Please read the entire syllabus. All information will be useful to your work in the course.
The course is cross-listed as the following in BlazerNET.
| CRN | Subject | Course | Section | Title |
|---|---|---|---|---|
| 49145 | GBSC | 720 | VTK | JC- Data Science Club |
| 49150 | IDNE | 720 | VTR | Applications in Data Science |
The Data Science Journal Club course originated from a collaboration between the UAB IT Research Computing (RC) group, the Graduate Biomedical Sciences (GBSC) department, and the Neuroengineering (IDNE) department. The course exists to foster knowledge transfer between Research Computing and students who are interested in growing their data science, programming and high-performance computing skill set.
Future years will see increasing demand for technology skills in every employment sector. Our goal is to enable student competitiveness in their future careers through guided growth of data science-related skills. We accept students at all skill levels of data science, assuming familiarity with computers and reading graphical plots.
While the course is self-directed, we don't want students getting lost. We want you to be successful and feel confident in applying the skills you learn. If you have concerns, get stuck, need ideas or even a Rubber Duck, please feel free to contact us.
The Data Science Journal Club Course is a combination of a Journal Club and Independent Study. We expect students to identify and research one or more Data Science-related topics of their choosing, then apply what they've learned to practical problems. Study topics may be in data science, data engineering, data visualization, programming, high-performance computing, user-experience, and computer-related accessibility. The student's selected topics should be applicable to scientific research, ideally their own research projects. Students must perform three one-on-one demonstrations with instructors. Each demonstration must show what was learned and a practical implementation.
| Name | Role | |
|---|---|---|
| William Warriner (he/him) | wwarr@uab.edu | Instructor of Record (IoR) |
| Matthew Defenderfer | mdefende@uab.edu | Instructor |
| Prema Soundararajan | prema@uab.edu | Instructor |
Information about how and where to attend Office Hours may be found at https://docs.rc.uab.edu/#how-to-contact-us.
The University of Alabama at Birmingham considers all its students, faculty, and staff to be a strength and critical to its educational mission. In this class, we will strive to be a community where we can learn from the many perspectives and worldviews which may differ from our own. We are all expected to contribute to creating a respectful, welcoming, and safe environment that fosters a sense of belonging through open and honest dialogue. To this end, we should always conduct discussions in a way that honors, respects, and extends dignity to all class members.
The Office of Access & Engagement works to promote success for everyone in the UAB community. We work to address challenges faced by our students, faculty and staff to ensure everyone has access to available programs and resources they need to promote success and retention, and to foster an accessible and welcoming culture. Since its founding, UAB has played a pivotal role in shaping the future in Birmingham and beyond by making a positive difference in as many lives as possible. We do this by serving all people — a commitment that continues today through our vision, mission and shared values.
The Office of Access & Engagement strives to make sure every member of the UAB community has access to available programs and resources they need, recognizing that some students, faculty and staff may face more barriers and have a greater need for additional support than others due to their circumstances.
Please visit The Office of Access and Engagement Website for more information.
Note: This OAE section has replaced the previous section on DEI (Diversity, Equity, and Inclusion) to comply with AL-SB129-2024.
In accordance with Title IX, the University of Alabama at Birmingham does not discriminate on the basis of gender in any of its programs or services. The University is committed to providing an environment free from discrimination based on gender and expects individuals who live, work, teach, and study within this community to contribute positively to the environment and to refrain from behaviors that threaten the freedom or respect that every member of our community deserves. For more information about Title IX, policy, reporting, protections, resources and supports, please visit the UAB Title IX webpage for UAB's Title IX Sex Discrimination, Sexual Harassment, and Sexual Violence Policy; UAB's Equal Opportunity and Discriminatory Harassment Policy; and the Duty to Report and Non-Retaliation Policy.
UAB is committed to providing an accessible learning experience for all students. If you are a student with a disability that qualifies under the Americans with Disabilities Act (ADA) and/or Section 504 of the Rehabilitation Act, and you require accommodations, please contact Disability Support Services for information on accommodations, registration, and procedures. Requests for reasonable accommodations involve an interactive process and consist of a collaborative effort among the student, DSS, faculty and staff. If you are registered with Disability Support Services, please contact me to discuss accommodations that may be necessary in this course. If you have a disability but have not contacted Disability Support Services, please call (205) 934-4205 or visit the DSS website.
Our intent with the course has three parts. The first is for students to learn new techniques and technologies to assist with or facilitate data science. The second is for students to grapple with the application of those techniques and technologies. The third is to discuss with us how that went so we can check understanding and provide mentoring and guidance. We also love seeing all the cool stuff our students learn, and frequently find ourselves learning new perspectives, details, gotchas and technologies in the process.
