Time/Place: Asynchronous Online
Instructor: Channah F. Naiman
When emailing me, please be sure to put COMP 306/406 in the subject line!!
MS Teams Code: 1ke9vnc
(If you have not yet used MS Teams, download the app here, which you can install on your phone or laptop. Once in Teams click on Teams-->Join a Team, and then enter our code
STAT 103: Fundamentals of
Statistics or STAT 203: Statistics or ISSCM 241: Business Statistics
or PSYC 304: Statistics or instructor permission
Statistics is listed as a prerequisite; however, we have a "crash course" in basic statistics for those coming into the course with little statistics, or for those who need a review. In addition, although database design is not listed as a prerequisite, there will be several references to relevant database design topics.
general reference (language and platform independent); for homework
problems, lectures and examples:
is useful for reference and conceptual examples (language independent,
with nice illustrations) of some of the underlying concepts/algorithms
(such as apriori, basic classification and clustering algorithms and
more). The book doesn't have a more recent edition, but it is
something of a classic as a data mining text. Since you can find
this quite inexpensively on the internet, I am including it here for
reference. (Also, because it is not a new book, I have found the entire
pdf for free at a legitimate university site, see below.) The book
is now approaching 10 years old, which is the limit that we can really
use it, even for a classic. That's really a shame, since the newer
book that shares a lot of the examples, figures and topics is by Tan,
and I have found the explanations and overall structure to be less
helpful. So this semester, you kind of lucked out.
Mining: Concepts and Techniques, Third Edition
Authors: Jiawei Han, Micheline Kamber, Jian Pei
Publisher: Morgan Kaufmann; 3rd edition (2012)
ISBN-10: 0123814790 or try this
Required: For the RapidMiner and R lab part of the course:\
Data Mining for the Masses
****There is an excellent
fourth edition out that is an online and updated version of the
third edition. It has updated powerpoint slides, short
explanatory videos, review questions and other support
materials. Some students have really liked this version of the
lab text, so I have created a course
link where you can purchase this online text for $69.99.
site for the third edition
in R (assignments, labs, cases, etc.), for reference and some
Title: Data Mining For Business and Analytics
Concepts, Techniques and Applications in R.
Authors: Shmueli, Bruce, Yahav, Patel, Lichtendahl
Publisher: Morgan Kaufmann; 3rd edition (July 6, 2011)
ISBN-13: 978- 1118879368
examples: (You don't have to buy the book. It is based off
of his website. Illustrated examples, if you are interested in Data
Visualization for your project presentation.) (Or just take the
Alboukadel Kassambara. Guide to Create Beautiful Graphics in R, STHDA, 2013. isbn: 9781532916960. Most examples, with small modifications, are available on his wonderful website and his R support website.
Course Objectives and Goals
After taking this course, students should be able to:
"There's no such thing as an emergency. There is only poor planning." While this clearly does not apply to actual (and verifiable) medical and family emergencies, if you wait until the last day before something is due, and then your Internet connection goes down, this does not qualify as an emergency. Give yourself plenty of time to submit your assignments on time. If I see that most of the class needs extra time for a specific assignment (and has been working on it!!) I may be willing to extend the deadline. But in general, your poor planning or poor time management does not constitute a reason for me to extend the deadline for you. I am especially careful not to do so as this would be unfair to the other students who turn in their work on time. We have limited number of sessions, during which time we have an exam, a project, labs (some quite intense), and homework assignments. Do not fall behind in your work. Do not wait until the last minute. I will not be sympathetic. You may have heard that I am, in fact, sympathetic. That is no longer the case. I have evolved. Late assignments are worth only half credit. This is true even if you have a valid reason for submitting the homework late. Usually, late assginments must be submitted within one week of the due date for half credit. For some assignments, you can't submit it late at all. And for some, I do not allow an entire week for late submission, but only a few days. Please check Sakai for exact due dates and the last time for a late submission for a specific assignment. Further, they can only be submitted late if I have not posted the answers to the homework. After one week (or the late submission deadline), you will receive zero points for any unsubmitted assignments. No exceptions.
dates. Assignments are due as
specified in the syllabus Course Schedule and on Sakai. I
scheduled due dates in order to give you appropriate time to work on and
complete assignments. Do not assume that they are all due on the
same day of the week. They are not.
All assignments and due dates are posted on the Course Schedule on the
Syllabus, and also on the Course Calendar on Sakai.
Late Credit. Do not assume that there
is an automatic "half credit" for late assignments. There is
Extenstions and "submit until". Any extensions in due dates will be sent as email announcements on Sakai. In the rare event that I allow an individual student to submit an assignment late, it will be graded as half credit. Some assignments have a “submit until” date listed on the Sakai assignment. That is not the due date. The “submit until” date is only valid when I give permission to a student or to the class to extend the due date.
at the last minute. The purpose of the due dates is so that
you won't fall behind. I take due dates seriously. So should
you. It is your best interest NOT to wait until the last minute to
begin working on your homework and labs. I cannot guarantee that I
will be able to help you on the due date. I have many other
students, and generally, when a student waits until the last minute, he
or she is less prepared and needs even more time. This would not
allow me to maximize my availability to all students.
not such thing as an emergency. This is an online
class. Assume that there will be technical issues, or that your
internet connection may occasionally go down. Barring a
catastrophic internet disaster or a true (and verifiable!) last-minute
medical emergency, there is no such thing as an emergency. There
is only poor planning.
