MIS 4470 - Practical Computing for Data Analytics

Course Information 

MIS 4470

Winter 2019, 3 credits

Tu 6:30p-9:00p in 202EH

This course provides hands-on experience necessary to analyze and identify patterns and insights from large business data sets. Programmatic analytical tools such as R, Python and SAS will be introduced. Data warehousing and analytics tasks such as data acquisition, data cleansing and preparation, analysis and visualization and communication of the results will be emphasized. Students will also be exposed to building, training and testing various machine learning, data mining and statistical models.
Prerequisite(s): QMM 2410 or STA 2226 and (MIS 3050 or MIS 4460) or EGR 1400 with a minimum grade of (C) in both courses.

Course Structure: Lab Sessions- We will meet once per week, Tue from 6:30-9:00 in 202EH. I envision the sessions to be similar to my MIS 4460/5460 class much more of a “studio” than a lecture class. There will usually be hands-on, interactive lessons where I present various topics and we bring them to life together with software tools. We’ll do problem solving, guided tutorials, and discussion of the business analytics and data science worlds. I’ll leave a large chunk of time each session for individual and group work during which I’ll be the roving consultant, answering questions and helping you figure things out for yourself. A tentative schedule of topics is near the bottom of this syllabus.

Professor Information  

Prof. Mark Isken

(isken@oakland.edu)

Office:317 Elliott Hall

(248.370.3296)

Main course website: http://www.sba.oakland.edu/faculty/isken/courses/mis5470_w19/

My OU website: http://www.sba.oakland.edu/faculty/isken/

My Github site: https://github.com/misken/

My hselab website: http://hselab.org/

My LinkedIn Profile: www.linkedin.com/pub/mark-isken/a/633/48/

Learning Outcomes 

In this class you will begin to learn how to use R and Python (along with a myriad of companion libraries) to do business analytics work such as:

• accessing datasets from a variety of sources and doing the “wrangling” needed to prepare them for analysis
• exploratory data analysis and visualization
• build predictive models using statistics and machine learning techniques
• create and manage reproducible analytical processes and workflows
• communicate results and tell stories with data and models

Textbooks and Materials 

Textbooks: Traditional textbooks don’t exist for a class like this. Instead we’ll be using a number of inexpensive paperback books that will cover different aspects of the course. They are all well worth owning. In addition, we will be using numerous free webbased resources. All these books have official websites from which you can buy print, PDF, or eBooks. Of course, you can also find them at numerous places on the web. I’ve listed approximate pricing from checking a few of the online booksellers. All the details about the textbooks is available from the course website page: http://www.sba.oakland.edu/faculty/isken/courses/mis5470_w19/course_logistics.html#textbooks.

Software: Business schools tend to be Microsoft dominated places. After all, Excel is the “Swiss army knife” of business and Powerpoint is everywhere. However, the analytics world is a far more diverse place. I’m going to give you an opportunity to explore a wide range of new tools and computing environments. I want all of us to be able to work in the same computing environment whether we are in the lab or at home. So, I’ve created a virtual machine based “analytic appliance” that we’ll call pcda. The pcda appliance comes preconfigured with:

• A flavor of the Linux OS called Lubuntu
• R and R Studio
• The Anaconda distribution of Python for scientific computing
• Geany, a nice text editor and programming IDE
• PyCharm - a great Python IDE
• A web browser, file manager, command shell, and other tools such as git (version control) and pandoc (file format converter)

Linux!? Yep, you are going to learn Linux. You may have heard of Ubuntu as it’s the most popular Linux “distro” out there for the average home user. Lubuntu is a lightweight version of Ubuntu that just has the minimal set of the Linux OS that we’ll need. While Lubuntu is GUI based, we will also be using the “shell” (like a Windows command line but a jillion times better and more powerful). Lubuntu (and Ubuntu) are both free and open source.

Both R and Python are free and open source products with huge communities of analytics users and contributors. They overlap to some degree but have distinct strengths. Both are well worth learning. They both allow you to do things that would be absolutely hideous and painful to do in Excel. The pcda appliance was created with VirtualBox, a free software package from Oracle for creating and using virtual machines. pcda will be available in the teaching lab and I’ll be showing you how you can use it on your own computer as well. As a start, you’ll need to download and install VirtualBox. See course website for detailed instructions on obtaining and using the pcda appliance - http://www.sba.oakland.edu/faculty/isken/courses/mis5470_w19/course_logistics.html#software-and-pcda-virtual-machine.