To summarize students will:
All students are required to give three demonstrations during any Office Hours, one demonstration per visit. The demonstrations are student-led, informal discussions, with a presentation element. Students may collaborate on projects, but every student much show their own, original efforts. Each demonstration must be data science related, or somehow facilitate data science.
We realize the last bullet point is very broad, so we account for student knowledge, skill and experience when you give your demonstrations. We have no expectations of student starting skill level or experience. With that in mind, examples of past topics are included in the following list. If you have an idea but are unsure if it would be sufficient material, please feel free to contact us.
As you prepare for your demonstrations, please be thinking about the context of your work. An incomplete list of questions to guide your thoughts about context.
Be prepared for us to challenge you with new ways of thinking!
We are less interested in what your results are, and more interested in how you obtained those results and how they fit into the context of data science in general and your field in particular.
If you are not able to make the office hours sessions, please contact us as soon as possible to make alternative arrangements.
The dates in the course summary below are the last day of final exams, and therefore the due dates for the demonstrations. Please plan ahead to finish all three of them before that date. We cannot accept work after the due dates below without prior, individual discussion.
The course is treated as a pass/fail course. The grade reflected on your transcript may depend on the section you are in. If the section you are in uses a letter grade, then a pass is assigned "A" and fail assigned "F".
Grading is based on a syllabus attestation, due on the second Wednesday of classes, and on demonstrations. Three demonstrations are required, and each has its own assignment within Canvas.
See Assignments, or the bottom of this Syllabus, for details.
Due dates progress throughout the semester:
Syllabus Affirmation is due 2026-01-23.
Demonstration #1 is due 2026-02-20.
Demonstration #1 is due 2026-03-27.
Demonstration #1 is due 2026-05-01.
Because office hours are open to potentially hundreds of researchers outside the scope of this course, only one session can be presented per student per office hours visit. Please plan accordingly.
If for any reason you are not able to adhere to the above requirements, please contact us as soon as you know so we can make plans to accommodate you. Remember, we want you to be successful!
Our course may differ from the typical university course experience. We do not host regularly scheduled course meetings. Instead, we offer twice-weekly office hours. The office hours serve two purposes for this course.
Each student must make three office hours visits for the three demonstrations. Students are encouraged to attend office hours if they have questions that are best answered in a virtual meeting. Past examples include:
For students who are just entering the data science space, please consider trying some of Kaggle's micro-courses on data analysis, visualization and machine learning: https://www.kaggle.com/learn
For students who want to develop skills to facilitate data science, see the Software Carpentry's list of lessons: https://software-carpentry.org/lessons/. These lessons include the Unix shell, Git version control and programming skills in Python and R.
For students who want to develop data literacy skills in the context of specific fields, see the Data Carpentry's list of lessons: https://datacarpentry.org/lessons/. One of the included fields is genomics, which may be of particular interest to many UAB students.
Feel free to seek out and use other sources! If you do so, please inform us of those sources so we can curate our list and better serve future students.
We encourage you to responsibly explore usage of AI. You are not required to use AI for any part of this course. As always, beware of AI nonsense or "hallucinations". As a budding technologist, we want you to be aware that "hallucination" is a marketing term. We prefer to call it what is: nonsense. Nevertheless, the term of art is "hallucination" so, to avoid confusion, we will continue using that term.
You should be aware that hallucinations are a mathematical certainty and undetectable without independent verification. In other words, you need to already understand the AI's output (and its relationship to reality) well enough to be able to spot hallucinations. Alternatively, you can have an expert check the output. Otherwise, you cannot hope to spot hallucinations reliably. And, now that you are aware of these facts, you should be aware it would be intellectually dishonest to pretend AI output is valid or truthful when you haven't verified. Always verify AI output!
You may use AI to assist with the development of any demonstration assignments, subject to the UAB AI guidelines, and our additional, course-specific guidelines below.
Remember, the purpose of the course is for you to learn something and show off what you learned. Don't replace your mind with AI. This is a chance for you to shine, so don't let the AI outshine you!
I'm completely new to data science, what are some good starting points?
To get started with general data science tools and techniques, try Kaggle Learn: https://www.kaggle.com/learn
Check out the Data Carpentries Lessons: https://datacarpentry.org/lessons/
For starting bioinformatics, also try Rosalind.info: https://rosalind.info/problems/locations/
What tools are available to make it easier to manage my projects?
We suggest using Git! Learn more at: https://swcarpentry.github.io/git-novice/
Am I allowed to use my own research projects for this course?
Yes, and we encourage applying new tools and techniques! Be mindful that we can't accept any work for research protected by IRB or using PHI/HIPAA data, or data that requires any special security compliance. Be sure to get your supervisor's approval first.