Welcome to another one of my projects. In this repository, I will take a deeper dive into data analysis. I will showcase my work using SQL, Python, MS Excel, and Power BI. Each project will focus on applying different data analysis techniques and tools to solve real-world problems and derive insights from datasets.

This project contains SQL-based data analysis projects, which are part of my learning from the Data Analysis and SQL course by the University of California, Davis, on Coursera. The projects analyze agricultural production data, including commodities like milk, cheese, honey, coffee, yogurt, and eggs.

• create table.sql: Script to create the required tables.
• Task 1.sql: SQL queries for various analysis tasks.
• Task 2.sql: Additional queries for cross-commodity and missing data reports.

For this project, I used Python for data cleaning on the logistics and supply chain dataset (source: Kaggle) and Power BI to create interactive visualizations. The visualizations cover metrics such as fuel consumption, delivery time deviation, traffic congestion levels, and warehouse inventory levels, which help analyze operational efficiency and risk management within the supply chain.

• !Explanation, Insight & Conclusion.md: This markdown file provides a comprehensive overview of the project, including the explanation of the data files, the insights derived from the analysis, and the final conclusions drawn from the logistics data. It summarizes the methodology, highlights key findings, and offers actionable recommendations based on the visualizations and cleaned data.
• Cleaning Data Logistic.ipynb: This Jupyter notebook contains the Python-based data cleaning process, utilizing the pandas library to preprocess and organize the raw data for further analysis.
• dynamic_supply_chain_logistics_dataset.csv: This is the raw dataset, containing unprocessed data that has yet to undergo any cleaning or transformation.
• Logistics Visual.pbix: This is the Power BI file containing the visualizations that provide insights into the supply chain and logistics data, including metrics like fuel consumption, delivery delays, and inventory levels.
• supply_chain_logistics_cleaned.csv: This is the final cleaned dataset after processing, ready for analysis and visualization.

In this project, like before I used Python for data cleaning on the Top Spotify Songs (source: Kaggle) and Power BI to create visualizations dashboard. The visualizations consist of several parts of overview, trends, Demographics, Tracks.

• !Explanation, Insight & Conclusion.md: A markdown file containing an explanation of the project's objectives, insights gained from data analysis, and conclusions from the results found.
• cleaned_top_spotify_song.csv: The cleaned dataset is ready for further analysis. This data includes information such as song name, artist, ranking, popularity, and technical features of the song. download file
• Cleaning_spotify_data.ipynb: Jupyter Notebook containing Python scripts for cleaning Spotify data. This includes de-duplicates, handling missing values, and data transformation.
• universal_top_spotify_songs.csv: A dataset containing a list of the most popular songs on Spotify globally, including data on song name, artist, popularity, and possibly some other metrics. download file
• Visual Spotify Dataset.pbix: A Power BI file containing an interactive visual dashboard that displays data analysis of Spotify songs using various graphs and metrics for better understanding.

In this project, I used Excel for cleaning the dataset from the Top Anime Dataset 2024 (source: Kaggle), especially using Power Query. Then, I used Power BI to visualize the data. The visuals show information about the top-rated anime in 2024, including things like top anime, genre, rating distribution, type, duration, and status, as well as trends based on days and months.

• !Explanation, Insight & Conclusion.md : A markdown file that explains the results of data cleaning, analysis, and visualization.
• Cleaned_Anime_Data.xlsx : This Excel file is the result of cleaning the data from the Top_Anime_data.csv file using Power Query.
• Top_Anime_data.csv : The Top Anime dataset contains information like rank, score, and other details, which will be cleaned and visualized later.
• Visual Top Anime.pbix : A Power BI file containing an interactive visual dashboard that displays data analysis of Top Anime in 2024, using various graphs and metrics for better understanding.