ITEC-320-002: Business Analytics Fall 2023
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ITEC-320-002: Business Analytics
Term: Fall 2023
Instructor Information
FACULTY NAME: Mahsa Oroojeni
LINK TO FACULTY BIO: https://www.american.edu/kogod/faculty/oroojeni.cfm
E-MAIL: [email protected]
OFFICE HOURS:M & Th, 4:00-5:00PM on Zoom
EMAIL RESPONSE TIME: Within 24 hours
ASSIGNMENTS FEEDBACK AND GRADING: Within five days
TA: Lawrence Collamer, Email: [email protected]
TA OFFICE HOURS: M,1:00-2:00pm
TA: Nedi Mouzourou, [email protected]
TA OFFICE HOURS: TBA
Class Time: T/F 11:20 AM - 12:35 PM (All times Eastern in this syllabus)
Course Description
Analytics is the process of transforming data into insight for making better decisions. It involves specifying a question, problem, or decision and finding the right answers using data. The process begins with identifying the appropriate data sources (internal and/or external, structured and/or unstructured), and the appropriate models, tools, and methods for analysis. Two areas of analytics are covered in this course: descriptive analytics examines historical data and identifies and reports historical patterns and trends, while predictive analytics predicts future trends and outcomes and discovers new relationships. Students are introduced to models, tools, and methods that are commonly used in each area of analytics. They develop skills in analytics that allow them to present data-driven solutions to problems in different business disciplines and functions. The course emphasizes model development and use of software tools to manage, report, and analyze data to achieve the best outcomes for a business.
Prerequisites: ITEC 200, STAT 202
Learning objectives:
After completing ITEC 320, a student should be able to:
1. Obtain and process data from existing data sources.
2. Use descriptive techniques to summarize data.
3. Build forecasting models to predict future outcomes.
4. Apply clustering techniques to data sets.
5. Apply prediction methods for numerical outcomes to data sets.
6. Apply classification methods for qualitative outcomes to data sets.
7. Recognize opportunities to apply analytics in various functional areas of an organization.
8. Apply several common techniques to visualize data.
Class structure:
This course uses a “flipped classroom” structure. Each week, there will be videos that you are required to watch on your own before Monday’s class. They are recorded short lectures and software tutorials. The videos are grouped by week on Canvas, with accompanying slides and datasets, and can be reached via the Modules page. A short quiz on the material from the videos will also be available via the Modules page for most weeks, and must be completed by 2:15 PM on Monday to receive credit. Our in-class time will be more hands-on and interactive, and will require you to have watched that week’s videos.
All of the required in-class work will take place on Mondays. Thursdays are optional working sessions where students can get more individual help with the concepts and software tools in the course. A dataset or exercise will sometimes be provided for the Thursday sessions, but those sessions are meant to be student-driven; show up with questions!
In an emergency, we’ll hold class online; updates and Zoom links will be provided as needed.
Reading:
The textbook for the course is Data Mining for the Masses, by Matthew North. A free pdf version is available at: https://docs.rapidminer.com/downloads/DataMiningForTheMasses.pdf. The datasets used by the textbook are also available for free, at https://sites.google.com/site/dataminingforthemasses/. In addition, occasional short readings will be provided via links in the course outline below, or on Canvas.
Office hours & interacting with your instructor:
I check email frequently; please feel free to email me any time if you have a question. My official policy is that I will get back to you within 24 hours. Barring crises, it’ll usually be faster than that.
My office hours are on Mondays and Thursdays 4-5 PM on Zoom.
Software tools:
We will primarily use Excel (including the Data Analysis add-in) and RapidMiner, which is available for free at https://my.rapidminer.com/nexus/account/index.html#downloads. We will also use Tableau for visualizing data; a free student version is available at https://www.tableau.com/academic/students. You will be expected to bring your laptop to class, and to be able to run the software tool(s) being used that week.