Microsoft Office 365 Free for OU Students. Enables access to a suite of Office 365 cloud products as well as download and install of the full Office Productivity Suite, including Word, Excel, PowerPoint, OneNote, and more. The software can be made available on up to 5 personal devices.

Assignments 

Grading 

• A: Comprehensive, thorough coverage of all objectives, required content, critical and higher level thinking, original and creative, sound use of English skills, both written and oral 

• B: Competent, mastery of basic content and concept, adequate use of English 

• C: Slightly below average work, has met minimum requirements but with difficulty 

• D: Has not met requirements of assignment/course, has significant difficulties in many areas

• F: Has not completed requirements; has not officially withdrawn from course before drop date

Assessment and Grading

There are three major components to grading:

• Homework assignments 60%
• In-class assignments and online quizzes 10%
• Final project 30%

Homework:  There will be a number of homework assignments that will give you a chance to apply the things you’ve learned in the class and to test your understanding of the material. The goal is learning. The course schedule includes required readings. The goal of the reading assignments is to prepare for class, to familiarize yourself with new terminology and definitions, and to determine which part of the subject needs more attention. The homework assignments may contain questions about the mandatory readings. When answering those please be brief and to the point!

Online Quizzes and In Class Assignments: There will be weekly, short, online quizzes. I’ve found them to be a useful incentive for keeping up with the material. There may also be a few in class assignments. Regarding quizzes, I will drop your lowest score. If you miss a quiz, do NOT ask me to reopen the quiz for you. Once quiz is closed, answers are available and it’s not fair to reopen the quiz.

Project: I want to give you the opportunity to apply your new skills to a dataset of interest to you. So, instead of a final exam, you’ll have a final project in which you’ll identify a question of interest for which relevant data exists. You’ll design and create analytical scripts in either R or Python (or both if you wish) to do the analysis, and then create various summaries and visualizations. You’ll figure out the best way to communicate the results and findings. For the project, you can form teams of 1-4 students. More information about the final project is available in Moodle.

Grading philosophy: I will evaluate your work holistically beyond mechanical correctness and focus on the overall quality of the work. In addition to the scores I will try to give some detailed written feedback. I really don’t want to put too much focus on grades. This is a new course, a challenging course, and I really just want people to start to learn this stuff so that they can compete on the analytics job market. So, I’m not going to take a very hard line on grading. I understand that this stuff is hard.

Collaboration Policy: You are welcome to discuss the course’s ideas, material, and homework with others in order to better understand it, but the work you turn in must be your own (or for the project, yours and your teammate’s). For example, you must write your own code, run your own data analyses, and communicate and explain the results in your own words and with your own visualizations. You may not submit the same or similar work to this course that you have submitted or will submit to another. Nor may you provide or make available solutions to homeworks to individuals who take or may take this course in the future.

Quoting Sources: You must acknowledge any source code that was not written by you by mentioning the original author(s) directly in your source code (comment or header). You can also acknowledge sources in a README.txt file if you used whole classes or libraries. Do not remove any original copyright notices and headers. However, you are encouraged to use libraries, unless explicitly stated otherwise! You may use examples you find on the web as a starting point, provided its license allows you to reuse it. You must quote the source using proper citations (author, year, title, time accessed, URL) both in the source code and in any publicly visible material. You may not use existing complex combinations or large examples. For example, you may not use a ready to use multiple linked view visualization. You may use parts out of such examples.

Missed Activities and Assignment Deadlines: Projects and homework must be turned in on time, with the exception of late days for homework as stated below. It is important that everybody attends and proactively participates in class and online. We understand, however, that certain factors may occasionally interfere with your ability to participate or to hand in work on time. Quizzes must be done on time.

Homework Deadlines and Late Days: Each student is given six late days for homework at the beginning of the semester. A late day extends the individual homework deadline by 24 hours without penalty. No more than two late days may be used on any one assignment. Assignments handed in more than 48 hours after the original deadline get a max grade of 80%. Late days are intended to give you flexibility: you can use them for any reason -no questions asked. You don’t get any bonus points for not using your late days. Also, you can only use late days for the individual homework deadlines - all other deadlines (e.g., project milestones, quizzes, in-class assignments) are hard.

