OMIS 665

Big Data Analytics

Northern Illinois University

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.