COMP
300/400
Data Mining
Syllabus
Spring, 2019
Course
Information
Comp 300-001 (6000) face-to-face section
Comp 300-002 (6002) online section
Comp 400-001 (5992) face-to-face section
Comp 400-002 (5993) online section
Wednesday, 4:15 - 6:45 (for the f2f sections)
Corboy Law Center, Room L08
Dr. Channah F. Naiman
cnaiman@luc.edu
TA: not happening
Catalog
Description
Data
mining is a major areas of exploration for
knowledge discovery in databases. This topic has gained great
relevance especially in the 1990’s and early 2000’s with web data
growing at an exponential rate. As more data are collected by
businesses
and scientific institutions alike,knowledge exploration techniques are
needed to gain useful business intelligence. This course will cover a
wide spectrum of industry standard techniques using widely available
database and tools packages for knowledge discovery.
Data
mining is for relatively unstructured data for which more sophisticated
techniques are needed. The course aims to cover powerful data mining
techniques including clustering, association rules, and classification.
We also introduce high volume data processing mechanisms by
building
warehouse schemas such as snowflake and star. OLAP query
retrieval
techniques are also introduced.
We learn the basics of Data
Warehousing structure and query formulation, as it impacts a data
miner. We do not query against an actual
data warehouse.
There are other courses for that, if you are interested in
Data
Warehousing.
Outcome
Students
will be able to define, apply and critically analyze data mining
approaches for fields such as security, health care, science, marketing
and text analysis.
Prerequisites
COMP
251: Introduction to Database Systems or COMP
271: Data Structures
Please note: although statistics and database design are not
absolutely required as prerequisites, they are important areas of
knowledge for
many data mining concepts. Therefore, we have a
"crash
course" in basic statistics, and several references to
relevant database design topics.
Textbooks and references
We are exploring different types of concepts software tools in this
course. Therefore, different texts and resources will be required
for the different modules. However, several of them are free or
very inexpensive.
Required:
For general
reference (language and platform independent); for homework problems,
lectures and examples:
This
book 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.
Title: Data Mining:
Concepts and Techniques,
Third Edition
Authors: Jiawei Han, Micheline Kamber,
Jian Pei
Publisher: Morgan Kaufmann; 3rd
edition (July 6, 2011)
ISBN-10: 0123814790 or try this
Required:
For the
RapidMiner and R lab part of the course:
Title:
Data Mining for the Masses, 3ed, with Implementations
in RapidMiner and R
Author:
Matthew North
ISBN-13:
978-1727102475
Support
site for the third edition
Reference:
or
implementations in R (assignments, labs, cases, etc.), for
reference and some good examples:
Title: Data Mining For Business and Analytics
Authors: Shmueli, Bruce, Yahav, Patel, Lichtendahl
Publisher: Morgan Kaufmann; 3rd
edition (July 6, 2011)
ISBN-10: 1118879368
ISBN-13: 978- 1118879368
For ggplot
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
DataViz class.)
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:
- Know basic
techniques for both supervised and unsupervised knowledge discovery.
- Know and use
software package techniques for mining.
- Apply R to data
preparation and basic DM algorithms
- Have a good grasp
of data mining techniques: classification, association rules,
clustering, etc.
- Understand the
impact of data warehousing schemas on the selection
of Data Mining algorithms
What this course is NOT:
- This is course is not a programming course in the data
mining algorithms.
- Neither is it a strictly applications course, where you
don't have to know the algorithms.
- What
this course is: This
course is a (hopefully)
happy medium, where you learn what the algorithms are and how they
work,
so that you can then use them appropriately and interpret the results.
The course keeps evolving towards a more application
orientation.
Software
We
will be using the data mining applications
package RapidMiner in this couuse. You may download the
current version
here.
Please check the Orientation Module on Sakai for more information and
instructions on installation. The Community Edition, free,
but it has a limitation of 10,000 rows. If
you sign up with your luc email address, you should automatically have
an educational license. This is important, as we have a major
lab
that requires more than the 10,000 rows, and you may require many rows
for your project. You can check this inside of RM by clicking
on
Settings-->Manage Licenses. If it does not show up
correctly,
then you can request an
educational license directly from RM. Please
install RM
as soon as possible. Although I
cannot
enforce deadlines before the course begins, I do request that you
submit a screen shot of your RM installation in the Orientation Module,
which is sent out shorlty before classes begin.