Using Technologies 

Moodle

Course Websites: For those of you who’ve had my Business Analytics class, you know that I create extensive Moodle sites with tons of learning resources, files for use in class, screencasts, the assignments, forums, and whatever else I decide to create. While we’ll still use Moodle for assignments, quizzes, forums and a few other things, all of the course content will be available from a website that’s publicly available from my Oakland University website: http://www.sba.oakland.edu/faculty/isken/courses/mis5470_w19/

In addition to the course websites, I’ll be referencing some items on my http://hselab.org/ site as well.

Technology Back-up Plan

• In the event that your computer crashes or internet goes down, it is essential to have a “backup plan” in place where you are able to log in using a different computer or travel to another location that has working internet.

• Any files you intend to use for your course should be saved to a cloud solution (Google Drive, Dropbox, etc.) and not to a local hard drive, USB stick or external disk. Saving files this way guarantees your files are not dependent on computer hardware that could fail.

Technology Help 

• For help using Moodle, use the Get Help link at the top of the Moodle page (moodle.oakland.edu). 

• For access to technology and in-person assistance, call or visit the Student Technology Center (Link to Student Technology Center: https://www.oakland.edu/stc/).

• For general technology assistance, consult the OU Help Desk (Link to Help Desk: https://www.oakland.edu/helpdesk/).

 Respect Rules of Netiquette 

1. • Respect your peers and their privacy.

   • Use constructive criticism.

   • Refrain from engaging in inflammatory comments.

Classroom and University Policies 

Classroom Behavior 

1. Academic conduct policy. All members of the academic community at Oakland University are expected to practice and uphold standards of academic integrity and honesty. Academic integrity means representing oneself and one’s work honestly. Misrepresentation is cheating since it means students are claiming credit for ideas or work not actually theirs and are thereby seeking a grade that is not actually earned. For more information, review OU’s Academic Conduct Regulations. (Link to Academic Conduct Regulations: https://www.oakland.edu/deanofstudents/policies/)

2. Following are some examples of academic dishonesty:

• Cheating. This includes using materials such as books and/or notes when not authorized by the instructor, copying from someone else’s paper, helping someone else copy work, substituting another’s work as one’s own, theft of exam copies, falsifying data or submitting data not based on the student’s own work on assignments or lab reports, or other forms of misconduct on exams.

• Plagiarizing the work of others. Plagiarism is using someone else’s work or ideas without giving that person credit; by doing this, students are, in effect, claiming credit for someone else’s thinking. Both direct quotations and paraphrases must be documented. Even if students rephrase, condense or select from another person’s work, the ideas are still the other person’s, and failure to give credit constitutes misrepresentation of the student’s actual work and plagiarism of another’s ideas. Buying a paper or using information from the World Wide Web or Internet without attribution and handing it in as one’s own work is plagiarism.

• Falsifying records or providing misinformation regarding one’s credentials.

• Unauthorized collaboration on computer assignments and unauthorized access to and use of computer programs, including modifying computer files created by others and representing that work as one’s own.

3. Behavioral Code of Conduct. Appropriate behavior is required in class and on campus. Disrespectful, disruptive and dangerous behavior are not conducive to a positive learning environment and may result in consequences. Core Standards for Student Conduct at OU includes

• Integrity. See academic conduct policy points above.

• Community. Policies regarding disruptive behavior, damage and destruction, weapons, and animals.

• Respect. Policies regarding harassment, hazing, and sexual misconduct (Link to Sexual Misconduct policy: https://www.oakland.edu/policies/health-and-safety/625/)

• Responsibility. Policies regarding alcohol, drugs, and other substances

See the Student Code of Conduct for details. (Link to Student Code of Conduct: https://www.oakland.edu/deanofstudents/student-code-of-conduct/)

Accommodation and Special Considerations 

Oakland University is committed to providing everyone the support and services needed to participate in their courses. Students with disabilities who may require special accommodations should make an appointment with campus Disability Support Services (DSS). If you qualify for accommodations because of a disability, please submit to your professor a letter from Disability Support Services in a timely manner (for exam accommodations provide your letter at least one week prior to the exam) so that your needs can be addressed. DSS determines accommodations based on documented disabilities. Contact DSS at 248-370-3266 or by e-mail at dss@oakland.edu. 

