IS 603-01 Decision Making Support Systems (Fall 2023)

Information Systems Department
University of Maryland Baltimore County
Baltimore, Maryland 21250
Departmental Office: Room ITE 404, ph. 410-455-3206

Course Description

This course will provide an overview of data-driven decision-making via artificial intelligence (AI) and data science (DS) technologies. The emphasis is on how to use these technologies to solve real-world problems, rather than on the algorithmic or mathematical details of the methods. Students will obtain an understanding of both fundamental concepts and practical insights in data-analytic thinking, as well as a foundation for further study in AI and DS.

Student learning outcomes: By the end of this course, you will be able to:

Lecture time and venue: Tuesdays 4:30PM - 7:00PM Sherman Hall 150

Instructors: Dr. James Foulds and Dr. Shimei Pan
Instructor email: (jfoulds,shimei) [at] umbc [dot] edu. Please use Piazza for course-related questions, instead of email, so that everyone can benefit from the answers.
Instructor office hours: Tuesdays 3:30 - 4:30 pm, ITE 447 (Foulds) and ITE 434 (Pan). (Only the instructor giving the lesson will hold the office hour that week. Other times by appointment)

Piazza: Sign up for this course at
Poll Everywhere: Vote on in-class poll questions at (Dr. Foulds) and (Dr. Pan) .


Required Textbook

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, 1st Edition, by Foster Provost and Tom Fawcett is the course textbook. You will need this book for mandatory weekly readings.

Course Requirements and Grading

The project will be done in groups of 2-3. Project proposals are to be submitted and approved by the deadline. All reports will be submitted on Blackboard, and only one member of each project group needs to submit them.

In this course, participation means more than just showing up. It also refers to contributing to everyone's learning, through active engagement in peer instruction exercises, in-class discussions, and Piazza questions/answers. Participation grades will be assessed as a percentage of peer instruction questions answered (correctly or not), with a 90% response rate being sufficient for full points, and by Piazza contributions. Two or more contributions (either questions or answers) on Piazza will earn you 2% of the final grade (1% for each of the two contributions).

With respect to final letter grades, UMBC's Catalog states that an A indicates "superior" achievement; B, "good" performance; C, "not satisfactory"; D, "unacceptable"; F, "failure." There is specifically no mention of any numerical scores associated with these letter grades. Below is how grades may be assigned based on your final points, accumulated over the semester. Grades will be assigned using a plus/minus system. It is university policy that A+, D+, and D- are not assigned. We do not grade on a curve, so that everyone in the class has the opportunity to succeed.

Final Grade Letter Grade Points when calculating GPA
91 - 100 A 4.0
89 - 90.99 A- 3.7
87 - 88.99 B+ 3.3
81 – 86.99 B 3.0
79 - 80.99 B- 2.7
77 – 78.99 C+ 2.3
71 – 76.99 C 2.0
69 - 70.99 C- 1.7
60 – 68.99 D 1.0
0 – 59.99 F 0.0

Homework and Exam Policies


Chapter numbers in the readings refer to the Provost and Fawcett textbook.
Lecture Summary Details Assessment Required reading
9/5/2023Week 1 (JF and SP)Course overview, Data-Analytic Thinking Course overview, introduction to data-driven decision making and data-analytic thinking Ch 1, or Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64–73.
9/12/2023Week 2 (SP) Business Problems and Data Science Solutions Canonical data mining tasks, the data mining process, supervised vs unsupervised learning HW1 out Ch 2
9/19/2023Week 3 (SP)Introduction to Predictive Modeling Models, induction, and prediction, finding correlations, attribute selection, tree induction Ch 3
9/26/2023Week 4 (SP)Fitting a Model to Data Finding “optimal” model parameters, choosing the goal for data mining, objective functions, loss functions, linear models. Sharing project ideas.HW1 due, HW2 out. Project groups formed by this date Ch 4
10/3/2023Week 5 (SP) Overfitting and Its Avoidance Generalization, fitting and overfitting, complexity control, regularization, hold-out method Project proposal due Ch 5
10/10/2023Week 6 (SP) Similarity, Neighbors, and Clusters Calculating similarity, using similarity for prediction, nearest neighbors, clustering HW2 due, HW3 out Ch 6
10/17/2023Week 7(SP) What Is a Good Model? Evaluating machine learning methods, expected value framework, baselines, various evaluation metrics Ch 7
10/24/2023Week 8 (JF) Visualizing Model Performance Visualization of model performance under uncertainty, profit curves, cumulative response curves, lift curves, ROC curves HW3 due, HW4 out Ch 8
10/31/2023Week 9 Midterm (take home)
11/7/2023Week 10 (SP) Data Science Ethics Fairness/bias in AI, privacy, accountability, transparency HW4 due, HW5 out Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica, May, 23, 2016.
11/14/2023Week 11 (JF) Toward Analytical Engineering Solving business problems with data science, designing solutions based on the data, tools, and techniques available Project mid-term progress report due Ch 11
11/21/2023Week 12 (JF)Other Data Science Tasks and Techniques Co-occurrences and associations, link prediction, causal modeling HW5 due Ch 12
11/28/2023Week 13 (JF) Data Science and Business Strategy Acquiring competitive advantage via data science, curating data science capability Ch 13
12/5/2023Week 14 (JF) Conclusion overview, limits of data science, ethics, next steps Ch 14
12/12/2023Week 15 (JF and SP) Group project presentations Digital copies of presentation slides due.
12/19/2023 Exam week Take-home final exam (due 11:59pm Tues 12/19/2023). Project final report due Thurs 12/21/2023 (11:59pm)

