https://github.com/kammitama5/data_science_001
A collection of Python Scripts used for DataScience, using Jupyter, etc
https://github.com/kammitama5/data_science_001
data-science jupyter-notebook
Last synced: 8 days ago
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A collection of Python Scripts used for DataScience, using Jupyter, etc
- Host: GitHub
- URL: https://github.com/kammitama5/data_science_001
- Owner: kammitama5
- Created: 2016-11-08T16:54:52.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-12-13T19:18:00.000Z (over 9 years ago)
- Last Synced: 2025-10-20T04:53:16.657Z (9 months ago)
- Topics: data-science, jupyter-notebook
- Language: Jupyter Notebook
- Size: 272 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
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# Data_Science_001
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1. A collection of Python Scripts used for DataScience, using Jupyter, etc
2. I am part of a team working on doing data analysis on SEER Cancer data, learning on a weekly basis.
I may post some notes and files based my learning for this group.
3. Also included is some of the exercises from DataCamp's "Intro to Data Science(using Python)" and "Intro to Data Science using R.
#In Data Science:
1. You usually start with a question to answer -> a problem to solve
2. You wrangle data -> data acquisition/ data cleaning
3. You explore data -> build intuition, find patterns
4. Make conclusions and predicitions (using machine learning, etc)
5. You communicate your findings (whether through a blog or through visualization).
1. Data visualization is useful for communication
You may need to return to your original question and refine it -> scientific process
1. create hypothesis, use empiricism to observe, make a conclusion
2. Sometimes not necessarily in order -> sometimes data acquisition happens before question to ask.
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#Categorical Variables:
There are two types of categorical variables:
1. Nominal:
-> Categorical variable without an implied order (reminds me of unsorted in Clojure)
2. Ordinal:
-> Have a natural ordering (eg. Small, Medium, Large, Extra-Large)