Michigan State University Data Science Capstone.
All projects you will find “in the real world” require you to learn something. Knowing how to learn something new is a key learning goal of this class. To help with the many skills you may need for your project, this capstone course maintains a repository of student generated “tutorials” which can be found in the following repository:
We will be working on these during Friday classes for the rest of the semester. In today’s assignment we would like you to just review what has been done. There are three major goals for this review.
Your team’s task for the day is to go through one of the tutorials (assigned by team by the instructors), find bugs, issue or improvement and then submit a “issue” to the git repository.
The following is a list of teams and the assigned tutorials from previous semesters.
Team | Tutorial |
---|---|
CEPI - Effective Communications | Functions_in_Python.ipynb Pandas.ipynb Seaborn_Tutorial_DTTD.ipynb TensorFlow_Tutorial.ipynb |
Documenters - Leadership Dossiers | AudioDataTutorial.ipynb FFmpegDemo.ipynb StandardLibrary_C++.ipynb Basic_Containers.ipynb |
Grassroots | Zotero_Instructions.ipynb GAMA_AutoML_Tutorial.ipynb Loops.ipynb PTest.ipynb |
HAP - At Risk Populations | PySpark_Tutorial.ipynb Central_Limit_Theorem.ipynb Numpy_Sympy.ipynb BigO_C++.ipynb |
HFH - Imaging QC | R_Shiny_App_Tutorial Tidyverse_Tutorial.ipynb pcatutorial.ipynb DTTD_Tutorial_Widgets-D2LAPITeam.ipynb |
HFH - Motion Tracking | ssh_key_gen Scope_C++.ipynb Eigenvalues.ipynb |
HFH - Pre / Post Operative Comparison | GridSearchCV_Tutorial.ipynb MorphologicalOperators_Tutorial DTTD_PowerBI_Tutorial.ipynb Auto_Cropping_Image_Tutorial |
HFH - Revenue Cycle | Instructions.ipynb References.ipynb BeautifulSoup.ipynb FineTune-Mistral-LLM-OwnData |
MSU - Healthy City Assessment | image_thresholding_tutorial AnomalyAndOutlierDetection.ipynb Video-Image-Data-Tutorial Mimesis.ipynb |
MSU - Justice for Otego | GoogleSheetsTutorial.ipynb Dask_Tutorial.ipynb censusdata_package_tutorial Pointers.ipynb |
MSU - University Wellness Database | tpot_tutorial.ipynb Classification.ipynb GoogleSheetsTutorial1.ipynb |
MSU - Grapes | GoogleSheetsTutorial DataSynthesizer_Tutorial.ipynb Streamlit BFG_Tutorial_DTTD.ipynb |
QSIDE - Act-Now | YOLO_Tutorial Gradients.ipynb RREF.ipynb Whitespace_Indentation.ipynb |
TechSmith - Healthy and Engaged User Data Exploration | Auto-SKLearn_AutoML socail_media_scrapper Tableau |
TwoSix - Public Feedback on Environment | PyTorch_tutorial.ipynb FuzzyWuzzy.ipynb Create_a_python_package.ipynb GUI_Tutorial.ipynb |
Your group is expected to review all of the tutorials. However, today we will start with just this small set. As a group do the following:
Gitlab and github have a simple mechanism for reporting “issues” inside the repository. Go to the SCHOLAR gitlab page and click on the “issues” button at the top. Once there you can read through the current issues and make new ones by pressing the green “new issues” button on the top right. When creating the issue put in a lot of details, be very specific about what file has the problem and what you know needs to be done to fix it. Please consider the following when submitting an issue:
Each member of the team should author at least one NEW git issue (comments to existing issues do not count). More is better but help each other out and try to make good quality issues that have substance and are not redundant and/or just filler. There is always something that is missing or needs improvement.
NOTE: I realize we are using a lot of jargon. This is normal when you start a new job. Please research anything you don’t understand and talk with your team. Come to your instructor with questions if you can’t figure out something together.
Written by Dr. Dirk Colbry, Michigan State University
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.