We are also using R, with RStudio as an IDE (although you are welcome
to use any other IDE of choice, such as Jupyter notebook or anything
else). The Orientation also walks you through the installation of
R and RStudio.
Weeks 1 and 2 have some introductory labs and videos to familiarize you
with both RapidMiner and R.
Academic Honesty
Students are expected to have read the statement on academic integrity
available http://www.luc.edu/academics/catalog/undergrad/reg_academicintegrity.shtml.
This policy applies to the course. The minimum penalty for academic
dishonesty is a grade of F for that assignment. Multiple instances or a
single severe instance on a major exam or assignment may result in a
grade of F for the course. All cases of academic dishonesty will be
reported to the department office and the relevant college office where
they will be placed in your school record.
Academic dishonesty
includes, but is not limited to, working together on assignments that
are not group assignments, copying or sharing assignments or exam
information with other students except in group assignments, submitting
as your own information from current or former students of this course,
copying information from anywhere on the web and submitting it as your
own work, and submitting anything as your own work which you have not
personally created for this course. If you do wish to use materials
that are not your own, please check with me ahead of time and cite you
source clearly. When in doubt, ask first!
Be
aware that I have updated the midterm exam with modified questions and
with additional questions on classification. I
have changed the values for many for the textbook problems
that are used for homework problems. For those problems that
require open-ended answers, please br very
careful
to state the answers in your own words, not in the words of the
Instructor's Manual, nor in the words of students who have previously
taken this course.
Regarding
the project: Project requirements must be approved of by me,
and
I may modify the requirements for a specific dataset/team.
Late
changes to the project requirements will usually not be allowed and may
not be made without permission. Teams must document
participation
by posting versions to Github or similar. A completed project
with no
record
of intermediate versions will not receive credit. Team members who
cannot demonstrate participation in the project will not receive
credit, or may receive reduced credit.
Lateness
Policy:
"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 2 exams, 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.
Religious
Holidays: Students with religious holiday
conflicts: Please
let me know
within the first two weeks of class if you have a religious holiday
conflict with any exam or homework due date, so that we can plan on an
accommodation.
Students
with Disabilities: If
you have a documented
disability
and wish to discuss academic accommodations, please contact the Student
Accessibility Center (773-508-3700 and SAC@luc.edu) as soon as possible.
Students with documented disabilities who provide me with
a
letter from the SAC office will be fully accommodated as per the terms
of the letter. I am not allowed to accommodate beyond the terms
of the letter, nor can accommodations be made retroactively--so please
get your accommodations letter to me as early in the semester as
possible. 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.
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:
https://www.luc.edu/itrs/sakai/sakaiadministrativeschedule/). Students 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 Statement
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.
Course
Components and Grading
- Attendance:
Attendance
refers to physical attendance for the
on-campus class and logging into Sakai for the online class.
Most of the attendance policies do NOT apply to the online
class,
although please be aware the Sakai does keep track of the frequency and
duration of visits. For the on-campus class, attendance may
be taken at any time,
hopefully every session. While there is no graded component
for attendance, any assignments and labs that are completed in class
may not be
made
up for any reason. If for any reason you do miss a class
session,
it is your responsibility to determine what you missed, locate any
handouts, determine any changes in assignments, course plans or
schedules, etc. If you have
to miss many classes, even for a
very
valid reason, you may want to consider taking this course another
semester. You
cannot get credit for work that you were unable
to
complete in class, even if your reason for missing class is valid. The
exception is for religious
holidays, which I am required to accommodate.
- Late
homeworks and labs: If you have a very good
reason for submitting something late, you may ask me, and I may allow you to
submit it late for half credit. I will only allow this once or maybe
twice during the semester. Habitual lateness will earn you
zeros.
