. Young-Jin (YJ) Lee
Associate Professor
Department of Business Information and
Analytics

Zoom Office Location:
https://udenver.zoom.us/j/3038714813

Office Hours: Th 10am – 12:00pm or by
appointment

Email: YoungJin.Lee@du.edu

Phone: 303-871-4813

Communication Conduct:

Feel free to refer to me on a first name basis.
“YJ” works fine.
If you prefer to refer to me as Professor Lee or
Dr. Lee, either of those are OK too.

I am open to the use of two communication
channels.

• Discussion board on the Canvas course site is
preferred for class-related Q&A. I will respond
to any communication as soon as possible.
Almost always within 24 hours or less. I will
send class level communications via Canvas
email and announcements. Make sure your
Announcement Notifications in Canvas are
turned on! Check your email daily for any
announcements.
• Email: only for personal matters.

Note: Friday is my designated research day. I
might not be as fast at responding to emails as
quickly Friday through Sunday, due to my
research days.

If you are having difficulty with the course
material, please see/talk to me at your earliest
convenience. Do not wait until the first exam to
see me about the difficulties you are
experiencing in comprehending the course
material.
Do not allow yourself to fall behind in covering
the assigned material as this will most certainly
result in a poor course grade. Keep up with your
assignments and the readings in the text!
Syllabus INFO3200, YJ Lee, SP 2021 2

Course Details
COURSE DESCRIPTION
This course explores the concepts of storytelling with data, prediction modeling, and presenting
statistical results. It covers the concepts of visualization terminology along with all the steps of
the modeling process: define goal, get data, explore & visualize data, pre-process data, partition
the data series, apply modeling technique(s), evaluation and compare performance, implement
the model, and communicate the results. The modeling techniques covered include Time Series
Forecasting, Clustering, Principal Components Analysis, Decision Trees, Naïve Bayes, K-
Nearest Neighbor, Multiple and Logistic Regression, and Machine Learning Approaches. This
course also covers the interpretation of real-time business data in terms of dashboards and
scorecards.

PREREQUISITES/CO-REQUISITES
Prerequisite: INFO 2020

LEARNING OUTCOMES
After completing this course, students will be able to:
• Define the terminology/vocabulary and methods associated with Data Mining and
Visualization in order to choose and perform appropriate analyses and also intelligently
talk to other analysts
• Recognize the benefit of and use a Data Modeling framework for solving problems with
data including:
o Prepare data for use in models with data manipulation and visualization
techniques
o Discuss and implement modeling and assessment of competing prediction
methods/models.
o Document the process of performing the analysis using the concepts of
reproducible research.
o Communicate data and modeling results effectively, including the use of data
visualizations (graphs and tables)

Course Material
REQUIRED MATERIAL
Required online Textbook: Fundamentals of Predictive Analytics with JMP, Second Edition,
2016, Klimberg and McCullough (You can access an online version of this book through the DU
Library at
http://du.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=e025x
na&AN=1442380&site=ehost-live&scope=site&ebv=EB&ppid=pp_34)
Software:
• Microsoft Office
• JMP Pro 15 (Free at https://pioneerweb.du.edu/resources)
• Tableau 10.5 (Free at https://www.tableau.com/academic/students#form)
• R and R Studio (Free) [Required for Business Analytics Majors/optional otherwise]

Syllabus INFO3200, YJ Lee, SP 2021 3

Supplemental Texts: Throughout the class you will be provided with links to other books

• Now You See It, Stephen Few, 2009
• Storytelling with data, Cole N. Knaflic, 2015
• Show Me the Numbers, Stephen Few, 2004
• The Functional Art, Alberto Cairo, 2012
• Information Dashboard Design, Stephen Few, 2006
• Visualize This, Nathan Yau, 2011

Grading & Course Requirements
GRADING
Performance will be evaluated on the items below. For this class, all assignments assume you
are trainees for Stats Dairy (a fictional company). Your training score is only a measure of your
performance in this class and does not reflect my opinion of you as an individual or your worth
as a person.