The schedule may be subject to change. The summary and details columns are only a guideline of the content likely to be covered, and the dates on which material is covered may shift.

Instructional Methods

Traditional lectures will be augmented with active learning methods, primarily in the form of peer instruction exercises. Research has strongly indicated that active learning improves student outcomes in STEM fields versus traditional lecturing (Freeman et al., 2013). We will be using the Poll Everywhere service for polls and quizzes.

Pre-class reading assignments will be given for each lesson, which are very important for learning, and for making the best use of our limited time together (a partially "flipped classroom" approach). These readings are therefore required.


This course will make use of the free, open source WEKA data mining toolkit.

Academic Integrity

UMBC's policies on academic integrity will be strictly enforced (see the University System of Maryland's policy document, UMBC's academic integrity overview page, and the UMBC catalog). In particular, all of your work must be your own. Acknowledge and cite source material in your papers or assignments. While you may verbally discuss assignments with your peers, you may not copy or look at anyone else's written assignment work or code, or share your own solutions. Any exceptions will result in a zero on the assessment in question, and may lead to further disciplinary action. Some relevant excerpts from UMBC's policies, as linked to above, are:

The use of software tools to obfuscate your work in order to hide plagiarism is wrong, and is strictly prohibited.

Policy on ChatGPT and other Large Language Models (LLMs)

The use of language models such as ChatGPT for assignments will be strictly regulated. Your work must be your own. While language models can be lightly used as a writing aid, their use must be limited to a small part of any solution and they must not contribute to the substance of your answer. Any use of these tools as a writing aid must be declared in your submission, with an explanation of why the work is your own and why the use does not constitute plagiarism. You must also submit a log of all queries used in your submission. If you have any questions, please check with us.

Diversity Statement on Respect

Students in this class are encouraged to speak up and participate during our meetings. Because the class will represent a diversity of individual beliefs, backgrounds, and experiences, every member of this class must show respect for every other member of this class. (Statement from California State University, Chico’s Office of Diversity and Inclusion).

Accessibility and Disability Accommodations, Guidance and Resources

Accommodations for students with disabilities are provided for all students with a qualified disability under the Americans with Disabilities Act (ADA & ADAAA) and Section 504 of the Rehabilitation Act who request and are eligible for accommodations. The Office of Student Disability Services (SDS) is the UMBC department designated to coordinate accommodations that would create equal access for students when barriers to participation exist in University courses, programs, or activities.

If you have a documented disability and need to request academic accommodations in your courses, please refer to the SDS website at for registration information and office procedures.

SDS email:
SDS phone: (410) 455-2459

If you will be using SDS approved accommodations in this class, please contact me (instructor) to discuss implementation of the accommodations. During remote instruction requirements due to COVID, communication and flexibility will be essential for success.

Counseling Center

Diminished mental health can interfere with optimal academic performance. The source of symptoms might be related to your course work; if so, please speak with me. However, problems with other parts of your life can also contribute to decreased academic performance. UMBC provides cost-free and confidential mental health services through the Counseling Center to help you manage personal challenges that threaten your personal or academic well-being.

Remember, getting help is a smart and courageous thing to do -- for yourself and for those who care about you. For more resources get the Just in Case mental health resources Mobile and Web App. This app can be accessed by clicking:

The UMBC Counseling Center is in the Student Development & Success Center (between Chesapeake and Susquehanna Halls). Phone: 410-455-2472. Hours: Monday-Friday 8:30am-5:00pm.

