OMIS 665
Big Data Analytics
Northern
Spring 2019
Professor: Chuck
Downing |
|
Office: Barsema
328 O |
Office Hours: Best way to reach me is via e-mail. In person: Wednesday 1:45-5:30 p.m. (BH 328 O) and
before and after each class for 15 minutes, or by appointment. |
|
Phone: 753-6381 E-Mail: cdowning@niu.edu |
Web
Page: ChuckDowning.com (can also
connect through BlackBoard)
Course Description and
Prerequisites:
When
Dr. Downing was in high school, if he needed to do a report on Abe Lincoln (or
anything else) he had one primary source:
his family’s Compton’s Encyclopedia.
More than 30 years later our society is awash in data and
information. Google Abe Lincoln and you
are greeted with over 16 million results.
We’ve moved from too little information to far too much. As we click around the Internet, transact
with our credit cards, and carry out day-to-day activities, information is
being captured constantly. The question
for business organizations is What to do with all of this data? How can organizations use the massive amounts
of available data to increase their strategic position, make better decisions,
target customers more precisely, etc.?
Such questions and opportunities are causing an explosion in the need
for professionals who understand Data Science and Big Data Analytics.
This
course provides an in-depth study of the concepts, methods, and tools for Data
Science and Big Data Analytics. Topics
include the Data Analytics Lifecycle, Basic Data Analytics Methods using the
open-source RStudio, Advanced Analytics Theories and Methods including
Clustering, Association Rules, Linear and Logistic Regression, Classification
and Time Series Analysis, and Advanced Analytics Technology and Tools including
the open-source software MapReduce and Hadoop.
Course Objectives:
·
Deploy a
structured lifecycle approach to data science and big data analytics projects
·
Reframe a
business challenge as an analytics challenge
·
Apply analytic
techniques and tools to analyze big data, create statistical models, and
identify insights that can lead to actionable results
·
Select optimal
visualization techniques to clearly communicate analytic insights to business
sponsors and others
·
Use tools such as
R and RStudio, MapReduce/Hadoop, and in-database analytics
·
Explain how
advanced analytics can be leveraged to create competitive advantage and how the
data scientist role and skills differ from those of a traditional business
intelligence analyst
Prerequistes: OMIS 652 - Business
Applications of Database Management Systems
Suggested (not required) Prerequistes:
·
A strong quantitative
background with a solid understanding of basic statistics, as would be found in
a statistics 101 level course.
·
Experience with a
scripting language, such as Java, Perl, or Python (or R). Many of the lab
examples taught in the course use R (with an RStudio GUI), which is an open
source statistical tool and programming language.
Textbook:
None. Reading your Student Guide will be your text
book.
Our class will be using the
web-app NIUResponse.com as a student response system this semester. Thanks to Dean Rajagopalan there is no charge
to you for this app this semester, saving you between $18.80 and $73.35
depending on which NIUResponse option you would have selected. You are required to bring a web-enabled
device (laptop, tablet, smartphone) to class each session so you can respond to
instructor questions via this app. If
you do not have a web-enabled device, please contact the instructor
immediately.
Note: To obtain participation points using
NIUResponse.com, you are expected to be IN CLASS. This web app is accessible from anywhere, but
please avoid the temptation to login while not physically present in the
class. If you are caught doing so, your
first offense will result in a 20-point semester deduction from your
Participation grade (Example: You were
set to receive a 96% in Participation for the semester, but in this case you
would receive a 76%), your second offense will result in you receiving a 0% for
Participation for the entire semester, and your third offense will result in
you receiving a failing grade (“F”) for the course.
GRADING:
The percentage breakdown of
your final grade is as follows:
15% Class Participation
15% Lab
Assignments
10% Project
(including presentation)
30% Quizzes
30% Final Exam
Grades are assigned as follows:
Above 93.5%: A
Above 89.5%: A-
Above 86.5%: B+
Above 83.5%: B
Above 79.5%: B-
Above 76.5%: C+
Above 69.5%: C
CLASS PARTICIPATION:
Attendance and active
participation constitute a significant component of evaluation in this
class. I am a very strong advocate of
those students who attend each class and make insightful comments. It is your
responsibility to read the material assigned before class and be prepared to
offer thoughts or insights as part of a general class discussion. Not only will such preparation garner
participation points, it will significantly reduce the time needed to prepare for
the quizzes and the final. A student
response system will be used in class to ensure that Participation is fairly
graded. Your base Participation grade
will be response questions answered correctly divided by 90% of response
questions asked (10% drop). You can
increase your Participation Grade by helping classmates with course material
(Helper System), or in the “Boost” category by adding value to class or solving
a stated issue.
LABS:
There
are several lab assignments throughout the semester, to be done on an
individual basis. These assignments will
be described in class. See the schedule
for due dates and description. Labs are
out of 100 points and a student’s lab average is composed of equal weighting of
all labs, with the lowest lab score automatically dropped.
MAJOR PROJECT:
This
will be done in groups.
Groups: By the end of the third week of class, you need to
have formed a project group. Sign-up
sheets will be on the Web. At the end of
the semester you will be asked to evaluate the contribution of the other
members of your group, and using that evaluation and my own observation,
certain group members will have their grades for group assignments adjusted.
Your
group will perform application of the
Data Analytics Lifecycle to a Big Data Analytics Challenge. More details will be presented in class.
QUIZZES:
There are five quizzes
throughout the semester, based primarily on the text readings and labs, and
also on any assigned outside readings, and class lectures and discussions. Quizzes will be taken during class time, and
results will be distributed online. A
zero will be given on any quiz missed and there will be no makeup's. The lowest quiz grade will be dropped for
each student. However, the "quiz
drop provision" is intended to assist those students who experience
circumstances beyond their control and must miss a quiz. While quizzes on which a student performs
poorly may also be dropped, it is not recommended that a student plan on using
the drop provision for that purpose. In
order to maintain grading consistency, special treatment cannot and will not be
given, under any circumstances. All
students are allowed to drop exactly one quiz.
Save your drop to make things easier on all of us!
FINAL EXAMINATION:
The final examination will be
a cumulative exam designed to be very similar to the quizzes. Think of it as a very long quiz.
STUDENTS WITH DISABILITIES
If
you need an accommodation for this class, please contact the Disability
Resource Center as soon as possible. The DRC coordinates accommodations for
students with disabilities. It is located on the 4th floor of the Health
Services Building, and can be reached at 815-753-1303 or drc@niu.edu.
Also,
please contact me privately as soon as possible so we can discuss your
accommodations. Please note that you will not be required to disclose your
disability, only your accommodations. The sooner you let me know your needs,
the sooner I can assist you in achieving your learning goals in this course.