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data_science_journal_club_assignment.html 0 → 100644
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<p>Bear in mind objectives and resources from the <a title="Syllabus" href="">Syllabus</a>:</p>
<ol start="1" type="1">
<li>Learn new data science stuff</li>
<li>Grapple with that new stuff</li>
<li>Discuss that new stuff with us</li>
</ol>
<p>Data Science Club Material to get you started: <a class="external"
href="https://www.youtube.com/channel/UCZoOS2e699Ge0DND1oy1BJQ" target="_blank"
rel="noopener"><span>https://www.youtube.com/channel/UCZoOS2e699Ge0DND1oy1BJQ</span></a>. Feel free to use other
sources, please inform us of those sources.</p>
<p>Office Hours are held Mondays and Thursdays from 10 AM to noon via Zoom. Meeting links are available at <a
class="inline_disabled" href="https://docs.rc.uab.edu/#contact-us" target="_blank"
rel="noopener noreferrer">https://docs.rc.uab.edu/#contact-us</a>.</p>
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data_science_journal_club_checklist.md 0 → 100644
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# DSJC New Session Checklist
The intent is to have the course run every session each year: Fall, Spring, Summer. The UAB Academic Calendar dictates when classes start for each session, and the goal should be to have the course published the week before classes start.
Instructor of Record: Kristina M. Visscher <mailto:kmv@uab.edu>
UAB Academic Calendar: <https://www.uab.edu/students/academics/academic-calendar>
## The Checklist
- [ ] Merge GBSC and IDNE sections. Requires a ticket via course instructor Kristina Visscher <mailto:kmv@uab.edu>.
- [ ] Add course TAs and instructors as needed. Requires a ticket via course instructor Kristina Visscher <mailto:kmv@uab.edu>. This must occur _after_ the merger! Please instruct central IT on this point.
- [ ] Upload the [syllabus](data_science_journal_club_syllabus.html)
- Click the `</>` symbol at the bottom-right of the big text box and copy-paste the HTML (linked above) into the box. Click the symbol again to verify the formatting is correct.
- Show Course Summary: checked
- **Manually verify all links!**
- [ ] Set up the [assignments](data_science_journal_club_assignment.html)
- Assignment name e.g. `Demonstration #1`
- Click the `</>` symbol at the bottom-right of the big text box and copy-paste the HTML (linked above) into the box. Click the symbol again to verify the formatting is correct.
- **Change the "Syllabus" link to the new course syllabus URL.**
- Points: `1`
- Display Grade as: `Complete/Incomplete`
- Submission Type: `No Submission`
- Group Assignment: unchecked
- Peer Reviews: unchecked
- Assign:
- Assign to: Everyone
- Due: last day of final exams according to the current session on <https://www.uab.edu/students/academics/academic-calendar>
- Available from/until: blank
- Click save and publish! Ensure the assignments are published in the Assignments main page, with a green checkmark icon next to each.
Once the first one is created, go to the main Assignments page. Click the vertical three dots next to the assignment you created. Click "Duplicate" and go from there.
- **Manually verify all links!**
- [ ] Adjust settings
- Course Details tab:
- Set appropriate start and end dates according to <https://www.uab.edu/students/academics/academic-calendar>
- End date should be at least one week after the last day of final exams so students may review their grades and course material
- Typically Canvas does a good job of picking these dates
- Navigation tab:
- Disable all except:
- Home
- Announcements
- Syllabus
- Assignments
- Grades
- People
- Chat
- UAB Libraries
- UAB Cares
- UAB Policies
- Technology Resources
- Student Academic and Support Services
- Put them in the above order
- [ ] Use Student View to verify the course has appropriate appearance and function.
- Make sure syllabus looks good
- Make sure assignments are visible (they need to be published)
- **Manually verify all links!**
- [ ] Publish the course. Requires the help of instructor Kristina Visscher <mailto:kmv@uab.edu>.
- [ ] Make an announcement welcoming new students.
data_science_journal_club_syllabus.html 0 → 100644
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<h2 id="top_">SP2023 GBSC/IDNE 720/790-VTR JC- Data Science Club</h2>
<p>Welcome!</p>
<p>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.</p>
<p>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 <a href="https://en.wikipedia.org/wiki/Rubber_duck_debugging">rubber
duck</a>, please feel free to contact us.</p>
<p>&nbsp;</p>
<p>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.<a href="http://www.uab.edu/dss"></a></p>
<p>&nbsp;</p>
<h3 id="dei">Diversity, Equity and Inclusion (DEI) Statement:</h3>
<p>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. 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 will conduct discussions in a way that honors, respects, and extends dignity to everyone.</p>
<p>Please visit <a class="inline_disabled" href="https://www.uab.edu/dei/" target="_blank"
rel="noopener">https://www.uab.edu/dei/</a> for more information.</p>
<p>&nbsp;</p>
<h3 id="intent">Our Intent:<strong><br /></strong></h3>
<p>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.</p>
<p>To summarize students will:</p>
<ol start="1" type="1">
<li>Learn new data science related stuff</li>
<li>Grapple with that new stuff</li>
<li>Discuss that new stuff with us</li>
</ol>
<p>&nbsp;</p>
<h3 id="work">Required Work:</h3>
<p>All students are required to give three demonstrations<strong></strong></p>
<ul>
<li>during any office hours, one per visit...</li>
<li>...in a student-led informal discussion style.</li>
<li>Students may collaborate on projects...</li>
<li>...but each student must show their original work.</li>
<li>Each demonstration must be data science related or somehow facilitate data science.</li>
</ul>
<p>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.</p>
<ul>
<li>Using machine learning or deep learning to create a predictive model.</li>
<li>Creating your very first Anaconda environment to share with collaborators.</li>