For information on additional academic support services and equipment, visit the Study Aids webpage of Disability Support Services website. (Link to Disability Support Services website: https://www.oakland.edu/dss/)

Mental Health and Well-Being 

Oakland University is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, contact The OU Counseling Center at Graham Health at (248) 370-3465.  Student resources can also be found at the Dean of Students website by clicking on Student Health & Safety Resources. (URL: http://www.oakland.edu/deanofstudents)

Attendance policy 

Excused Absence Policy 

This policy for university excused absences applies to participation as an athlete, manager or student trainer in NCAA intercollegiate competitions, or participation as a representative of Oakland University at academic events and artistic performances approved by the Provost or designee. A student must notify and make arrangements with the professor in advance. For responsibilities and procedures see Academic Policies and Procedures, which includes other considerations such as a new Bereavement Leave Policy. (Link to Academic Policies and Procedures: https://www.oakland.edu/provost/policies-and-procedures/)

Religious Observances 

Students should discuss with their professor at the beginning of the semester to make appropriate arrangements. Although Oakland University, as a public institution, does not observe religious holidays, it will continue to make every reasonable effort to help students avoid negative academic consequences when their religious obligations conflict with academic requirements.  See The OU Diversity Calendar for more information. (Link to calendar: https://www.oakland.edu/diversity/calendar/) 

Preferred Name and Pronoun 

If you do not identify with the name that is listed with the registrar’s office, please notify me so that I may appropriately amend my records. In addition, if you prefer to go by a different pronoun, please inform me. For more information including a preferred first name on university records please review OU’s Preferred Name Policy (URL: oakland.edu/uts/common-good-core-resources/name-services/)

Sexual Misconduct 

Faculty and staff are responsible for creating a safe learning environment for our students, and that includes a mandatory reporting responsibility if students share information regarding sexual misconduct/harassment, relationship violence, or information about a crime that may have occurred on campus with the University. In such cases, the professor will report information to the campus’ Title IX Coordinator (Chad Martinez, chadmartinez@oakland.edu or 248-370-3496). You can report such incidents to the Dean of Students Office directly. Students who wish to speak to someone confidentially can contact the OU Counseling Center at 248-370-3465. Additionally, students can speak to a confidential source off-campus 24 hours a day by contacting Haven at 248-334-1274. The Dean of Students website provides more information on your options and support services. (oakland.edu/deanofstudents/sexual-assault-and-violence-initiative/students/)

Add/Drops 

As a student, university policy officially gives you the responsibility to add and drop courses. Put in your calendar deadline dates for dropping courses (even if you think it won’t be necessary), and consult the Drop or Not Guide to make a well-informed decision before dropping a course. (https://www.oakland.edu/registrar/registration/dropornot/)

Faculty Feedback: OU Early Alert System 

As a student in this class, you may receive “Faculty Feedback” in your OU e-mail if your professor identifies areas of concern that may impede your success in the class. Faculty Feedback typically occurs during weeks 2-5 of the Fall and Winter terms, but may also be given later in the semester and more than once a semester. A “Faculty Feedback” e-mail will specify the area(s) of concern and recommend action(s) you should take. Please remember to check your OU email account regularly as that is where it will appear. This system is to provide early feedback and intervention to support your success. (Link to Faculty Feedback for students: https://www.oakland.edu/advising/faculty-feedback/)

Emergency Preparedness 

In the event of an emergency arising on campus, the Oakland University Police Department (OUPD) will notify the campus community via the emergency notification system. The professor of your class is not responsible for your personal safety, so therefore it is the responsibility of each student to understand the evacuation and “lockdown” guidelines to follow when an emergency is declared. These simple steps are a good place to start:

• OU uses an emergency notification system through text, email, and landline. These notifications include campus closures, evacuations, lockdowns and other emergencies. Register for these notifications at oupolice.com.

• Based on the class cell phone policy, ensure that one cellphone is on in order to receive and share emergency notifications with the professor in class.

• If an emergency arises on campus, call the OUPD at (248) 370-3331. Save this number in your phone, and put it in an easy-to-find spot in your contacts.

• Review protocol for evacuation, lockdown, and other emergencies via the classroom’s red books (hanging on the wall) and oupolice.com/emergencies.

• Review with the professor and class what to do in an emergency (evacuation, lockdown, snow emergency).

Violence/Active Shooter: If an active shooter is in the vicinity, call the OUPD at (248) 370-3331 or 911 when it is safe to do so and provide information, including the location and number of shooter(s), description of the shooter(s), weapons used and number of potential victims.  Consider your options: Run, Hide, or Fight.