- Labs:
During class, we will be working on
specific lab
assignments, which must be completed in class unless otherwise
indicated. These will be hands-on applications of the
concepts and algorithms covered in class. Often, labs will
simply
be checked in class to see that you have completed them. You
may be asked to submit them on Sakai. Sometimes, there may be
a
small lab assignment to exercise the techniques that we
cover. Data Mining is an iterative
process, and we will be exploring
the data together. On campus students must be present for
labs
to get credit
for them, unless I specify that you may work off the online videos.
Online students do not have to
complete
labs in a physical classroom, but must complete them by the dates shown
for submission, which correspond to the in-person class sessions.
You should complete the installation of
RapidMiner on your own as soon as possible, preferably before class
begins. RapidMiner is not installed in LUC
labs. You will have to install the software on your laptops.
If you do not have a laptop, team up with someone who does.
If that doesn't work, I can set up a VM for you, but I need
to
know before class begins.
- Homework
Assignments:
I have cut back on the homework assginments, in favor of
additional labs. For your convenience, all homework
assignments
(except for
the Orientation ones) are in
a single Excel spreadsheet.
Please note that the homework problems have been
reconstructed so
that the values are not the same as in the text; nor are they the same
as the last time I taught the course. This means that you
really
have to do the homework. For the concept questions that do
not
require calculations, please be very careful to state the answers in
your own words. Do not copy
from the textbook. Do not copy
from the
Instructor's Manual. Do not copy
from other students, either in this
class or previous classes. If you do, you will be subject to
the penalites for Academic Honesty violations. There are many different
models and algorithms covered in the course. The purpose of
the homeworks is to give you prompt application of the foundational
concepts and algorithms, as well as prompt feedback on your
understanding of the material. Practically speaking, the
homeworks will prepare you for the exam, which is not lab-based.
- Each tab in the homework spreadsheet is a separate problem.
You may add worksheets
and submit your homework in the same spreadsheet (which may be helpful
to you in preparing for the exam), or you may submit a separate
document. Or, you may keep your spreadseet in Google Drive,
Box.com or Github and submit a link on Sakai. (That's probably a
good idea!)
Handwritten work should be limited to those graphs or charts
that
may be difficult or cumbersome to create with software. Homework may be
worked on with a team of your classmates, and may be submitted as one
submission per team. You
must
always submit something
on Sakai: either the homework itself, or a comment that it
was
submitted in person (when and to whom), or a comment that a team member
submitted the homework on your behalf. If you are submitting
on behalf of your team, please indicate in your comment the names of
the students in the team. If
you do not, you may not get credit for the homework, even
if your team member submitted it on your behalf. It is not
sufficient for your team member to submit your name. You must
also submit a comment and submit it as your assignment. That is your
responsibility!!
- Exams:
There is one exam. It is scheduled in Week 9, which is late
enough to include at least some of the Classification algorithms, but
early enough to allow you to withdraw from the course if you are not
pleased with your performance. In the
case of
a medical
emergency, I will determine if a makeup exam will be administered, or
if another accommodation will be made. Medical
emergencies must be documented and will be verified.
Please
note that I am very strict about not allowing makeup exams except in
the most extreme emergencies. You are aware of the Academic Calendar
before the semester begins. Do
not schedule travel plans or
vacations on the exams date. The online
class will be informed
as to
the time window and the exact format of the online exam. If at all
possible, I will try to schedule in-person proctoring dates and times
for online students.
- Project:
You are required to design and implement
a project that analyzes a large dataset, using
some of the techniques and algorithms that are covered in the course.
The project presentations will be on 5/01, which is our schedule
Final Exam day. If more
time is
needed, presentations will be scheduled on 4/24. I
will let
you
know well in advance if you have to be ready by
4/24. Projects
may be completed in teams; however, every team member is expected to
fully participate in the project in order to earn the team
grade.
There
are more details about the project in the project assignments.
- Project Excellence:
A "very good" project that fulfills the requirements and is
perfectly satisfactory can earn full points for all non-Excellence points for the Project.
If you get full points on your
project and do not earn any
Project Excellence points, please don't ask me what you did "wrong".
The answer will most likley be "nothing".
