Course Syllabus
Contact Information
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Delivery
- Synchronous Online: Learning occurs online and in real-time, using technology such as web-conferencing software for synchronous interaction. We will hold a weekly Live Session during the course that you will either attend in real-time or watch a recording to complete a make-up assignment. Please refer to the Course Components section of the Syllabus to review the Attendance requirements.
Course Prerequisites
- MIT 5602: Management Information Systems
- MIT 5032: Analytics Programming-R
Course Materials
You will be required to purchase a year's license for a software package called NodeXL. R and RStudio are free to download.
Software:
Textbooks:
Our course textbooks and references are Open Educational Resources, which you can access through the links below.
Program Learning Outcomes (PLO)
- Analyze data and provide valuable business insight
- Solve problems in relation to IT concerns and applications
- Communicate technical information to non-technical business professionals
- Demonstrate an understanding of the business, organizational, and regulatory context of information technology and organizational information systems
- Demonstrate an understanding of how information technology and systems can be leveraged to provide business value
Course Learning Outcomes (CLO) Alignment
I try hard not to waste time in our class. Everything we will do in this class will advance your analytics capabilities. We will focus on the following learning outcomes.
| CLO | Description | PLO |
|---|---|---|
| A | Develop the domain knowledge needed to engage with social analytics materials and concepts | 4, 5 |
| B | Perform data collection activities and analysis of social analytics data | 1, 2 |
| C | Use tools and techniques for social analytics OR Select appropriate tools and techniques for social analytics | 1, 2 |
| D | Analyze and critique methods and approaches for social analytics | 2, 3, 4 |
| E | Compose informed and measurable social analytics questions | 2, 3, 4 |
Grades
Breakdown
| Activity | Description | Points | Percentage |
|---|---|---|---|
| Participation | Attending Live Sessions or writing a summary of the Live Sessions | 8 @ 10 pts each | 10% |
| Quizzes | Preparation quizzes every week on readings and recorded lecture | 8 @ 10 pts each | 10% |
| Assignments | Weekly individual assignments | 8 @ 100 pts each | 30% |
| Group Project | Group project on a topic of choice | 100 points | 20% |
| Final Exam | Take-home, comprehensive final exam | 100 points | 30% |
| Total | 1160 points | 100% |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89% | B |
| 70–79% | C |
| 60–69% | D |
| Below 60% | F |
Course Components
Attendance During Live Sessions
I try to make every minute of our time together during Live Sessions valuable. Students will receive credit for attending Live Sessions. I realize that attending all sessions is sometimes not possible. So, if you miss it, you can also watch the recording of the Live Session and summarize it to receive the same credit. Postings that record attendance are due by midnight on the Sunday following the Live Session.
Quizzes
We will have a quiz every week to get us ready for each Live Session. Each quiz will be multiple choice and will be administered through Canvas. The quizzes will be open-book and are designed to incentivize becoming familiar with that week’s material. Quizzes are posted one week before the quiz due date and, unless otherwise posted, are due before class on the day the quiz is due. This is automatically enforced, with no exceptions. You are allowed to drop your lowest quiz score.
Assignments
Every week, we will have an assignment that will mirror the reading and in-class work for that week. In most cases, you will have the opportunity to perform the analyses we learn about that week on your own, with data that you collect, on a topic that you are interested in. These assignments will be due on the following Sunday of the week they are assigned.
Group Project
Class members will divide into 3-person teams and select a group project topic. The group project will address a current topic group members are interested in. Group members will work together to gather a data corpus, conduct analysis, interpret the results, and describe clear implications for organizations. At the conclusion of the group project, I will be soliciting peer evaluations and making needed adjustments to credit for individual team members.
Final Exam
The final exam is take-home and is open-book and open-note. It includes multiple choice and short answer questions regarding course topics. You are on your honor to not obtain help from anyone on the exam once the exam has been posted. The final exam will be comprehensive.
Course Policies
Communication
It is most effective to contact me through email. I try to respond the same day I get emails, but always within 24 hours. Posting on class discussion boards is also a good way to communicate with me in a way that lets other class members see.
Feedback
All feedback will be given within a week and will be delivered through canvas.
Late Policy
With the exception of university-excused absences, late work is not accepted. Due dates are automatically enforced. When juggling competing priorities, remember: It is always better to submit something for this class rather than nothing at all. I usually give partial credit.
Plagiarism
Plagiarism is the most common form of academic misconduct at OU. There is no college-level assignment that can be satisfactorily completed by copying. OU's basic assumption about writing is that all written assignments show the student's own understanding in the student's own words. That means all writing assignments, in class or out, are assumed to be composed entirely of words generated (not simply found) by the student, except where words written by someone else are specifically marked as such with proper citation. Including other people's words in your paper is helpful when you do it honestly and correctly. When you don't, it's plagiarism.
For more information about plagiarism, watch this video and then take this short course offered by University Libraries.
Use of Generative AI
Generative AI is one of the most exciting developments in social analytics. It is important that you know how to use generative AI tools. First, attempts must be your own work. However, you are allowed to use generative AI tools in revising code, proofing text, and sharpening analysis. Such use must be clearly disclosed in the submission.
When using generative AI tools, keep a couple of things in mind:
- These tools make mistakes and you are responsible for what you submit. When questioning low marks, I don't want to hear "But that is what ChatGPT said..."
- This class is an opportunity to learn and deepen your capabilities. If everything you produce during this class can be done by AI, you will be immediately replaceable.
University Academic Policies and Student Support
Access the University Academic Policies Document.
Course Summary:
| Date | Details | Due |
|---|---|---|