CMSE 495
Michigan State University Data Science Capstone.
In-Class Assignment: Tutorial Review
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.
- Learn how to better use git to interact with code.
- Learn what content is available in this repository that may help you with your projects.
- Identify ways the repository can be used to help future students.
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.
Agenda (80 Minutes)
- (50 minutes) Group Review of the Data Tools Tutorial Demo (DTTD)
- (10 minutes) Report out to class
- (20 minutes) Group co-work time
1. Group Review of the Data Tools Totorial Demo (DTTD)
The following is a list of teams and the an assigned tutorial form previous semesters.
| Team | Tutorial |
|---|---|
| CEPI - Anomaly Detection | GAMA_AutoML_Tutorial.ipynb FFmpegDemo.ipynb |
| City of Grand Rapids - Social Impact | pcatutorial.ipynb FuzzyWuzzy.ipynb |
| Ford - Defect Prediction | Video-Image-Data-Tutorial Seaborn_Tutorial_DTTD.ipynb |
| HAP - Synthetic Data Generation | Classification.ipynb Auto_Cropping_Image_Tutorial |
| HFH - Revenue Cycle Prediction | Streamlit AudioDataTutorial.ipynb |
| ICER - User Data Analytics | tpot_tutorial.ipynb GoogleSheetsTutorial.ipynb |
| Intramotev - Automated Video Data Labeling for Autonomous Trains | GoogleSheetsTutorial1.ipynb BeautifulSoup.ipynb |
| Kellanova - Point of Sale Analysis | GUI_Tutorial.ipynb MorphologicalOperators_Tutorial |
| QSIDE - SToPA: MultiTown Data Analysis | image_thresholding_tutorial censusdata_package_tutorial |
| Techsmith - Healthy and Engaged User Data Exploration | Create_a_python_package.ipynb DTTD_PowerBI_Tutorial.ipynb |
| Tribal Start Program - Tribal Early Childhood Research Data | PyTorch_tutorial.ipynb DTTD_Python_Package.ipynb |
| TwoSix - LLM to Graphs | DTTD_Tutorial_Widgets-D2LAPITeam.ipynb socail_media_scrapper |
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:
- Clone the DTTD repository.
- Follow the directions and get your tutorial “working”.
- Add “issues” to the git issue list for all things that need to be improved in the tutorial. Make sure the issue is well written and clearly states the file/tutorial that needs fixing. Every student should add an issue.
Notes for making Git Issues
Gitlab and github have a simple mechanism for reporting “issues” inside the repository. Go to the DataTools Tutorial Demo 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:
- Bad issues are things like “this is confusing” or “needs more”.
- Make sure you specify the exact files that need changing.
Getting Credit for this assignment
Each member of the team should author at least one git issue. 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.