This commit is contained in:
louiscklaw
2025-02-01 02:11:37 +08:00
commit a786e870b8
8 changed files with 117 additions and 0 deletions

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*.mp4 filter=lfs diff=lfs merge=lfs
*.zip filter=lfs diff=lfs merge=lfs
*.7z filter=lfs diff=lfs merge=lfs
*.tar.gz filter=lfs diff=lfs merge=lfs
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# Documents
*.doc diff=astextplain
*.DOC diff=astextplain
*.docx diff=astextplain
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*.dot diff=astextplain
*.DOT diff=astextplain
*.pdf diff=astextplain
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**/~*.*

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git status .
@pause
git add .
git commit -m"update yo006yo,"
start git push

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#!/usr/bin/env bash
set -ex
git config --global http.version HTTP/1.1
git config --global lfs.allowincompletepush true
git config --global lfs.locksverify true
git config --global http.postBuffer 5368709120
git add .
git commit -m 'update,'
git push
echo "done"

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---
tags: [HKDI, pending, kaggle]
---
# yo006yo
### Collect Open / Real-Time Data, Visualization and Analysis

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{
"name": "yo006yo",
"version": "1.0.0",
"description": "",
"main": "index.js",
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1",
"gitUpdate": "git add . && git commit -m'update,'&& git push"
},
"keywords": [],
"author": "",
"license": "ISC"
}

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---
tags: [pending, kaggle]
---
# task1
## Brief
### You are required to collect open data and real-time data.
### Part 1: Jupyter notebook Data Analysis and suggestion of actionable items
- Download the Top 200 common passwords by country 2021 database from www.kaggle.com
- Manipulate and rearrange the data if necessary
- Visualize the data using 8 or more charts using Python programming in Jupyter notebook.
- The sunburst chart, heat map, and pair-plot must be used.
- 1 or more 3D chart is essential.
- 1 or more map, such as choropleth map in plotly should be displayed.
- Analyze the charts (and data) which may reveal some facts to us.
- Provide insights and suggest actionable items.
- (You may add other related data set(s) to enrich your insights and suggestions.)
### Part 2: Real-time data processing and visualization, in Jupyter notebook.
- Collect and store real-time data using the API of HK Accident and Emergency waiting time (of Hospitals) in NoSQL database (e.g., MongoDB).
- remarks : https://data.gov.hk/en-data/dataset/hospital-hadata-ae-waiting-time/resource/164c3478-1791-4f9e-94d9-70d2374a48e2
- The data collection duration should be 3 or more days, within November and/or December.
- The collection frequency should be every 15 minutes or less.
- Create Jupyter Notebook to read data into a Pandas dataframe.
- (You may export the data, using Mongo Compass, to a json file first.)
- Process and visualize the data.
- Produce 3 or more charts.
- You are encouraged to use python 3D visualization techniques too.
- Analyze the charts (and data) to reveal some facts.
- Provide insights / comments / suggestions.
### Items should include:
- Exported collection(s) of the open data / samples of real-time data, from MongoDB
- Jupyter Notebooks that visualize and analyze the data sets, with summary, conclusions and so on in Markdown.
- Demonstrate data collection process and present all results / insights, in a video.
- Upload everything to Moodle 1 week after the last lesson.