<li>Learning to use version control (git and github) with your existing code.</li>
<li>Extracting insights from existing results by applying new visualization methods.</li>
<li>Migrating a data analysis workflow from Excel to Python and pandas.</li>
<li>Scaling a data analysis workflow from a lab workstation to Cheaha.</li>
<li>Exploring a new programming language.</li>
<li>Writing your very first script in any programming language.</li>
<li>Identifying and extracting salient features from complex data for further processing.</li>
<li>Understand what makes data tidy and why it is important.</li>
</ul>
<p>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.</p>
<ul>
<li>What are some drawbacks or limitations?</li>
<li>How does it compare to alternatives?</li>
<li>How does it work?</li>
<li>Why does it work?</li>
<li>Where can we go next?</li>
<li>Why will others want to use my work?</li>
<li>How can I transfer techniques to other projects?</li>
</ul>
<p>Be prepared for us to challenge you with new ways of thinking!</p>
<p><strong>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.<br /></strong></p>
<p>If you are not able to make the office hours sessions, please contact us as soon as possible to make alternative
arrangements.</p>
<p><strong>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.<br /></strong></p>
<p>&nbsp;</p>
<h3 id="grading">Grading:</h3>
<p>The course is pass/fail. Grading is based purely on attendance and demonstrations. Three demonstrations are required,
each has its own assignment within Canvas.</p>
<p>The demonstrations are all due by the end of the session (i.e. Fall, Spring, Summer). Because office hours are open
to potentially hundreds of researchers outside the scope of this course, <strong>only one session can be presented
per student per office hours visit.</strong> <strong><em>Please plan accordingly.</em></strong></p>
<p>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!</p>
<h3 id="scheduling">Scheduling, Attendance &amp; Office Hours:</h3>
<p>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.</p>
<ol style="list-style-type: decimal;">
<li>Each student must make three office hours visits for the three demonstrations.</li>
<li>Students are encouraged to attend office hours if they have questions that are best answered in a virtual
meeting. Past examples include:</li>
</ol>
<ul>
<li style="list-style-type: none;">
<ul style="list-style-type: disc;">
<li>Is my topic suitable for this course?</li>
<li>Where can I find more resources on topics?</li>
<li>Can you preview my demonstration material?</li>
<li>Am I on the right track?</li>
</ul>
</li>
</ul>
<p>Office Hours are held Mondays and Thursdays from 10 AM to noon via Zoom. Meeting links are available at our
documentation (<a href="https://docs.rc.uab.edu/#contact-us" target="_blank"
rel="noopener">https://docs.rc.uab.edu/#contact-us</a>).</p>
<p>&nbsp;</p>
<h3 id="resources">Course Resources:</h3>
<p>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: <a class="inline_disabled" href="https://www.kaggle.com/learn"
target="_blank" rel="noopener">https://www.kaggle.com/learn</a>.</p>
<p>For students who want to develop skills to facilitate data science, see the Software Carpentry's list of lessons: <a
class="inline_disabled" href="https://software-carpentry.org/lessons/" target="_blank"
rel="noopener">https://software-carpentry.org/lessons/</a>. These lessons include the Unix shell, Git version
control and programming skills in Python and R.</p>
<p>For students who want to develop data literacy skills in the context of specific fields, see the Data Carpentry's
list of lessons: <a class="inline_disabled" href="https://datacarpentry.org/lessons/" target="_blank"
rel="noopener">https://datacarpentry.org/lessons/</a>. One of the included fields is genomics, which may be of
particular interest to many UAB students.</p>
<p>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: <a
class="external" href="https://www.youtube.com/channel/UCZoOS2e699Ge0DND1oy1BJQ" target="_blank"
rel="noopener"><span>https://www.youtube.com/channel/UCZoOS2e699Ge0DND1oy1BJQ</span></a>.</p>
<p>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.</p>
<p>&nbsp;</p>
<h3 id="support">Technical Support:</h3>
<p>Research Computing Official Site: <a class="external" href="https://www.uab.edu/it/home/research-computing"
target="_blank" rel="noopener">https://www.uab.edu/it/home/research-computing</a>.</p>
<p>Cheaha Documentation: <a class="external" href="https://docs.rc.uab.edu" target="_blank"
rel="noopener">https://docs.rc.uab.edu</a>.</p>
<p>Cheaha Web Portal: <a class="external" href="https://rc.uab.edu" target="_blank"
rel="noopener">https://rc.uab.edu</a>.</p>
<p>If you need software installed, or something just doesn't work like you expect, please email us at <a
href="mailto:support@listserv.uab.edu" target="_blank" rel="noopener">support@listserv.uab.edu</a>. 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 (<a href="https://docs.rc.uab.edu/help/support" target="_blank"
rel="noopener">https://docs.rc.uab.edu/help/support</a>) for more information.</p>
<p>&nbsp;</p>
<h3 id="dss">Disability Support Services (DSS) Statement<strong>:<br /></strong></h3>
<p>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 934-4205 or visit <a
href="http://www.uab.edu/dss">http://www.uab.edu/dss</a>.</p>
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<p>Hello everyone and welcome to Data Science Journal Club (DSJC)!</p>
<p>Please take a few minutes to read over the <a title="Syllabus"
href="/courses/1593764/assignments/syllabus">syllabus</a> as it is the central repository for information about
this course.</p>
<p>If you are new to the course, welcome! We hope you have an enlightening experience. We are happy to answer questions
and provide guidance, please feel free to reach out with questions, and please read the syllabus for more detailed
information about expectations.</p>
<p>If you are returning, welcome back! Not much has changed this time around other than a slightly updated syllabus.</p>
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