Project Excellence points can be earned for a project that
is truly excellent, incorporating components well beyond the material
covered in class, or simply having a real "Wow" factor. A
grade of "A" means "excellent". There are many points in this
course that could be considered free points. Full points for
Homework and Labs can be earned if significant effort is demonstrated:
you are graded for completeness, not necessasrily for
correctlness (although grossly incorrect answers will be makred off).
But for a project to get an A, it must be excellent. As I
review your progress during project walkthroughs, I will tell you what
you can do to earn excellence points for your project.
- Extra
Credit: There is no extra credit in this
course, as
this is not practical to the course, nor is it fair to other students.
-->Important note
about team submissions: Repeating what was
written above under Homework: If I announce that an
assignment may be worked on in a team (for instance, pair programming),
each
team member must submit something on Sakai. If you are
the
team member submitting the assignment, you must also submit a note on
Sakai, listing each team member for
whom you are submitting the assginment. If someone else is
submitting the assginment, you must submit a note in the Assignment
comment box telling me who is submitting the assignment for your team.
Do not assume that just because your team member submitted
the
assignment that you will automatically get credit. You will
not. You MUST
submit a comment letting me know that it was submitted on your behalf.
93 -
100 |
A |
90 - 92 |
A- |
87 -89 |
B+ |
83 - 86 |
B |
80-82 |
B- |
77 - 79 |
C+ |
73-76 |
C |
70-72 |
C- |
67-69 |
D+ |
60 - 66 |
D |
59 and
lower |
F |
The table below lists the
points value
for each graded component of the course.
Meeting |
Week |
Assignment
Type |
Assignment
Name |
Points |
Due
Date |
16-Jan |
Orientation |
Orientation |
Video
Tour |
5 |
23-Jan |
|
|
Orientation |
Syllabus |
5 |
23-Jan |
|
|
Orientation |
Greetings
Forum |
5 |
23-Jan |
|
|
Orientation |
Using
Zoom |
0 |
23-Jan |
|
|
Orientation |
Install
RM |
5 |
23-Jan |
|
|
Orientation |
Install
R-Studio |
5 |
23-Jan |
|
|
Orientation |
Join
Piazza |
0 |
23-Jan |
16-Jan |
Week
1 |
Lab
(RM) |
Install
RM Repositories |
10 |
23-Jan |
23-Jan |
Week
2 |
Lab
(RM) |
RM
Getting Started |
15 |
30-Jan |
|
|
Lab
(R ) |
Intro
R |
10 |
30-Jan |
|
|
Homework |
Chapter
2 |
15 |
30-Jan |
30-Jan |
Week
3 |
Homework |
Chapter
3 |
15 |
6-Feb |
6-Feb |
Week
4 |
Lab
(RM and R) |
DMM-Ch3: Data
Prep DMM-Ch4: Correlation |
15 |
9-Feb |
|
|
Lab
(RM)
(links for R info)
|
Visualization,
Discretization 3 ways
|
15 |
13-Feb |
|
|
Homework |
Chapter
4 |
15 |
13-Feb |
13-Feb |
Week
5 |
Lab
(RM and R) |
DMM-Ch 5 (RM):
Assoc -FP
|
10 |
16-Feb |
|
|
DMM-Ch 5 (R): Assoc Rules
|
10
|
16-Feb
|
|
|
Lab
(RM) |
202_Single-Rule | 10
|
16-Feb |
|
|
Homework |
Chapter
6 |
20 |
27-Feb |
20-Feb
|
Week 6
|
Project
|
Exploring Datasets, prelim.
