In-Class Assignment: SCHOLAR Tutorial Review
Agenda (110 Minutes)
- (20 minutes) Tech-Talk
- (40 minutes) Group Review of the SCHOLAR git repository
- (20 minutes) Report out to class
- (30 minutes) Group co-work time
TechTalk
Why Email Still matters
Today’s Tech Talk explores the idea that data is a map, not the destination. While understanding your data is an important part of any project, successful teams stay focused on the problem they are trying to solve rather than letting the dataset become the project. We will discuss data discovery, exploratory data analysis (EDA), data schemas, data dictionaries, and data bibliographies as tools for understanding and documenting datasets.
We will also explore how projects can make meaningful progress even when data is not yet available. By defining project goals, identifying information needs, creating schemas, and using synthetic or pilot datasets, teams can begin planning solutions long before the final data arrives. Just as you can plan a trip before you have a complete map, you can often design and prototype a project before obtaining all of the data needed to complete it.
Group SCHOLAR Review
The following is a list of teams and the randomly assigned tutorials.
| Team | Tutorial |
|---|---|
| Bradford White - Data-Driven Design | Visualizations-BERT-Topics.ipynb React Tutorial (HFH_MT).ipynb DTTD Tableau Instructions.ipynb SCHOLAR Classification.ipynb |
| Bradford White - Water Leak Detection | Zotero_Instructions.ipynb Uniscraper_Tutorial Mimesis.ipynb Pointers.ipynb GoogleSheetsTutorial |
| CEPI - Effective Communications | DataSynthesizer_Tutorial.ipynb Tidyverse_Tutorial_.ipynb environment.yml TensorFlow_Tutorial.ipynb DTTD_Tutorial_Widgets-D2LAPITeam.ipynb |
| Customers Lens - Entrepreneurial Challenges | PowerBI_Scholar_Workshop.md References.ipynb PTest.ipynb PySpark_Tutorial.ipynb image_thresholding_tutorial |
| Delta Dental - Canonical Mouth Dataset Development | Pandas.ipynb RREF.ipynb BeautifulSoup.ipynb social_media_scrapper imageassets |
| Henry Ford Health - Video Segmentation | Basic_Containers.ipynb polars HPCC_Initial_Tutorial.ipynb SAHI_Tutorial_COCO_Demo.ipynb lib |
| Joyntly - User Engagement | GAMA_AutoML_Tutorial.ipynb Matplotlib_tutorial.ipynb ssh_key_gen makefile _Template.ipynb |
| Kellanova - Demand Forecasting | pcatutorial.ipynb MorphologicalOperators_Tutorial faker.ipynb FineTune-Mistral-LLM-OwnData AudioDataTutorial.ipynb |
| Luce - Lumber | tpot_tutorial.ipynb FuzzyWuzzy.ipynb Whitespace_Indentation.ipynb Gradients.ipynb BigO_C++.ipynb |
| MSU - Curriculum Analytics | Selenium_tutorial.ipynb datasets gis topic-modelling tpot_environment.yml |
| MSU - Southwest Lansing Project | Video-Image-Data-Tutorial Orange_tutorial.ipynb BERT_VectorSimilarity_Python Loops.ipynb SCHOLAR_Google_Sheets_API.ipynb |
| NCEAS - Unsupervised NLP | censusdata_package_tutorial DAX_Tutorial Camtasia Seaborn_Tutorial_DTTD.ipynb SAHI_tutorial1_1.ipynb |
| TeliAI - Agentic Campaign Insight Analyzer | Auto_Cropping_Image_Tutorial Tidyverse_Tutorial.ipynb Auto-SKLearn_AutoML Central_Limit_Theorem.ipynb Dask_Tutorial.ipynb |
| ToolsForHumanity - IRIS Recognition | FFmpegDemo.ipynb Numpy_Sympy.ipynb YOLO_Tutorial GridSearchCV_Tutorial.ipynb BFG_Tutorial_DTTD.ipynb |
| UofM - Civil Rights Litigation Website | Eigenvalues.ipynb OpenCV_tutorial_SCHOLAR.ipynb DTTD_PowerBI_Tutorial.ipynb Streamlit Create_a_python_package.ipynb |
| WBPD - Crash Safety | answercheck.py GUI_Tutorial.ipynb AnomalyAndOutlierDetection.ipynb R_Shiny_App_Tutorial Networkx and Pyvis.ipynb |
Your group is expected to review all of the tutorials. However, today we will start with just this small set (I recommend one tutorial per person). As a group do the following:
- Clone the SCHOLAR repository.
- Follow the directions and get your tutorials “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 at least one issue for that person to get credit.
Getting Credit for this assignment
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.