Hussein
The Data Engineer

Superstore Project

PowerBI, DAX & Data model

This project involves advanced data analysis using Power BI to gain insights from a Superstore dataset. 🛒 After thorough data cleaning and processing, a dashboard was created using DAX and various visualizations. 📊 Key insights include: 1️⃣ Total Customers 👥 2️⃣ Total Orders 📦 3️⃣ Products Sold 🛍️ 4️⃣ Total Returns ↩️ 5️⃣ Top-Performing Products and Regions 🌟

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This project focuses on advanced data analysis of an E-Commerce dataset using Python. 🐍 The data was cleaned by handling null values, correcting datatypes, and removing outliers. 🧹 Visualization charts were created to reveal key insights, including: 1️⃣ Top 5 Highest-Priced Products 💰 2️⃣ Best Month for Sales 📅 3️⃣ Top 5 Countries by Revenue 🌍

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This project focuses on analyzing an Apple App Store dataset using SQLite. Despite initial challenges with data import and SQLite's handling of large datasets, I successfully split the data for analysis. 🚀 The project involved performing Exploratory Data Analysis (EDA) 🧐, including checking for unique apps, missing values, app distribution by genre, and analyzing user ratings. Key Insights 📊: 1️⃣ App Type and Ratings: Examined whether paid apps have higher ratings than free apps. 2️⃣ Apps with Multilingual Support: Explored if apps supporting more languages tend to have higher user ratings. 3️⃣ Highest-Priced Categories: Identified which app categories have the highest prices. 4️⃣ Low-Rated Genres: Checked for genres with low user ratings. 5️⃣ Description Length and User Ratings: Investigated if there's a correlation between app description length and user ratings. 📉📈

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This project involves web scraping the Horn Africa Jobs website to gather information on job postings. 🌐 The goal is to extract essential job details, such as: Job Name 💼 Salary 💰 Location 📍 The extracted data is then saved into a CSV file for further analysis and accessibility. 🗂️ This process helps in tracking job opportunities and analyzing trends in the job market, making it easier for job seekers to find relevant positions.

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This project involved three key stages: data collection, transformation, and visualization. 1️⃣ Data Collection: Using web scraping, I gathered valuable table data from a website, akin to a pig foraging for truffles. 🐽 2️⃣ Data Transformation: I refined the dataset by removing null values and changing column data types. 3️⃣ Data Visualization: I created three clear visuals showing the top 10 countries by population, countries with the highest median age, and the top 5 countries by land area. 🌍📊 Additionally, I automated the process using Python to send the results via email. This project demonstrates how data analysis can uncover valuable insights effectively." 📈

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📊 Problem Statement: The goal is to analyze the dataset to extract meaningful insights regarding employee retention. 🔍 Data Analysis We meticulously cleaned the data by addressing duplicates, null values, and errors. Additionally, we categorized working years into four groups: (1-10 years), (11-20 years), (21-30 years), and (31-40 years). Using pivot tables and pivot charts, we created an interactive dashboard. 💡 Insights Employee Count: The current number of employees exceeds those who left. 👥 Education: Departing employees were more likely to hold bachelor's degrees compared to master's degrees. 🎓 Departments: The Research and Development department had the highest turnover, yet it also housed the most employees. 🏢 Job Roles: The Sales Executive role saw the highest number of departures. 👔 These insights highlight key factors influencing employee retention and turnover. 📈📋🔍

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