Data Analysis Learning Basics

🚀 Data Analysis Basics By Python - Learning Journey

Hi there! 👋
This is a summary of my Data Analysis Basics learning journey.
I’ve completed the foundational theoretical and practical parts, and I’m now applying what I’ve learned through small projects.
Below, I’ll be adding links to the projects I build as I practice and grow.

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✅ Skills I’ve Learned:

• 📊 Understanding the fundamentals of data analysis
• 🧹 Data cleaning techniques
• 📈 Exploratory Data Analysis (EDA)
• 🐼 Working with Pandas and NumPy
• 📉 Data visualization using Matplotlib and Seaborn
• 📦 Handling CSV and Excel files
• ⏱️ Managing missing values and datetime features
• 📌 Basic statistics for data analysis

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🧪 Projects

Here’s a list of basic projects I’ve done to practice these skills.
I’ll be updating this list with links to each project:

1. Counts Of Top Skills in Job Posting.ipynb
2. Job Frecuance and Salary.ipynb
3. Jobs Demand.ipynb
4. Salary and Counts of Job Posting For Top 10- Skills.ipynb
5. Salary destribution For Data Analyst in the US.ipynb
6. Top 5 Data Analysts Per Month.ipynb
7. Top 5 Skills Data Analysts Per Month.ipynb
8. Top 10 Highest Paid and Demand Skills For Data Analysts.ipynb

The final Projects

9. The Final Project_1.ipynb
10. The Final Project_2.ipynb
11. The Final Project_3.ipynb
12. The Final Project_4.ipynb
13. The Final Project_5.ipynb

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📌 Notes

This file will continue to grow as I progress in my data analysis journey.
Stay tuned for more updates! 🚀