Скачать или смотреть Exploratory Data Analysis Project in Python | Jupyter Notebook Walkthrough (Cyclistic Bike Share)

#LearnPython #PythonForDataAnalysis #PythonForDataScience #EDA

This video will:
1. Help you learn how to work with categorical data in Python
2. Help you create more customized and advanced visualizations in Python using Matplotlib, Plotly, and Seaborn
3. Give you an extensive Exploratory Data Analysis framework and checklist to put you ahead of the average analyst
4. Help anyone who did the Cyclistic Google Data Analytics Project in R Programming or SQL, and may want to learn how to do it using Python.

📖 Jupyter Notebooks on Kaggle:
▶️Part 2(Analysis) : https://www.kaggle.com/code/terrenceindata...

▶️Part 1(Data Cleaning) : https://www.kaggle.com/code/terrenceindata...

⏰ time stamps ⏰
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00:00 Intro
00:58 Import Libraries
02:00 Introduction
03:49 Read-In Data
04:06 Update Data Types
06:09 Feature Understanding
07:04 'biker_status' - Univariate Analysis
14:30 'bike_type' - Univariate Analysis
16:40 'hour_of_day' - Univariate Analysis
24:05 'day_of_week' - Univariate Analysis
27:50 'month' - Univariate Analysis
30:40 Geospatial Summarization
30:50 Latitudinal and Longitudinal Data
51:51 Time Series Summarization
01:21:30 Bivariate Analysis
01:22:00 'biker_status' vs 'bike_type'
01:25:17 'biker_status' vs 'miles_traveled'
01:26:02 'biker_status' vs 'hour_of_day'
01:30:24 Outro

Balancing all the thoughts that go into an extensive EDA process can be really overwhelming!

🤖Having a repeatable or reproducible system in place can make this process come routine and natural to you if you take the time to write it out well and have the discipline to stick to it. Not to mention, a good checklist can lead you to some really granular, useful, insights that the average analyst may overlook. This helps you to become a stronger analyst.

⚙️When learning data analytics and completing your first project, you want to find a way to 'error proof' the processes as much as possible, within both Visualization and EDA in general. My approach to achieving this was to create a step by step checklist or 'cheat sheet' to streamline the analysis process for every project.

Interestingly enough, it's not easy to find really good, detailed checklists... and so I wrote out my own system from scratch!

I'm familiar with some basic Data Analysis Process frameworks that to me aren't very specific in terms of wrangling data in Python. Here's a free resource that may actually get your mind thinking like an analyst while working in 🐍 Python Pandas 🐼!

💡You can use this approach as a base for your notebook and then do further research on data cleaning, transforming, univariate and bivariate analysis, etc...

📃Start a checklist! You can use my bullet points(Table of Contents) from my Kaggle Notebook:

1. Preparing & Understanding Data
2. Transform and Clean Data
3. Feature Understanding
4. Insight Analysis
5. Summary
6. Recommendations
7. Conclusion

💭This is a simple base template for your checklist. You can use my notebooks to fill in the details as well, and include anything from it that you'd like to implement into your future projects or analysis processes.

I use this process for all applicable projects.

✅Your feedback is appreciated!
✅Feel free to ask any questions you have!
✅I'd love for you to mention any suggestions on project improvisions, or video ideas you'd like to see next!
✅Also, if you learned something new, comment below and let us know what you learned from the video!

📝SUBSCRIBE!📝
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👋😊 Hi, I'm Terrence! I'm sharing my portfolio projects on this channel! Be sure to subscribe and join me on your self-education journey!

📥Contact Me📥
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Email: [email protected]

My Portfolio Website: https://terrenceindata.wixsite.com/portfolio

🤝 connect with me 🤝
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LinkedIn - https://www.linkedin.com/in/terrenceindata
Kaggle - https://www.kaggle.com/terrenceindata
GitHub - https://github.com/terrenceInData
Instagram - https://www.instagram.com/terrenceindata
Twitter(X) - https://twitter.com/terrenceindata

😃Thanks for watching everyone!

Please consider subscribing if you liked the video: https://www.youtube.com/@terrenceinda...