Important Dates:
Grading:
|
Deliverable |
Percentage |
Composition |
|
HW Assignments |
12% (4 assignments, 3% each) |
Individual |
|
Quizzes |
4% (10 quizzes, 0.4% each) |
Individual |
|
Activities (in-class) |
10% (6 activities, 1-3% each) |
Team |
|
Exams |
52% (26% midterm, 26% final) |
Individual |
|
Course project |
20% (1% for proposal, 3% for outline, |
Team |
|
Class Participation |
2% |
Individual |
|
TOTAL |
100% |
|
A: 93.0 or above; A-: 90.0-92.9;
B+: 87.0-89.9; B: 83.0-86.9; B-: 80.0-82.9;
C+: 77.0-79.9; C: 73.0-76.9; C-: 70.0-72.9;
D: 60.0-69.9; F: less than 60.0.
The course grade cutoffs are fixed and non-negotiable. Neither overall grades nor grades for individual deliverables will be curved.
Deliverables:
1. Homework Assignments: The assignments will involve the use of software, and will be submitted via Canvas. Discussing the assignments with classmates is encouraged; however, the submitted write-up must be the student’s own work. Writing quality is included in grading; expectations are provided in the course writing rubric. All submissions are checked automatically by anti-plagiarism software. Late submissions will lose 10% immediately, and an additional 10% for each hour that they are late.
2. Quizzes: There will be short multiple choice quizzes posted on Canvas for most weeks based on that week’s videos. The quizzes must be completed before class to receive credit. They are not meant to be difficult, only to make sure everyone is keeping up with the material.
3. Activities: The in-class activities will require students to address one or more challenges based on a real-world dataset and/or problem using techniques from the course. Each activity will take up most or all of the in-class time for that week. The dates of the activities are given in the course outline. Activities may be done either individually or in groups of up to four. (Groups do not have to be the same for each activity, nor the same as the project groups.) If you have to miss class on the day of an activity, you must let me know beforehand to be allowed to make it up. You will get a makeup version to do on your own, and it will be due by 11:59PM the following day.
4. Exams: There will be a midterm and a final, both of which will be in class. They are strictly individual, and are randomized in several ways. You will be allowed a calculator and a single page of notes; no other materials or electronic devices are permitted. The final exam covers only the material from the second half of the course.
5. Business Analytics Project: A team with 4-5 students will identify an organization and build models and methods to enhance data-driven decision making in that organization. Students will formulate the problem, identify the right sources of data, analyze data, and prescribe actions to improve both the process of decision making and the outcomes resulting from those decisions. This project will be delivered in four phases: a project proposal, a project outline, an in-class presentation, and a written report. Students may choose their own groups. All group members will be required to complete peer evaluation forms at the end of the semester, and are expected to notify me of any group problems as early as possible. I reserve the right to change group composition and to require non-contributors to do individual projects.
6. Class Participation: Participation is measured by the ability of students to bring quality discussion and contributions into class. The course is based on a model of active learning. Failure to do in-class work will reduce the participation grade, as will absences, lateness, and disruptive or unprofessional behavior. One absence is excused, with no questions asked.
COURSE OUTLINE (schedule is subject to change)
The date listed in the left column is Monday’s date for that week.
|
Date |
Topics |
Readings & Deliverables |
|
MODULE 1: INTRODUCTION |
||
|
Week 1: 28-Aug
|
Course Introduction |
Week 1 Videos |
|
Week 2: 4-Sept |
Obtaining & Processing Data Activity: Data cleaning |
Week 2 Videos |
|
Week 3: 11-Sept |
RapidMiner tutorial |
Week 3 Videos |
|
MODULE 2: DESCRIPTIVE ANALYTICS |
||
|
Week 4: 18-Sept |
Clustering Intro |
Week 4 Videos |
|
Week 5: 25-Sept |
Data Visualization 2 |
Week 5 Videos |
|
Week 6: 2-Oct |
Association Rules Activity: Recipe Associations |
Week 6 Videos
|
|
MODULE 3: FORECASTING |
||
|
Week 7: 9-Oct |
Midterm Review |
No class on Friday 13-Oct (Fall break) |
|
Week 8: 16-Oct |
Midterm Exam |
|
|
Week 9: 23-Oct |
Time Series Forecasting
|
Week 9 Videos
Week 9 Quiz |
|
MODULE 4: PREDICTIVE ANALYTICS |
||
|
Week 10: 30-Oct |
Activity: Capital Bikeshare Regression refresher |
Week 10 Videos
http://www.excel-easy.com/examples/regression |
|
Week 11: 6-Nov |
Intro to Predictive Analytics |
Week 11 Videos |
|
Week 12: 13-Nov |
Decision Trees |
Week 12 Videos |
|
Week 13: 20-Nov |
Prediction vs. Classification |
Week 13 Videos |
|
Week 14: 27-Nov |
Activity: Movie |
|
|
PRESENTATIONS AND COURSE WRAP-UP |
||
|
Week 15: 4-Dec |
Presentations Final Exam Review
|
Project Written Reports due by Dec 6 11:59 PM
|
|
15- Dec 08:10AM |
Final Exam |
|
Statement on the use of artificial intelligence (AI) tools in student work
In ITEC-320, you are permitted to use AI tools in a supporting role. However:
1) The use of AI tools in student work must ALWAYS be disclosed. (Explain exactly how you used it.) Failure to do so is considered plagiarism.