Students with Disabilities: Loyola University Chicago provides reasonable accommodations for students with disabilities. Any student requesting accommodations related to a disability or other condition is required to register with the Student Accessibility Center (SAC). Professors will receive an accommodation notification from SAC, preferably within the first two weeks of class. Students are encouraged to meet with their professor individually in order to discuss their accommodations. All information will remain confidential. Please note that in this class, software may be used to audio record class lectures in order to provide equal access to students with disabilities. Students approved for this accommodation use recordings for their personal study only and recordings may not be shared with other people or used in any way against the faculty member, other lecturers, or students whose classroom comments are recorded as part of the class activity. Recordings are deleted at the end of the semester. For more information about registering with SAC or questions about accommodations, please contact SAC at 773-508-3700 or SAC@luc.edu.
Students who are allowed to take their exams in the SAC office are encouraged to do so. Should you choose to take the exam in the classroom, I cannot guarantee that the classroom environment will be quiet enough to provide you with the environment that your disability may require. If you choose to take the exam in the classroom, you are taking that risk.
notes for this course:
Online Recording Policy
In this class software
may be used to record live class discussions. As a student in this
class, your participation in live class discussions will be recorded.
These recordings will be made available only to students enrolled in the
class, to assist those who cannot attend the live session or to serve as
a resource for those who would like to review content that was
presented. All recordings will become unavailable to students in the
class when the Sakai course is unpublished (i.e. shortly after the
course ends, per the Sakai administrative schedule:
who prefer to participate via audio only will be allowed to disable
their video camera so only audio will be captured. Please discuss this
option with your professor. The
use of all video recordings will be in keeping with the University
Privacy Statement shown below: Privacy
Assuring privacy among faculty and students engaged in online and face-to-face instructional activities helps promote open and robust conversations and mitigates concerns that comments made within the context of the class will be shared beyond the classroom. As such, recordings of instructional activities occurring in online or face-to-face classes may be used solely for internal class purposes by the faculty member and students registered for the course, and only during the period in which the course is offered. Students will be informed of such recordings by a statement in the syllabus for the course in which they will be recorded. Instructors who wish to make subsequent use of recordings that include student activity may do so only with informed written consent of the students involved or if all student activity is removed from the recording. Recordings including student activity that have been initiated by the instructor may be retained by the instructor only for individual use.
|93 - 100||A|
|90 - 92||A-|
|83 - 86||B|
|77 - 79||C+|
|60 - 66||D|
|59 and lower||F|
|Week Beginning||Week||Assignment Type||Assignment Name||Points||Due Date|
|before semester||Orientation||Orientation||Video Tour and Syllabus
|18-Jan||Week 1||Lab (RM)||Install RM Repositories||10||26-Jan|
|24-Jan||Week 2||Lab (RM)||RM Getting Started||15||31-Jan|
|Lab (R )||Intro R||10||31-Jan|
|31-Jan||Week 3||Homework||Chapter 3||15||7-Feb|
|7-Feb||Week 4||Lab (RM and R)||DMM-Ch3: Data
(links for R info)
Discretization 3 ways
|14-Feb||Week 5||Lab (RM and R)||DMM-Ch 5 (RM):
(R): Assoc Rules
||Lab: Text Mining||FP/Clustering||20||16-Mar|
|Lab: Text Mining||Zipf/Mandelbrot||35||16-Mar|
|Lab: Text Mining||Web crawling/Word Clouds||35||16-Mar|
|14-Mar||Week 8||Lab (RM)||Classification
Decision Trees Bayes, CrossValidation,
depends on Midterm
depends on Midterm
|21-Mar||Week 9||Midterm Exam||250||23-Mar|
||Project Zoom Meetings
||Project Proposal Zoom Meetings
|Lab (RM)||KNN, NN, CTS using NN||25||4-Apr|
|4-Apr||Week 11||Lab (RM)||Affinity Marketing||50||13-Apr|
|11-Apr||Week 12||Project||Progress Report||5||18-Apr|
|18-Apr||Week 13||Homework||Chapter 10||10||25-Apr|
|Lab (RM and R)||DMM: K-Means, Clustering||10||25-Apr|
Submissions, meeting attendance, etc.
This schedule is a guide. Exact dates and topics may be subject to change. It is my best estimate, but we may have to adjust the schedule slightly. You are responsible for all announcement/changes made in class or posted on Sakai.
||Before Class Begins||Orientation Module||see Sakai Orientation module!!
1: Intro to Course
Lab (RM): Install Repositories
Chapter 2: Data Visualization and Similarity Measures
Crash Course in Stats, Part 2 (Probability Distributions)
||Getting started RM website
Three Text Mining Labs: (see Sakai for instructions, videos, and process downloads):
These are much more serious labs than in earlier weeks. You will love them!! Do NOT wait until the last minute to work on them.
Optional Zoom meetings re: team datasets!!
|9||3/21||Midterm Exam (scheduled for 3/23)
(withdraw deadline is still TBA on the Academic Calendar)
on Projects, Questions, Project Team zoom meetings
Project Presentations, video or zoom
Academic Calendar: Graduate