Building-a-Beginner-Analytics-Dashboard-Python-SQL-CSV

This project is a beginner-friendly end-to-end data pipeline that demonstrates how to load data from a CSV file, clean it, store it in a SQL database, and build a simple analytics dashboard in streamlit using Python .

It is designed as an introductory project for anyone learning data engineering, data analytics, or Python-based ETL pipelines.

📁 Project Structure

📦 Beginner_Data_Pipeline_Python_SQL_CSV_file

┣ 📄 README.md

┣ 📄 pipeline.py

┣ 📄 dashboard.py

┣ 📄 sales_data.xlsx

Data Architechture Diagram

🚀 Features

• Import data from a CSV file

• Clean and preprocess the dataset using Pandas

• Store cleaned data into a SQLite database

• Use SQL queries to aggregate and analyze the data

• Create a simple analytics dashboard (tables + charts) in Streamlit (Web based visualization platform)

• Beginner-friendly, fully documented Python code

🛠 Technologies Used

• Python 3.x

• Pandas

• SQLite3

• SQLAlchemy

• Altair (statistical visualization library)

📊 Example Insights Generated

• The first five products

• Total Revenue

• Revenue by Region

• Top-performing products

• Daily Sales Trend by products

▶️ How to Run the Project

1. Clone the Repository git clone https://github.com/Kindoli/Building-a-Beginner-Analytics-Dashboard-Python-SQL-CSV-.git cd Building-a-Beginner-Analytics-Dashboard-Python-SQL-CSV-

2. Run the Pipeline python pipeline.py

This will:

• Load the CSV

• Clean the data

• Insert it into the SQL database (sales.db)

4. Run the Dashboard Script python dashboard.py

This will generate charts and summary tables.

Streamlit Dashboard snapshot

📂 Sample Code Snippet

df = pd.read_excel("sales_data.xlsx")

df_clean = df.dropna()

df_clean.to_sql("Sales_data", conn, if_exists="replace", index=False)

📘 Learning Objectives

This project helps beginners learn:

• Reading CSV files in Python

• Basic data cleaning

• SQL operations using Python

• Building a simple analytics dashboard using Streamlit

• Structuring a real-world mini data pipeline

🤝 Contributing

Pull requests are welcome! Feel free to submit improvements or additional analytics steps.