Personal projects using SQL and Power BI
Hello, and thanks for stopping by! This repository contains examples of SQL I've written for solo projects. You'll also get a chance to explore a Power BI dashboard which showcases my data visualization skills. Feel free to take a look around and reach out if you have any feedback or questions.
This SQL project showcases my proficiency in working with a dataset related to a cycling club. The dataset includes information about club members, their bikes, track times, track locations, and more. Each table represents a specific aspect of the cycling club's operations. I've created and manipulated tables, performed data insertion and updates, and executed various SQL queries. These queries help extract meaningful insights, such as tracking member activities, analyzing track data, and identifying member characteristics. Overall, this portfolio demonstrates my data analysis skills using SQL for a cycling club dataset.
In this SQL project, I performed an analysis using the Kaggle dataset "Top 50 Spotify Tracks 2020" by Atilla Colak. I utilized SQL queries to gain insights into the characteristics of these top songs, including energy, danceability, key, and more. The analysis aimed to uncover trends and patterns in the most popular songs of 2020, providing valuable information for music enthusiasts and data analysts.
Download my AdventureWorks Dashboard
I have successfully created a comprehensive Power BI dashboard for AdventureWorks Cycles, a fictional manufacturing company specializing in bicycles and accessories. This dashboard is designed to provide a detailed view of the company's operations and help in tracking key performance indicators (KPIs), analyzing regional performance, product-level trends, and identifying high-value customers.
Comprising four distinct pages — Executive dash, Map, Product detail and Customer detail Here's a breakdown of what I have accomplished in each stage.
Stage 1: Data Transformation I've automated the extraction, transformation, and loading of project data using Power Query. This involved connecting to data sources, shaping data with various tools, and applying transformations, including rolling calendars, conditional columns, and aggregations. I also established data source parameters and integrated Excel models for streamlined data preparation.
Stage 2: Data Modelling I've adhered to data modelling best practices by creating an efficient AdventureWorks data model. This model emphasizes concepts like database normalisation, fact-dimension tables, primary-foreign keys, and relationship cardinality, ensuring an optimal foundation for analysis.
Stage 3: DAX Analysis I've enhanced data analysis capabilities with Data Analysis Expressions (DAX). This involved crafting calculated columns and measures, utilizing DAX functions for mathematical, statistical, and time intelligence tasks, all while considering row and filter context.
Stage 4: Dynamic Reporting I've brought data to life with interactive reports and dashboards that adhere to data visualization best practices. These include cards, KPIs, line charts, tables, matrices, and map visuals. Interactivity features like drill-through, slicers, bookmarks, and formatting enhancements have been added for a user-friendly, informative output.
Overall, this Power BI dashboard is now a powerful tool for AdventureWorks Cycles, enabling data-driven decisions and offering valuable insights into their business operations. The attached pdf will provide a visual representation of the Dashboard.
Download my Maven Markets Dashboard
Im pleased to announce the successful creation of a comprehensive Power BI dashboard for Maven Markets, a fictional supermarket franchise. The dashboard is structured across two main pages, each focusing on crucial aspects of the business.
Page 1: Topline Performance This page is designed to provide an overview of the company's overall performance, featuring four key performance indicators (KPIs) The Product Matrix includes vital information such as product-wise transactions, profit margins, and return rates. Additionally, a dynamic map visualizes regional and city-wise sales, allowing for a quick assessment of geographic performance.
Page 2: Customer Detail The second page delves into customer-centric insights, offering a detailed view of the top customer's orders and revenue. Key elements of this page include: The Top Customer Overview provides detailed information about the highest-value customer, including their orders and contribution to overall revenue. The Top 10 Customers Matrix offers a snapshot of the most valuable clients, and the Bar Chart provides an insightful comparison of different stores' performance and profits.
Development Stages:
Stage 1: Data Transformation I've streamlined the data preparation process by automating data extraction, transformation, and loading using Power Query. This involves connecting to relevant data sources, applying necessary transformations, and ensuring data integrity. I've also implemented rolling calendars, conditional columns, and aggregations, optimizing the data for further analysis.