|
20
|
20-Feb
|
27-Feb |
Week 7
|
Lab: Text
Mining |
FP/Clustering |
20 |
13-Mar |
|
|
Lab: Text
Mining |
Zipf/Mandelbrot |
35 |
13-Mar |
|
|
Lab: Text
Mining |
Web
crawling/Word Clouds |
35 |
13-Mar |
|
|
Project Lab
|
Explore Datasets, continued
|
15
|
2-Mar
|
13-Mar |
Week
8 |
Lab
(RM) |
Classification
Models:
KNN, Bayes, CrossValidation,
ROC/LIFT
|
30 |
16-Mar |
|
|
Homework |
Chapter
8 |
20 |
27-Mar |
|
|
Project |
Project
Proposal |
15 |
27-Mar |
20-Mar
|
Week 9
|
Midterm
|
|
250
|
20-Mar
|
27-Mar |
Week 10
|
Lab
(RM) |
KNN,
NN, CTS using NN |
25 |
31-Mar |
|
|
Homework |
Chapter
9 |
15 |
3-Apr |
3-Apr |
Week
11 |
Lab
(RM) |
Affinity
Marketing |
50 |
6-Apr |
10-Apr |
Week
12 |
Project |
Progress
Report |
5 |
10-Apr |
|
|
Project
|
Project Freeze
|
0
|
11-Apr
|
17-Apr |
Week
13 |
Homework |
Chapter
10 |
10 |
1-May |
|
|
Lab
(RM and R) |
DMM:
K-Means, Clustering |
10 |
21-Apr |
1-May |
Week 15
|
Project |
Models (+inerpretation)
|
125 |
1-May |
|
|
Project |
Presentation |
50
|
1-May |
|
|
Project |
Report |
25 |
1-May |
|
|
Project |
Excellence |
50 |
1-May |
Participation, Prompt Submissions, meeting attendance, etc.
|
10
|
|
TOTAL POINTS |
1000 |
|
Course
Schedule
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.
Date
|
Topic
|
Text/Files/Links
|
Due
|
Before class begins |
Orientation Module |
see Sakai Orientation
module!!
Install
R and R-Studio
|
preferably before class
starts |
1/16
|
Intro to
Course
Intro to Data Mining
Crash Course in Stats, Part 1 (central tendency, dispersion)
Getting to know your data
|
- syllabus
- Han:
Chapter 1
- Fasten
Your
Seatbelts (FYSB), Part 1
- Slides
and
Videos on Sakai
|
|
Lab
(RM): Install Repositories
|
all
links and videos on Sakai |
1/23
|
Data
Visualization
Similarity Measures
Crash Course in Stats, Part 2 (Probability
Distributions)
Lab (RM):
Getting started (off the RM website)
Lab
(R):
Intro to R (time permitting)
|
- Han:
Chapter 2
- FYSB
slides
and videos, Part 2 (on Sakai)
- links
to
RM intro videos, on Sakai
- INtro-R
Project
files, datasets, video, on Sakai
|
-
Lab (RM): Install Repositories
- all
Orientation assignments
|
1/30 |
Data
Visualization; Data Reduction;Attribute Reduction;
Discretization; Missing Values
Crash Course in Stats, Part 3 (Hypothesis Testing) |
Han:
Chapter 3 |
- HW,
Chapter 2
-
(all
HW assignments in the same file)
-
Lab (RM): Getting Started
-
Lab(R): Intro R
|
2/06
|
Data
Warehousing
Lab
(RM and R):
Visualization, Discretization, Corrleation
Lab:
Discretization 3 ways
|
Chapter 4
Data
Mining for the Masses
(DMM--RM and R, Ch. 3 and Ch. 4)
DMM
files
Additional RM lab files, links on Sakai
|
-
HW, Chapter 3
- DMM Labs
Ch 3-4, DUE: 2/09
|
2/13
|
Frequent
Patterns
Demo Problem 6.6
Demo p. 257 FP Growth
Lab:
FP and Association Rules (including DMM with RM and R, and
also
an additional lab named "202_SingleRule", which in not in DMM)
|
Chapter 6
DMM--RM and R,
Ch. 5 (Association Rules, FP_Growth)
Additional RM labs ("202_SingleRule")
|
- HW, Ch 4
- Lab:
Visualization/Discretization
- DMM
Ch 5-6, and 202_SingleRule Labs DUE: 2/16
|
2/20
|
catch-up Frequent Patterns and
labs
Begin discussion of Dataset Exploration for Project
Lab: Exploring Datasets, preliminary exploration
|
|
- Project: Explore Datasets
(preliminary DUE at end of class period)
|
2/27
|
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!!
