Data Analyst skilled in SQL, Tableau, Power BI and Python @BadrAnalyst
This project features an interactive Streamlit dashboard analyzing the Seaborn 'tips' dataset, which records restaurant bill details such as total amounts, tips, customer demographics (e.g., gender, smoking status), and dining details (e.g., day, time). Visualized using Plotly, it provides insights into tipping patterns and behaviors.
This project features a dynamic Tableau dashboard designed to visualize COVID-19 deaths data, offering insights into trends, geographical distributions, and critical mortality metrics during the pandemic, ultimately aiming to facilitate a deeper understanding of the data to support informed public health analysis and decision-making.
This project provides a Python-based analysis of residential unit prices, with a focus on data cleaning, visualization, and exploratory data analysis (EDA). Key insights include price distribution and correlations between factors such as unit size, location, and pricing.
This project conducts an extensive SQL exploratory data analysis on COVID-19 deaths and vaccinations, aiming to reveal trends and insights about vaccination rates and their effects on mortality, ultimately enhancing the understanding of the pandemic's dynamics to support data-driven public health decisions.
An interactive Power BI dashboard that explores survey insights from data professionals, focusing on job satisfaction, field entry challenges, career priorities, and demographics, providing a comprehensive view of the factors shaping the global data profession.
This project analyzes startup growth and expansion factors using Python for data analysis and Matplotlib for visualizations. It complements findings with data visualizations in Power BI, providing actionable insights into funding and market trends to support strategic decision-making and business growth.
This project explores Udemy course data using NumPy and Pandas for data manipulation. It provides insights into course topics, ratings, and pricing, with visualizations created using Matplotlib, Seaborn, and Plotly to present key trends and patterns in the dataset for better understanding and decision-making.
This project entails data cleaning, analysis, and visualization of bike sales data in Excel, examining factors such as income, gender, age, commute distance, region, marital status, and education to reveal customer demographics and purchasing behavior through an interactive dashboard.
This Python project analyzes student test scores in math, reading, and writing, exploring correlations with parental education, lunch type, and test preparation. The analysis includes data cleaning, visualization, and statistical insights into factors impacting academic performance.
An interactive Power BI dashboard that explores survey insights from data professionals, focusing on job satisfaction, field entry challenges, career priorities, and demographics, providing a comprehensive view of the factors shaping the global data profession.
This project entails a thorough SQL data cleaning process of the Nashville Housing dataset, addressing inconsistencies, managing missing values, and optimizing the data to facilitate accurate analysis and insightful exploration of housing trends and patterns in Nashville.
This project uses BeautifulSoup4 to scrape job listing data, including titles, companies, and job types, from a specified web source. The collected data is cleaned and processed with Pandas, providing a structured dataset for further analysis. The goal is to facilitate job market insights through efficient data collection and preparation.
This project involves web scraping data on the largest U.S. companies by revenue, gathering key information like rank, company name, industry, revenue, number of employees, and headquarters location. The data is organized into a CSV file, making it ready for analysis and insights into the country's leading corporate players.
Morocco, Casablanca