DTSA 5798 Supervised Text Classification for Marketing Analytics

Instructors: Chris Vargo, Scott Bradley

Course Description

Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students will learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students will walk through a conceptual overview of supervised machine learning, and dive into real-world datasets through instructor-led tutorials in Python. The course will conclude with a major project.

Course Objectives

By completing this course, you will be able to:

• Describe text classification and related terminology (e.g., supervised machine learning)
• Apply text classification to marketing data through in a peer-graded project
• Apply text classification to a variety of popular marketing use cases via structured homework
• Evaluate the text classification models you create for your project
• Tune and improve the performance of the text classification models you create for your project

MS-DS Program Learning Outcomes

Successful completion of this course demonstrate your achievement of the following learning outcomes for the MS-DS program:

• Acquire, clean, wrangle, and manage data.
• Correctly perform exploratory data analyses in order to assist with the generation of scientific hypotheses.
• Apply principles and methods of probability theory and statistics to draw rational conclusions from data.
• Construct an appropriate statistical model in order to answer important scientific or business-related questions.
• Assess the validity of a statistical model when applied to a particular dataset.
• Understand the principles of efficient algorithms for dealing with large scale data sets and be able to select appropriate algorithms for specific problems.
• Understand and be able to apply the main computational techniques used to analyze large data sets, including a variety of data mining and machine learning approaches.
• Correctly apply the data science skills above to a specific domain area (e.g., business, climate science).
• Clearly communicate the results of a data science analysis to a non-technical audience.
• Use peer feedback, self-reflection and video analysis to improve collaboration skills.
• Create reproducible statistical workflows.

Drops, Tuition Refunds, and Withdrawals

Because the MS-DS has flexible course start dates, all drops, tuition refunds, withdrawals and grades are handled at the individual course level. It is the student’s responsibility to monitor these deadlines. Coursera and CU Boulder are not responsible for notifying the students of these deadlines. For approximate session timelines, access the Boulder MS-DS Onboarding Course via the MS-DS degree homepage. To drop or withdraw from a course please complete the appropriate form on the CU Boulder Office of the Registrar website.

Drops and Refunds

Each student has 14 days from a class start date or their enrollment date (whichever is later) to request a drop for 100% tuition refund. Students are only eligible to drop a course if they have not accessed the restricted content (timed proctored exam) or received a grade.  When a course is dropped under these conditions, it will not appear on the student’s record.

Withdrawal

Students who request to drop the course after the 14-day period and who have not accessed the timed proctored assessment may withdraw from the course but will not receive a refund. When a student withdraws from a course under these conditions, the student will receive a grade of W on their academic record. W grades have no bearing on the GPA and credit total.

Students who access a timed, proctored final exam are ineligible for a drop, withdrawal, or refund, and are assigned a grade.

Grading

Course Grading Policy

Uniform Letter Grade Rubric

Grade percentages convert to letter grades according to the scheme below. 73% or higher is considered passing.

Program Policies

Suspected Violations of AI Tool Usage Policy

If program staff suspects you may have used AI tools to complete assignments in ways not explicitly authorized or suspect other violations of the honor code, they will contact you via email. Be sure to respond promptly to any related communication so your perspective is included in the case review. Failure to respond timely will not prevent the completion of a case review.

In suspected cases of unauthorized AI tool usage, the program may:

• Request the documentation noted above (see AI Usage Documentation Guidelines) or other supplementary materials
• Issue a warning
• Assign a 0–50% grade for the question
• Assign a 0–50% grade for the assignment
• Assign an F grade for the course
• Reference prior violations
• Remove access to the course, related materials, and tools
• Contact the Office of Student Conduct & Conflict Resolution to report suspected Honor Code violations

Turnitin and similar AI detection tools may be used in these courses for initial detection of possible honor code violations. All suspected violations will be reviewed by a human. AI tools alone will not be used to determine if an assignment is plagiarized, and results from these tools will not be used alone as evidence to penalize students.

University Policies

Accommodation for Disabilities

If you qualify for accommodations because of a disability, please submit your accommodation letter from Disability Services to your faculty member in a timely manner so that your needs can be addressed. Disability Services determines accommodations based on documented disabilities in the academic environment. Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or dsinfo@colorado.edu for further assistance. If you have a temporary medical condition, see Temporary Medical Conditions on the Disability Services website.

Classroom Behavior

Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, political affiliation or political philosophy. Class rosters are provided to the instructor with the student's legal name. We will gladly honor your request to address you by an alternate name or gender pronoun. Please advise us of this preference early in the semester so that we may make appropriate changes to my records. For more information, see the policies on classroom behavior and the Student Code of Conduct.

Honor Code

The University of Colorado Boulder takes issues of academic dishonesty extremely seriously.

Students in all of CU Boulder’s courses, whether not-for-credit or for-credit, are expected to perform to the highest standards of academic honesty.

Students enrolled in for-credit courses are members of the CU Boulder’s community and are subject to the Honor Code Office’s policies and procedures. Information on the Honor Code can be found at the Honor Code Office website.

Students who violate the Honor Code are subject to discipline. Violations of the policy may include: plagiarism, cheating, fabrication, lying, bribery, threats, unauthorized access to academic materials, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty. Students are specifically expected to turn in original work and cite portions created by other authors. If a student has doubts regarding what collaboration is permissible in the course, the student should consult the discussion forums or the course facilitator directly.

Sexual Misconduct, Discrimination, Harassment and/or Related Retaliation

CU Boulder is committed to fostering a positive and welcoming learning, working, and living environment. CU Boulder will not tolerate acts of sexual misconduct (including sexual harassment, exploitation, and assault), intimate partner violence (including dating or domestic violence), stalking, protected-class discrimination, or harassment by members of our community. Individuals who believe they have been subject to misconduct or retaliatory actions for reporting a concern should contact the Office of Institutional Equity and Compliance (OIEC) at 303-492-2127 or cureport@colorado.edu. Information about the OIEC, university policies, reporting options, and other resources can be found on the OIEC website.

Please know that faculty and instructors have a responsibility to inform OIEC when made aware of incidents of sexual misconduct, discrimination, harassment, and/or related retaliation, to ensure that individuals impacted receive information about reporting options and support resources. This applies regardless of where or when an incident occurs as long as it involves a member of the CU community.

Religious Holidays

Campus policy regarding religious observances requires that faculty make every effort to deal reasonably and fairly with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. Since this is an online class, with no fixed weekly calendar schedule, we ask that you arrange your workload to accommodate your religious practice. See the campus policy regarding religious observances for full details.