- Text
Mining
using FP and Clustering
- Text
Mining
using Zipf-Mandelbrot
Distribution
- Web
crawling
and Word Clouds
Lab:
Exploring Datasets, continued
|
All files
and links posted on Sakai |
- Lab: Explore
Datasets (refined), DUE 3/2
|
3/13
|
Project
Discussion, time
permitting
Classification
Lab:
Rules,
Decision Trees, KNN, Bayes, CrossValidation,
ROC Charts, Lift Chart
|
Chapter 8
|
-
Labs: Text Mining
- Labs:
Classification, due 3/16
|
3/20
|
Midterm Exam
(withdraw deadline is 3/25!!)
|
Midterm
Review |
|
3/27 |
Classification,
continued (KNN, Neural Networks)
Lab: Neural Networks, possible Medical lab
Project Review for proposal (time
permitting) |
Chapter 9
(KNN and Neural Networks) |
|
4/03
|
Lab:
Affinity Marketing using RapidMiner
(very complex lab, do NOT wait until the last minute!!)
Project
Solidification: Team Meetings (online students, zoom)
|
|
|
4/10 |
Project
Team Meetings--finalize project requirements
Project Freeze! (NO changing of project teams or requriements
after this date!!)
|
|
|
4/17
|
Clustering
Lab: K-Means Clustering
|
Chapter 10
DMM, Chapter 6
|
- Lab: DMM KMeans Clustering DUE 4/21
|
4/24
|
Work on Projects,
Questions, Zoom Team Meetings
|
|
Lab:
K-Means, Clustering DUE 4/21!! |
5/01
|
Project
Presentations
|
|
|
Regular
SPRING Semesters |
|
|
2019 |
Spring
Semester Open
registration ends at midnight |
Sun |
Jan.
13 |
Martin
Luther King, Jr.,
Holiday, No classes |
Mon |
Jan. 21
|
Spring
Semester Begins. Late and
Change of Registration
begins - Late registration fee applies |
Mon
|
Jan.
14
|
Late
and change registration
ends. Last day to withdraw without a mark of "W" |
Tues
|
Jan.
22 |
Last
day to drop class(es) with a Bursar credit of 100%- dates maintained by
Bursar |
Sun
|
Jan.
27
|
Last
day to convert from
credit to audit or vice versa - Last day to request or cancel pass/no
pass option |
Mon
|
Jan.
28 |
Last
day to drop class(es) with a Bursar credit of 50%- dates maintained by
Bursar |
Sun
|
Feb.
10
|
Summer
Registration
Begins |
Mon
|
Feb.
11 |
Ash
Wed (46 days before
Easter): Classes meet,
Special worship services available |
Wed
|
Mar. 6
|
Last
day to drop class(es) with a Bursar credit of 20% (zero credit
thereafter) |
Sun
|
Feb.
17
|
Last
day for students to
submit assignments to change an "I" grade, from the preceding Fall
Semester and the preceding "J" Term, to a letter grade;
Faculty may set an earlier deadline |
Mon
|
Feb.
25 |
Last
day to file applications
with Deans' offices for degrees awarded in December for this year |
Fri
|
Mar.
1 |
Spring
Break: No classes |
Mon-Sat
|
Mar. 4 - 9
|
Classes
Resume |
Mon
|
Mar.
11 |
Last
day (5:00 p.m.) to
withdraw with a grade of "W", after this date, the penalty grade of
"WF" is assigned |
Mon
|
Mar.
25 |
Good
Friday , No classes (offices
closed) |
Fri
|
Apr. 19
|
Easter
Holiday: No classes Thurs
evening (classes that start
4:15 p.m. or later are canceled) through Mon afternoon (classes
beginning on or after 4:15 p.m. will be held) |
Thurs - Mon
|
Apr. 18 - 22
|
Fall
Semester UGRD
Registration begins |
Mon
|
Apr. 8
|
Spring
Semester classes end |
Fri
|
Apr. 26
|
Final
Examinations |
Mon - Sat
|
April 29 - May 4
|
*Study
Day Wednesdays: No
daytime exams will be held. |
|
|
Evening
classes meeting at
4:15pm or later will hold exams as scheduled. |
|
|