2) AI tools, given free rein, will sometimes plagiarize, make mistakes, and fabricate information. You are responsible for everything you submit! In this course, AI tools will do an extremely poor job on most of the deliverables. Do not use them without a thorough understanding of their strengths and weaknesses.
This is a new and rapidly evolving topic. Do not assume something that was acceptable in one class will be acceptable in another class.
BUSINESS NETIQUETTE
The term "Netiquette" refers to the etiquette guidelines for electronic communications, such as e-mail and discussion forum postings. Netiquette covers not only rules to maintain civility in discussions but also special guidelines unique to the electronic nature of forum messages. Please review Virginia Shea's The Core Rules of Netiquette for general guidelines that should be followed in this course.
Technical Requirements
Browser Information: For best performance, Canvas should be used on the current or first previous major release of either the Chrome or Firefox web browsers instead of Safari or Internet Explorer. Some multimedia objects will require you to enable third-party cookies in order to play them.
Canvas only requires an operating system that can run the latest compatible web browsers. Your computer operating system should be kept up to date with the latest recommended security updates and upgrades.
For more information please see the Canvas Community's information on browser compatibility.
Canvas Course Access: This online learning course uses technology created by Canvas, a leading provider of Internet infrastructure software for online education. The minimum technical requirements needed to participate in a Canvas course are available on the AU website. Participants will use their AU account to log in at https://canvas.american.edu.
Canvas Support: Participants can get help with Canvas using the Help menu, located at the bottom of your Global Navigation menu after you log in to Canvas. Users can also visit the global Canvas Community website for how-to guides and tutorials.
AU Help Desk (focuses on all other IT issues): Answers to your technology questions are just an e-mail, instant message, or phone call away. Contact the IT Help Desk at 202-885-2550, [email protected], or AskAmericanUHelp to reach one of our professional staff who can answer your questions and provide general troubleshooting assistance. Students can also log on to the Need Help Now portal for support.
AU Policy on Server Unavailability or Other Technical Difficulties: The university is committed to providing a reliable online course system to all users. However, in the event of any unexpected server outage or any unusual technical difficulty that prevents completion of a time-sensitive assessment activity, the instructors may extend the time windows and provide an appropriate accommodation based on the situation.
UNIVERSITY POLICIES
It is our shared responsibility to know and abide by the American University’s policies that relate to all courses, which include topics like:
· Academic integrity
· Emergency Preparedness
· Copyright Violations
· Academic Support Services
· Student Support Services
· Learning Resources
Please visit https://american.instructure.com/courses/9618 for the full list of campus-wide policies and follow up with me if you have questions.
DISCLAIMER
The instructor reserves the right to make modifications to this information throughout the semester.
Acknowledgement of Conditions of this Syllabus
I have read, understood, and accepted the conditions and requirements of the syllabus for ITEC-320 (Business Analytics) in Fall 2023. This includes all information regarding course material, attendance and conduct, preparation, exams and grade requirements, technology policies, and the statement and policy on academic integrity.
The syllabus acknowledgement and academic integrity quizzes on Canvas MUST be completed by the end of the second week of class.
2023-09-27