Family Educational Rights and Privacy Act (FERPA) Notice

Please note that as per federal law we are unable to discuss grades over email. If you wish to discuss grades, please come to office hours.

Sexual Assault, Sexual Harassment, and Gender Based Violence and Discrimination

UMBC Policy in addition to federal and state law (to include Title IX) prohibits discrimination and harassment on the basis of sex, sexual orientation, and gender identity in University programs and activities. Any student who is impacted by sexual harassment, sexual assault, domestic violence, dating violence, stalking, sexual exploitation, gender discrimination, pregnancy discrimination, gender-based harassment, or related retaliation should contact the University’s Title IX Coordinator to make a report and/or access support and resources. The Title IX Coordinator can be reached at or 410-455-1717.

You can access support and resources even if you do not want to take any further action. You will not be forced to file a formal complaint or police report. Please be aware that the University may take action on its own if essential to protect the safety of the community. If you are interested in making a report, please use the Online Reporting/Referral Form. Please note that, if you report anonymously, the University’s ability to respond will be limited.

Notice that Faculty are Responsible Employees with Mandatory Reporting Obligations:

All faculty members are considered Responsible Employees, per UMBC’s Policy on Sexual Misconduct, Sexual Harassment, and Gender Discrimination. Faculty and teaching assistants therefore required to report all known information regarding alleged conduct that may be a violation of the Policy to the Title IX Coordinator, even if a student discloses an experience that occurred before attending UMBC and/or an incident that only involves people not affiliated with UMBC. Reports are required regardless of the amount of detail provided and even in instances where support has already been offered or received.

While faculty members want to encourage you to share information related to your life experiences through discussion and written work, students should understand that faculty are required to report past and present sexual harassment, sexual assault, domestic and dating violence, stalking, and gender discrimination that is shared with them to the Title IX Coordinator so that the University can inform students of their rights, resources, and support. While you are encouraged to do so, you are not obligated to respond to outreach conducted as a result of a report to the Title IX Coordinator.

If you need to speak with someone in confidence, who does not have an obligation to report to the Title IX Coordinator, UMBC has a number of Confidential Resources available to support you:

Other Resources:

Child Abuse and Neglect:

Please note that Maryland law and UMBC policy require that faculty report all disclosures or suspicions of child abuse or neglect to the Department of Social Services and/or the police even if the person who experienced the abuse or neglect is now over 18.

Pregnant and Parenting Students

UMBC’s Policy on Sexual Misconduct, Sexual Harassment and Gender Discrimination expressly prohibits all forms of discrimination and harassment on the basis of sex, including pregnancy. Resources for pregnant, parenting and breastfeeding students are available through the University’s Office of Equity and Civil Rights. Pregnant and parenting students are encouraged to contact the Title IX Coordinator to discuss plans and ensure ongoing access to their academic program with respect to a leave of absence – returning following leave, or any other accommodation that may be needed related to pregnancy, childbirth, adoption, breastfeeding, and/or the early months of parenting.

In addition, students who are pregnant and have an impairment related to their pregnancy that qualifies as disability under the ADA may be entitled to accommodations through the Office of Student Disability Services.

Religious Observances & Accommodations

UMBC Policy provides that students should not be penalized because of observances of their religious beliefs, and that students shall be given an opportunity, whenever feasible, to make up within a reasonable time any academic assignment that is missed due to individual participation in religious observances. It is the responsibility of the student to inform the instructor of any intended absences or requested modifications for religious observances in advance, and as early as possible. For questions or guidance regarding religious observances and accommodations, please contact the Office of Equity and Civil Rights at

Hate, Bias, Discrimination and Harassment

UMBC values safety, cultural and ethnic diversity, social responsibility, lifelong learning, equity, and civic engagement.

Consistent with these principles, UMBC Policy prohibits discrimination and harassment in its educational programs and activities or with respect to employment terms and conditions based on race, creed, color, religion, sex, gender, pregnancy, ancestry, age, gender identity or expression, national origin, veterans status, marital status, sexual orientation, physical or mental disability, or genetic information.

Students (and faculty and staff) who experience discrimination, harassment, hate, or bias based upon a protected status or who have such matters reported to them should use the online reporting/referral form to report discrimination, hate, or bias incidents. You may report incidents that happen to you anonymously. Please note that, if you report anonymously, the University’s ability to respond may be limited.

Campus Resources