Stage 2: Data Modeling I've adhered to best practices in data modelling, emphasizing database normalization, fact-dimension tables, primary-foreign keys, and relationship cardinality. This ensures a robust foundation for analysis, enabling efficient queries and reporting.
Stage 3: DAX Analysis The data analysis capabilities have been enhanced using Data Analysis Expressions (DAX). I've crafted calculated columns and measures, employing DAX functions for mathematical, statistical, and time intelligence tasks. This ensures accurate and meaningful insights, considering row and filter context.
Stage 4: Dynamic Reporting I've created visually compelling and interactive reports adhering to the best data visualization practices. The dashboard includes cards, KPIs, line charts, tables, matrices, and map visuals. Interactivity features such as drill-through, slicers, and formatting enhancements have been incorporated for a user-friendly and informative output.
The attached PDF provides a visual representation of the Power BI dashboard, showcasing its functionality and the valuable insights it offers to Maven Markets for data-driven decision-making.
Download my Harmony Real Estate Dashboard
A comprehensive Power BI dashboard designed for Harmony Real Estate, facilitating property analysis, KPI tracking, and operational insights.
Page 1: Property Overview:
- Map Visualization: Highlighting property locations with bubbles.
- Tree Map: Enabling easy property filtering based on criteria.
- Line Chart: Displaying average property price trends by sub-localities.
- Matrix: Providing detailed property information at a glance including property type, zip code, bedrooms, baths, and price.
Page 2: Property Detail:
- KPI Cards: Featuring essential metrics like average property size and price per square foot.
- Brokerage Information Card: Showing associated brokerage for each property.
- Matrix: Offering detailed property specifics.
- Key Influencers Diagram: Identifying property type trends.
Development Stages:
Stage 1: Data Transformation
- Automated ETL Process: Utilized Power Query for data extraction, transformation, and loading.
- Data Shaping: Applied various tools for shaping data according to requirements.
- Parameterization: Established data source parameters for flexibility.
Stage 2: Data Modeling
- Efficient Data Model: Followed best practices for database normalization and relationships.
- Optimization: Optimized data model for efficiency and scalability.
Stage 3: DAX Analysis
- Advanced Analysis: Leveraged DAX for meaningful insights.
- Function Utilization: Employed a variety of DAX functions for diverse analytical requirements.
Stage 4: Dynamic Reporting
- Interactive Dashboards: Developed interactive reports with user-friendly features.
- User Experience Enhancement: Incorporated interactivity buttons for seamless navigation.
- Insightful Visualization: Designed visuals to convey actionable insights effectively.
Conclusion: This Power BI dashboard empowers Harmony Real Estate stakeholders with actionable insights, facilitating data-driven decision-making and operational excellence.
Run Demographic Data Analysis with Jupyter Notebook and Google Colab
Description
This project is a demographic data analysis challenge that utilizes the Pandas library in Python to analyze a dataset extracted from the 1994 Census database. The dataset contains various demographic attributes such as age, workclass, education, occupation, race, and salary, among others.
The objective of this analysis is to answer specific questions related to the demographics and their correlation with certain factors like income levels, education, and occupation. By leveraging the powerful data manipulation capabilities of Pandas, we can extract meaningful insights from the data.
Questions
- Count of individuals by race.
- Work out the mean age of male participants
- Return the percentage of individuals with a Bachelor's degree
- Calculate the percentage of those with advanced degrees earning >50K
- Calculate the percentage of non-advanced degree holders earning >50K
- Determine the least amount of hours worked per week
- Calculate the percentage of >50K earners working minimum work hours
- Show the country with the highest percentage of >50K earners
- Display is the most common job among >50K earners in India
Execution Below are the steps to execute the analysis:
Load the Data: Open the Google Colab link and sign into your Google account.
Output: After running the initial command to load the library and file, you can execute the following commands in order. The questions are indicated in the comments. The final answers will then be displayed,