Discover the fun facts of PCC course enrollment data and give it a analysis.
flowchart LR
subgraph pcc-course-enrollment-data-explorer
DATA(((Features)))
DATA --> Data-Sourcing
DATA --> Data-Analysis
subgraph Data-Sourcing
S(fa:fa-camera-retro)
style S fill:#98B476
direction TB
PCC[(PCC-COURSE-WEB)]
A[requester]
B[cleaner]
C[storer]
L[[log]]
S --every 30 mins--> A
A -.request data.-> PCC -.return html data.-> A
A --pass html data--> B
B --clean info--> C
C .-> L
C -.store cleaned data to local.-> S
end
subgraph Data-Analysis
ANA(fa:fa-spinner)
style ANA fill:#4E95B4
direction TB
ANA --> Info-Extraction
ANA --> E["Visualization of data trend"]
Info-Extraction --> Intepretation
subgraph Info-Extraction
CO[Popular courses]
CO --> IRA[Growth rate of enrollment]
IRA --> FC[Fastestclosed class]
NL[Courses that students do not like] --> DR[Course drop info]
UP[Unpopular courses] --> EL[Enrollment is low and fluctuates very little]
MO[More]
end
subgraph Intepretation
Q1["Why it happened?"]
DM[What these data mean] --> Student-Point-of-View
DM[What these data mean] --> School-Point-of-View
subgraph Student-Point-of-View
CH[Courses helpful to students]
Q21["chance to enroll a closed class"]
M2[More]
end
subgraph School-Point-of-View
BA[Optimize the budget of the course plan] --> CC[Appropriate course capacity]
CC --> HD[Add courses that are in high demand]
CC --> LD[Courses that may require reduced capacity]
end
end
end
end
flowchart LR
subgraph GPA Analysis
HELP(((How to help GPA?)))
HELP --> A[Find Popular Courses]
HELP --> ENROLL[How to enroll a closed course?]
HELP --> F[Disliked Courses]
subgraph Popular Courses
A --> B[Courses Close Fast]
A --> C[Enrollment Increment Rate]
A --> D[Visualization of Data Trend]
A --> GA[Which professor usually give add code]
B --> E[Ratemyprofessor.com Truthfulness]
C --> E
D --> E
GA --> E
end
subgraph Disliked Courses
F --> G[Course Drop Rate]
end
subgraph Enroll Closed Course
ENROLL --> H[Traditional Way]
ENROLL --> LATER["New Way: Fact behind Data Science"]
subgraph Traditional Way
H --> I[Registration Priority]
H --> J[Waitlist]
H --> K[Add Code]
end
subgraph New Way
LATER --> L[Student can be Dropped for many Reasons]
LATER --> P[New Courses added after Semester Start]
LATER --> O[Course Seat Monitor]
subgraph Course Seat Monitor
O --> Q[Sending Email Alerts]
end
L --> M[International Student Tuition not paying in time]
L --> N[Miss course Check-in Time]
end
end
end
This project wanted to explore PCC's (Pasadena City College) course enrollment data, to learn interesting facts about it and try to give it an analysis and interpretation. For example, a course is likely to have a large number of students drop at a certain time due to students not paying tuition. Students can use this opportunity to enroll in this closed course. This project hopes to support this conjecture with a large data set and discover more data phenomena.
PCC Data Science Website PCC Course Schedule Website
- Data Sourcing: Automatically captures and stores up-to-date information from the PCC Course Web every 30 minutes. This data is then get cleaned and extracted for further analysis.
- Obtained course data is uploaded to the output folder by the server owned by @perryzjc
- Data Analysis: Provide a range of capabilities for analyzing the data, including:
- Info Extraction: Extract and organize information such as the number of course statuses and course drop rates.
- Interpretation: Provide context for the data by answering questions such as "Why did this happen?" and "How can this data be used to benefit the user?" Examples include identifying potential opportunities for enrolling in a closed class.
- Visualization of Data Trends: Plan to provide clear visualization of data trend (TODO).
This guide provides a quick way to get started with this project.
- conda to be installed on the machine where the project will be run. Please make sure to have conda installed before running the project.
-
Clone this project repository via
git@github.com:perryzjc/pcc-course-enrollment-data-explorer.git -
Navigate to the directory where the repository is installed, i.e. pcc-course-enrollment-data-explorer
cd pcc-course-enrollment-data-explorer -
To create a suitable environment for running this project, I recommend using the
environment.ymlfile with conda. This file contains all the necessary dependencies and can be easily created using the following command:conda env create -f environment.yml
-
Once the environment is created, you can activate it by running the command
conda activate <environment_name>.Replace
<environment_name>with the actual name of the environment, which can be found in theenvironment.ymlfile, the prompt given by your shell (after running the commandconda env create -f environment.yml), or by running the commandconda info --envs. -
Run the code that you are interested in. For example,
python3 main.py
(pcc-course-enrollment-data-explorer) perryzjc@MBP pcc-course-enrollment-data-explorer % python3 frontend.py1.Course data are obtained every 30 minutes and are store to the location based on current time
(assume current time 2023-01-09-23-03-08)
pcc-course-enrollment-data-explorer
- data_analysis
- data_sourcing
- output
- data_analysis
- data_source
- 2023
- 01
- 09
- 2023-01-09-23-03-08.csv
- log
- log.txt
- README.md
- tests
- environment.yml
- main.py
- ...
log.txtgot updated. For example, a new line got added to the filelog.txt:
successfully store all course data as a csv file at time: 2023-01-09 23:03:08
See our CHANGELOG.md for a history of our changes.
- How to install conda and use it?
- Please check the following resources which might are helpful
Interested in contributing to this project? Please see our: CONTRIBUTING.md
- Create an GitHub issue ticket describing what changes you need (e.g. issue-1)
- Fork this repo
- Make your modifications in your own fork
- Make a pull-request in this repo with the code in your fork and tag the repo owner / largest contributor as a reviewer
Working on your first pull request? See guide: How to Contribute to an Open Source Project on GitHub
For guidance on how to interact with our team, please see our code of conduct located at: CODE_OF_CONDUCT.md
See our: LICENSE