Welcome!
The Data Science Journal Club course comes 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 interested in growing their data science, programming and high-performance computing skill set. Future years will see increasing demand for these skills in every employment sector. Our goal is to enable students to be more competitive in their future careers by facilitating self-directed growth in the data science space. We accept students at all skill levels of data science, assuming familiarity with computers and graphical plots.
While the course is mostly self-directed and hands-off, we don't want students to get lost. We want you to be successful and come away from this course feeling confident in applying the skills you have learned. If you have concerns, get stuck, need ideas or even a rubber duck, please feel free to contact us.
Please take the time to read this syllabus in its entirety. All of the information here should be useful as you work through the course.
Course Instructors (please contact us first!)
William Warriner (he/him), wwarr@uab.edu
Matthew Defenderfer, mdefende@uab.edu
Office Hours information is available here: https://docs.rc.uab.edu/#contact-us
Instructor of Record (please contact only as a last resort!)
Kristina Visscher, kmv@uab.edu
The University of Alabama at Birmingham considers the diversity of its students, faculty, and staff to be a strength and critical to its educational mission. In this class, we will strive to be an inclusive 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.
Please visit The DEI Website for more information.
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
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.
Due dates progress throughout the semester:
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.
Office Hours are held Mondays and Thursdays from 10 AM to noon via Zoom. Meeting links are available at our documentation (https://docs.rc.uab.edu/#contact-us).
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.
For students interested in learning more about any of the above with a focus on scaling with high-performance computing, we maintain a YouTube channel with some Data Science Club Material to get you started using Cheaha: https://www.youtube.com/channel/UCZoOS2e699Ge0DND1oy1BJQ.
Here is a list of free data science resources and starting points: https://www.datapen.io/. The following link narrows those resources down to just courses https://www.datapen.io/resources/free-course-resources.
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.
Research Computing Official Site: https://www.uab.edu/it/home/research-computing.
Cheaha Documentation: https://docs.rc.uab.edu.
Cheaha Web Portal: https://rc.uab.edu.
If you need software installed, or something just doesn't work like you expect, please email us at support@listserv.uab.edu. When asking for help, consider including all relevant details about your goal, what you tried, what you expected to happen, what actually happened, steps needed to reproduce the issue, and any error messages. If the error messages are longer than about 10 lines, copy-paste them into a text document and attach that to the email instead. Screenshots are also helpful. See our documentation on support (https://docs.rc.uab.edu/help/support) for more information.
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.