Grading Scale:
Stats Dairy regularly hires more trainees than it needs. By means of this course we determine
where to place the graduates of the program:
90% - 100% A Trainees who receive an A are considered on the "fast track" and will start
out as data mining analysts. Our studies show that most trainees who fall
in this group reach an executive position within 10 years.
80% - 89% B Trainees who receive a B will start out as assistant data mining analysts.
This does not mean that they cannot reach the executive level, but it will
be more difficult since they will not regularly be put into career-enhancing
positions such as overseas consulting assignments.
70% - 79% C Trainees who receive a C will be put into staff positions for further
development.
60% - 69% D Trainees who receive a D will be offered non-management positions.
00% - 59% F Trainees who receive an F will be separated from Stats Dairy.

Final letter grade scale: A: 93-100%; A-: 90-92.9%; B+: 87-89.9%, B: 83-86.9%; B-: 80-
82.9%; etc.

ASSESSMENT:

Performance will be evaluated on the items:

ACTIVITY Weight
Pre-Class Training Assignment Quizzes 17%
In-Class Group Tasks 15%
4 Cases 18%
Exam 1 15%
Exam 2 20%
Course Project (3 Phases) 15%
TOTAL 100%

Syllabus INFO3200, YJ Lee, SP 2021 4

COURSE REQUIREMENTS DETAILS

Make-up Exams/assignments: To reschedule an exam/assignment for a legitimate conflict,
you must receive permission from me BEFORE the exam/assignment in order to reschedule. If
you are sick the day of the exam, you still need to contact me before class for permission to
reschedule. Otherwise, you will receive a zero on the exam. No make-up exams will be given.

If a student is caught cheating on an exam (i.e., sharing answers or copying someone else’s
work) he or she will automatically receive an F in the class.

Pre-Class Training Assignment Quizzes: There will be quizzes to be completed before each
in-person class that will cover the reading material and the video lectures. No late submissions
are accepted.

Case Assignments: There will be 4 individual case assignments. Late work will be accepted
for a penalty of 7 points a day.

In-class Group Tasks: During class, mini-group discussions/problems will be completed to
help supplement our course material. You may work individually or with a total of 3 people in a
group. If you have an excused absence (max of 2 per quarter), you can complete the
assignment on your own and it is due one week later at the beginning of class.

Group Course Project: Detailed instructions will be posted on Canvas.

Excused Absences: Report an excused absence on the Canvas class home page. If you
have a University approved excuse, the maximum of 2 can be increased with permission.

Communication: If you are having difficulty with the course material, please see me at your
earliest convenience. Do not wait until the first exam to see me about difficulties you are
experiencing in comprehending the course material. Do not allow yourself to fall behind in
covering the assigned material as this will most certainly result in a poor course grade. Keep up
with your assignments and the readings in the text!
Policies
UNIVERSITY EXPECTATIONS, POLICIES AND RESOURCES
Inclusive Learning Environments.
• In this class, we will work together to develop a learning community that is inclusive and
respectful. Our diversity may be reflected by differences in race, culture, age, religion,
sexual orientation, socioeconomic background, and myriad other social identities and life
experiences.
• The goal of inclusiveness, in a diverse community, encourages and appreciates expressions
of different ideas, opinions, and beliefs, so that conversations and interactions that could
potentially be divisive turn instead into opportunities for intellectual and personal enrichment.
• A dedication to inclusiveness requires respecting what others say, their right to say it, and
the thoughtful consideration of others’ communication.
• Both speaking up and listening are valuable tools for furthering thoughtful, enlightening
dialogue. Respecting one another’s individual differences is critical in transforming a
collection of diverse individuals into an inclusive, collaborative and excellent learning
community.
Syllabus INFO3200, YJ Lee, SP 2021 5

• Our core commitment shapes our core expectation for behavior inside and outside of the
classroom. Office of Diversity, Equity, and Inclusion website (https://www.du.edu/diversity-
inclusion/index.html).

