Data Analysis with Python & Panda

This project focused on analyzing a dataset containing information about data scientist roles and salaries. The primary objective was to extract meaningful insights through data cleaning, exploration, and visualization.

📊 Data

The primary dataset used in this application is a cleaned CSV file derived from the Alura Archive.

• File: Data ("https://raw.githubusercontent.com/guilhermeonrails/data-jobs/refs/heads/main/salaries.csv")
• Access Date: 2025

🔬 How It Works

In (Data_Analysis_with_Python_&_Pandas.ipynb) you can find all detailed analysis:

1. Renaming DataFrame Columns
2. Analyzing what the categories of categorical columns are
   • Contract Type & Contract Type Description
   • Work regime
   • Company size
3. Changing category names:
   • 'SE': Senior
   • 'MI': Mid-level
   • 'EN': Entry-level
   • 'EX': Executive

🚀Data preparation and cleaning

The code serves as a step-by-step guide for understanding, cleaning, and preparing the dataset for more in-depth analysis, enabling clearer visualization of patterns and trends within the data science job market.

With this initial analysis, we were able to answer several key questions, such as:

• What is the most common level of experience in the dataset?
• What is the predominant type of contract?
• Which job position appears most frequently in the sample?
• Which country do most professionals in the dataset come from?
• Where are the majority of companies in the sample headquartered?
• What is the most common employment status?
• What is the typical size of companies represented in the sample?

📈 Data Exploration / Visualization Stage

Generated statistical visualizations including:

• Histograms to observe distribution patterns
• Boxplots to detect outliers and understand data spread
• Bar charts to compare categorical variables

🛠️🔧 Tools & Technologies

• Language: Python
• Libraries Used: Pandas for data manipulation, Matplotlib for visualizations
• Development Environment: Visual Studio Code with a virtual environment (venv)
• Dashboard Framework: Streamlit

⚙️ Setup and Installation

  - Create the virtual environment: python3 -m venv .venv
  - Activate the virtual environment on Windows: .venv\Scripts\Activate
  - Activate the virtual environment on MAC/LINUX: source .venv/bin/activate
  - Create a file called requirements.txt and add the necessary packages. pandas==2.2.3 streamlit==1.44.1 plotly==5.24.1
  - Install the necessary libraries. pip install -r requirements.txt
  - Create the Dashboard Interface with Streamlit

🌐 Dashboard Deployment

Built an interactive dashboard using Streamlit to allow users to:

• Filter data dynamically
• Generate custom visualizations

Successfully deployed the dashboard to Streamlit Cloud, making it accessible online: https://aplication-data-science.streamlit.app/