Students with Disabilities. Students who have disabilities or medical conditions and who want
to request accommodations should contact the Disability Services Program (DSP);
303.871.2372/ 2278; 1999 E. Evans Ave.; 4th floor of Ruffatto Hall. Information is also available
online on the DU Disability Services website; see Handbook for Students with Disabilities.
Please note that academic accommodations cannot be applied retroactively, so it is important
for you to register with DSP as soon as possible if you think you may need accommodations at
some point while at Daniels College of Business.
DU Honor Code. All students are expected to abide by the University of Denver Honor Code.
These expectations include the application of academic integrity and honesty in your class
participation, assignments and assessments. The Honor Code can be viewed in its entirety on
the DU Student Conduct website.
All members of the University of Denver are expected to uphold the values of Integrity, Respect,
and Responsibility. These values embody the standards of conduct for students, faculty, staff
and administrators as members of the University community.
In order to foster an environment of ethical conduct in the University community, all community
members are expected to take “constructive action,” that is, any effort to discuss or report any
behavior contrary to the Honor Code with a neutral party. Failure to do so constitutes a violation
of the DU Honor Code. Specifically, plagiarism and cheating constitute academic misconduct
and can result in both a grade penalty imposed by the instructor and disciplinary action including
suspension or expulsion. As part of their responsibility to uphold the Honor Code, instructors
reserve the right to have papers checked for plagiarism against a database of papers submitted
previously at DU, a national database of papers, and the Internet.
Additional University Expectations. Please review all University Expectations on the Daniels
College of Business syllabus website.
COURSE POLICIES
Attendance Policy. This course requires you actively participate in both asynchronous and
synchronous learning, although the instructor does not check your attendance during the
quarter.
Additional Course Policies, Resources and Information. You are responsible for access to
your personal computers and maintenance of your computing resources for this class. The
instructor cannot spend time teaching general aspects of using computers.
Syllabus Policy. This syllabus is subject to change based on the needs of the class and at the
discretion of the instructor.

Syllabus INFO3200, YJ Lee, SP 2021 6

Class Schedule
Below is a tentative schedule for the quarter. Please REFER TO CANVAS for updates!
Week Dates Topics Major Assignments
1 Th Apr 1 Course prep!
Exploring & Visualizing Data (part 1 & 2):
Install software
2 T Apr 6
Th Apr 8
In-Person: Exploring & Visualizing Data (part 1 & 2)
Async: Dashboards, Scorecards & Creating Maps
Data Management and PCA

3 T Apr 13
Th Apr 15
In-Person: Class activities for prior Async materials
Async: Time Series Forecasting (Trend, Seasonal and ARIMA
Models)

4 T Apr 20
Th Apr 22
In-Person: Class activities for prior Async materials
Async: Cluster Analysis/ Overview and Practice Midterm Exam


5 T Apr 27
Th Apr 29
In Person: Class activities for prior Async materials
Async: Midterm Exam
Homework #1

6 T May 4
Th May 6
In Person: Class activities for prior Async materials
Async: Data Mining Introduction

Homework #2
7 T May 11
Th May 13
In Person: Class activities for prior Async materials
Async: Classification & Regression Trees (in JMP & in R)

8 T May 18
Th May 20
In Person: Class activities for prior Async materials
Async: K-NN, GLM & Logistic Regression Models (in JMP & in R)

9 T May 25
Th May 27
In Person: Class activities for prior Async materials
Async: Presenting Results, R Dashboard, Adv. Tableau Techniques
Homework #3
Project Proposal
10 T June 1
Th June 3
In Person: Class activities for prior Async materials
Async: Advanced Models (in JMP & in R)


11 T June 8 In-Person: Project Presentations / Final Exam Homework #4
Project Story